diff --git a/man/details_C5_rules_C5.0.Rd b/man/details_C5_rules_C5.0.Rd
index 134cae562..f9039d589 100644
--- a/man/details_C5_rules_C5.0.Rd
+++ b/man/details_C5_rules_C5.0.Rd
@@ -25,8 +25,7 @@ less iterations of boosting are performed than the number requested.
 
 \subsection{Translation from parsnip to the underlying model call (classification)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{rules}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(rules)
+The \strong{rules} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(rules)
 
 C5_rules(
   trees = integer(1),
@@ -62,7 +61,7 @@ are not required for this model.
 \item Quinlan R (1992). “Learning with Continuous Classes.” Proceedings of
 the 5th Australian Joint Conference On Artificial Intelligence,
 pp. 343-348.
-\item Quinlan R (1993).”Combining Instance-Based and Model-Based
+\item Quinlan R (1993).“Combining Instance-Based and Model-Based
 Learning.” Proceedings of the Tenth International Conference on
 Machine Learning, pp. 236-243.
 \item Kuhn M and Johnson K (2013). \emph{Applied Predictive Modeling}.
diff --git a/man/details_bag_mars_earth.Rd b/man/details_bag_mars_earth.Rd
index 708512286..3e3312bed 100644
--- a/man/details_bag_mars_earth.Rd
+++ b/man/details_bag_mars_earth.Rd
@@ -27,8 +27,7 @@ columns. For a data frame \code{x}, the default is
 
 \subsection{Translation from parsnip to the original package (regression)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{baguette}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{bag_mars(num_terms = integer(1), prod_degree = integer(1), prune_method = character(1)) \%>\% 
+The \strong{baguette} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{bag_mars(num_terms = integer(1), prod_degree = integer(1), prune_method = character(1)) \%>\% 
   set_engine("earth") \%>\% 
   set_mode("regression") \%>\% 
   translate()
@@ -50,8 +49,7 @@ mode: \strong{baguette}.\if{html}{\out{<div class="sourceCode r">}}\preformatted
 
 \subsection{Translation from parsnip to the original package (classification)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{baguette}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(baguette)
+The \strong{baguette} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(baguette)
 
 bag_mars(
   num_terms = integer(1),
@@ -81,8 +79,8 @@ bag_mars(
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 }
 
 \subsection{References}{
diff --git a/man/details_bag_tree_C5.0.Rd b/man/details_bag_tree_C5.0.Rd
index 8c00081f1..979567399 100644
--- a/man/details_bag_tree_C5.0.Rd
+++ b/man/details_bag_tree_C5.0.Rd
@@ -19,8 +19,7 @@ This model has 1 tuning parameters:
 
 \subsection{Translation from parsnip to the original package (classification)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{baguette}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(baguette)
+The \strong{baguette} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(baguette)
 
 bag_tree(min_n = integer()) \%>\% 
   set_engine("C5.0") \%>\% 
diff --git a/man/details_bag_tree_rpart.Rd b/man/details_bag_tree_rpart.Rd
index efb873fbd..2503f64cc 100644
--- a/man/details_bag_tree_rpart.Rd
+++ b/man/details_bag_tree_rpart.Rd
@@ -31,8 +31,7 @@ the second level of the factor.
 
 \subsection{Translation from parsnip to the original package (classification)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{baguette}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(baguette)
+The \strong{baguette} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(baguette)
 
 bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) \%>\% 
   set_engine("rpart") \%>\% 
@@ -56,8 +55,7 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1
 
 \subsection{Translation from parsnip to the original package (regression)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{baguette}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(baguette)
+The \strong{baguette} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(baguette)
 
 bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) \%>\% 
   set_engine("rpart") \%>\% 
@@ -81,8 +79,7 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1
 
 \subsection{Translation from parsnip to the original package (censored regression)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{censored}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
+The \strong{censored} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
 
 bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) \%>\% 
   set_engine("rpart") \%>\% 
diff --git a/man/details_bart_dbarts.Rd b/man/details_bart_dbarts.Rd
index 5fdcccc62..68f7bf9ff 100644
--- a/man/details_bart_dbarts.Rd
+++ b/man/details_bart_dbarts.Rd
@@ -103,8 +103,8 @@ times number of observations.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 \code{\link[dbarts:bart]{dbarts::bart()}} will also convert the factors to
 indicators if the user does not create them first.
diff --git a/man/details_boost_tree_C5.0.Rd b/man/details_boost_tree_C5.0.Rd
index 1acce68f4..086c7a91c 100644
--- a/man/details_boost_tree_C5.0.Rd
+++ b/man/details_boost_tree_C5.0.Rd
@@ -59,8 +59,8 @@ are not required for this model.
 
 By default, early stopping is used. To use the complete set of boosting
 iterations, pass \code{earlyStopping = FALSE} to
-\code{\link[=set_engine]{set_engine()}}. Also, it is unlikely that early stopping
-will occur if \code{sample_size = 1}.
+\code{\link[=set_engine]{set_engine()}}. Also, it is unlikely that early
+stopping will occur if \code{sample_size = 1}.
 }
 
 }
diff --git a/man/details_boost_tree_mboost.Rd b/man/details_boost_tree_mboost.Rd
index 84c568cb3..df9dfa215 100644
--- a/man/details_boost_tree_mboost.Rd
+++ b/man/details_boost_tree_mboost.Rd
@@ -28,8 +28,7 @@ is to use all predictors.
 
 \subsection{Translation from parsnip to the original package (censored regression)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{censored}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
+The \strong{censored} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
 
 boost_tree() \%>\% 
   set_engine("mboost") \%>\% 
diff --git a/man/details_cubist_rules_Cubist.Rd b/man/details_cubist_rules_Cubist.Rd
index ef48fe06a..beeef85da 100644
--- a/man/details_cubist_rules_Cubist.Rd
+++ b/man/details_cubist_rules_Cubist.Rd
@@ -22,8 +22,7 @@ This model has 3 tuning parameters:
 
 \subsection{Translation from parsnip to the underlying model call (regression)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{rules}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(rules)
+The \strong{rules} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(rules)
 
 cubist_rules(
   committees = integer(1),
@@ -44,8 +43,7 @@ cubist_rules(
 ## 
 ## Model fit template:
 ## rules::cubist_fit(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
-##     committees = integer(1), neighbors = integer(1), max_rules = integer(1), 
-##     composite = TRUE)
+##     committees = integer(1), neighbors = integer(1), max_rules = integer(1))
 }
 }
 
@@ -62,7 +60,7 @@ are not required for this model.
 \item Quinlan R (1992). “Learning with Continuous Classes.” Proceedings of
 the 5th Australian Joint Conference On Artificial Intelligence,
 pp. 343-348.
-\item Quinlan R (1993).”Combining Instance-Based and Model-Based
+\item Quinlan R (1993).“Combining Instance-Based and Model-Based
 Learning.” Proceedings of the Tenth International Conference on
 Machine Learning, pp. 236-243.
 \item Kuhn M and Johnson K (2013). \emph{Applied Predictive Modeling}.
diff --git a/man/details_decision_tree_party.Rd b/man/details_decision_tree_party.Rd
index 7f1ed6a49..8d38f802a 100644
--- a/man/details_decision_tree_party.Rd
+++ b/man/details_decision_tree_party.Rd
@@ -29,8 +29,7 @@ evaluated for splitting. The default is to use all predictors.
 
 \subsection{Translation from parsnip to the original package (censored regression)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{censored}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
+The \strong{censored} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
 
 decision_tree(tree_depth = integer(1), min_n = integer(1)) \%>\% 
   set_engine("party") \%>\% 
diff --git a/man/details_decision_tree_rpart.Rd b/man/details_decision_tree_rpart.Rd
index 8bffbbf61..04108769b 100644
--- a/man/details_decision_tree_rpart.Rd
+++ b/man/details_decision_tree_rpart.Rd
@@ -63,8 +63,7 @@ This model has 3 tuning parameters:
 
 \subsection{Translation from parsnip to the original package (censored regression)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{censored}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
+The \strong{censored} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
 
 decision_tree(
   tree_depth = integer(1),
diff --git a/man/details_discrim_flexible_earth.Rd b/man/details_discrim_flexible_earth.Rd
index 1fbe2806b..2bac1fc77 100644
--- a/man/details_discrim_flexible_earth.Rd
+++ b/man/details_discrim_flexible_earth.Rd
@@ -28,8 +28,7 @@ intercept-only model.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{discrim}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
+The \strong{discrim} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
 
 discrim_flexible(
   num_terms = integer(0),
@@ -56,8 +55,8 @@ discrim_flexible(
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 }
 
 \subsection{References}{
diff --git a/man/details_discrim_linear_MASS.Rd b/man/details_discrim_linear_MASS.Rd
index 232a9f7f7..cc2ca4162 100644
--- a/man/details_discrim_linear_MASS.Rd
+++ b/man/details_discrim_linear_MASS.Rd
@@ -18,8 +18,7 @@ This engine has no tuning parameters.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{discrim}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
+The \strong{discrim} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
 
 discrim_linear() \%>\% 
   set_engine("MASS") \%>\% 
@@ -37,8 +36,8 @@ discrim_linear() \%>\%
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Variance calculations are used in these computations so \emph{zero-variance}
 predictors (i.e., with a single unique value) should be eliminated
diff --git a/man/details_discrim_linear_mda.Rd b/man/details_discrim_linear_mda.Rd
index bdbff23a2..1ca65a955 100644
--- a/man/details_discrim_linear_mda.Rd
+++ b/man/details_discrim_linear_mda.Rd
@@ -20,8 +20,7 @@ This model has 1 tuning parameter:
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{discrim}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
+The \strong{discrim} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
 
 discrim_linear(penalty = numeric(0)) \%>\% 
   set_engine("mda") \%>\% 
@@ -43,8 +42,8 @@ discrim_linear(penalty = numeric(0)) \%>\%
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Variance calculations are used in these computations so \emph{zero-variance}
 predictors (i.e., with a single unique value) should be eliminated
diff --git a/man/details_discrim_linear_sda.Rd b/man/details_discrim_linear_sda.Rd
index 95506f7e0..49d56fe77 100644
--- a/man/details_discrim_linear_sda.Rd
+++ b/man/details_discrim_linear_sda.Rd
@@ -34,8 +34,7 @@ This maps to
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{discrim}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
+The \strong{discrim} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
 
 discrim_linear() \%>\% 
   set_engine("sda") \%>\% 
@@ -53,8 +52,8 @@ discrim_linear() \%>\%
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Variance calculations are used in these computations so \emph{zero-variance}
 predictors (i.e., with a single unique value) should be eliminated
diff --git a/man/details_discrim_linear_sparsediscrim.Rd b/man/details_discrim_linear_sparsediscrim.Rd
index 275dd538f..5b2a07e30 100644
--- a/man/details_discrim_linear_sparsediscrim.Rd
+++ b/man/details_discrim_linear_sparsediscrim.Rd
@@ -34,8 +34,7 @@ execute, are:
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{discrim}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
+The \strong{discrim} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
 
 discrim_linear(regularization_method = character(0)) \%>\% 
   set_engine("sparsediscrim") \%>\% 
@@ -57,8 +56,8 @@ discrim_linear(regularization_method = character(0)) \%>\%
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Variance calculations are used in these computations so \emph{zero-variance}
 predictors (i.e., with a single unique value) should be eliminated
diff --git a/man/details_discrim_quad_MASS.Rd b/man/details_discrim_quad_MASS.Rd
index ca1e8283d..375646ff3 100644
--- a/man/details_discrim_quad_MASS.Rd
+++ b/man/details_discrim_quad_MASS.Rd
@@ -18,8 +18,7 @@ This engine has no tuning parameters.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{discrim}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
+The \strong{discrim} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
 
 discrim_quad() \%>\% 
   set_engine("MASS") \%>\% 
@@ -37,8 +36,8 @@ discrim_quad() \%>\%
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Variance calculations are used in these computations within each outcome
 class. For this reason, \emph{zero-variance} predictors (i.e., with a single
diff --git a/man/details_discrim_quad_sparsediscrim.Rd b/man/details_discrim_quad_sparsediscrim.Rd
index fc9bbef07..bdac315ca 100644
--- a/man/details_discrim_quad_sparsediscrim.Rd
+++ b/man/details_discrim_quad_sparsediscrim.Rd
@@ -32,8 +32,7 @@ execute, are:
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{discrim}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
+The \strong{discrim} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
 
 discrim_quad(regularization_method = character(0)) \%>\% 
   set_engine("sparsediscrim") \%>\% 
@@ -55,8 +54,8 @@ discrim_quad(regularization_method = character(0)) \%>\%
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Variance calculations are used in these computations within each outcome
 class. For this reason, \emph{zero-variance} predictors (i.e., with a single
diff --git a/man/details_discrim_regularized_klaR.Rd b/man/details_discrim_regularized_klaR.Rd
index d30120792..1679b4110 100644
--- a/man/details_discrim_regularized_klaR.Rd
+++ b/man/details_discrim_regularized_klaR.Rd
@@ -33,8 +33,7 @@ discriminant analysis (QDA) model.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{discrim}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
+The \strong{discrim} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
 
 discrim_regularized(frac_identity = numeric(0), frac_common_cov = numeric(0)) \%>\% 
   set_engine("klaR") \%>\% 
@@ -57,8 +56,8 @@ discrim_regularized(frac_identity = numeric(0), frac_common_cov = numeric(0)) \%
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Variance calculations are used in these computations within each outcome
 class. For this reason, \emph{zero-variance} predictors (i.e., with a single
diff --git a/man/details_gen_additive_mod_mgcv.Rd b/man/details_gen_additive_mod_mgcv.Rd
index c432bcd1a..e52a7e7e5 100644
--- a/man/details_gen_additive_mod_mgcv.Rd
+++ b/man/details_gen_additive_mod_mgcv.Rd
@@ -87,8 +87,8 @@ the \code{adjust_deg_free} parameter.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 }
 
 \subsection{References}{
diff --git a/man/details_linear_reg_brulee.Rd b/man/details_linear_reg_brulee.Rd
index 63715217d..e0c7e85cf 100644
--- a/man/details_linear_reg_brulee.Rd
+++ b/man/details_linear_reg_brulee.Rd
@@ -59,8 +59,8 @@ no improvement before stopping. (default: 5L).
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_linear_reg_gee.Rd b/man/details_linear_reg_gee.Rd
index 717dc5cac..6256bf53a 100644
--- a/man/details_linear_reg_gee.Rd
+++ b/man/details_linear_reg_gee.Rd
@@ -20,8 +20,7 @@ values.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{multilevelmod}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
+The \strong{multilevelmod} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
 
 linear_reg() \%>\% 
   set_engine("gee") \%>\% 
@@ -83,7 +82,7 @@ fit(gee_wflow, data = warpbreaks)
 }\if{html}{\out{</div>}}
 
 The \code{gee::gee()} function always prints out warnings and output even
-when \code{silent = TRUE}. The parsnip “gee” engine, by contrast, silences
+when \code{silent = TRUE}. The parsnip \code{"gee"} engine, by contrast, silences
 all console output coming from \code{gee::gee()}, even if \code{silent = FALSE}.
 
 Also, because of issues with the \code{gee()} function, a supplementary call
diff --git a/man/details_linear_reg_glm.Rd b/man/details_linear_reg_glm.Rd
index 89e6a18a2..9e656f4ce 100644
--- a/man/details_linear_reg_glm.Rd
+++ b/man/details_linear_reg_glm.Rd
@@ -49,8 +49,8 @@ To use a non-default \code{family} and/or \code{link}, pass in as an argument to
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 }
 
 \subsection{Examples}{
diff --git a/man/details_linear_reg_glmnet.Rd b/man/details_linear_reg_glmnet.Rd
index c1caebb96..0db9b8a89 100644
--- a/man/details_linear_reg_glmnet.Rd
+++ b/man/details_linear_reg_glmnet.Rd
@@ -46,8 +46,8 @@ see \link{glmnet-details}.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_linear_reg_gls.Rd b/man/details_linear_reg_gls.Rd
index ac4c97c6c..1f633d1cb 100644
--- a/man/details_linear_reg_gls.Rd
+++ b/man/details_linear_reg_gls.Rd
@@ -16,8 +16,7 @@ This model has no tuning parameters.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{multilevelmod}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
+The \strong{multilevelmod} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
 
 linear_reg() \%>\% 
   set_engine("gls") \%>\% 
diff --git a/man/details_linear_reg_keras.Rd b/man/details_linear_reg_keras.Rd
index 4f2630662..0ad5fd777 100644
--- a/man/details_linear_reg_keras.Rd
+++ b/man/details_linear_reg_keras.Rd
@@ -43,8 +43,8 @@ single hidden unit.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_linear_reg_lm.Rd b/man/details_linear_reg_lm.Rd
index 888386139..a5587a1d0 100644
--- a/man/details_linear_reg_lm.Rd
+++ b/man/details_linear_reg_lm.Rd
@@ -29,8 +29,8 @@ This engine has no tuning parameters.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 }
 
 \subsection{Examples}{
diff --git a/man/details_linear_reg_lme.Rd b/man/details_linear_reg_lme.Rd
index 900aef0c5..a28ab33e5 100644
--- a/man/details_linear_reg_lme.Rd
+++ b/man/details_linear_reg_lme.Rd
@@ -16,8 +16,7 @@ This model has no tuning parameters.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{multilevelmod}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
+The \strong{multilevelmod} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
 
 linear_reg() \%>\% 
   set_engine("lme") \%>\% 
@@ -38,7 +37,7 @@ This model can use subject-specific coefficient estimates to make
 predictions (i.e. partial pooling). For example, this equation shows the
 linear predictor (\emph{η}) for a random intercept:
 
-\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}}+\emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
+\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}} + \emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
 
 where \emph{i} denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
diff --git a/man/details_linear_reg_lmer.Rd b/man/details_linear_reg_lmer.Rd
index c923e0d5b..8462694d1 100644
--- a/man/details_linear_reg_lmer.Rd
+++ b/man/details_linear_reg_lmer.Rd
@@ -16,8 +16,7 @@ This model has no tuning parameters.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{multilevelmod}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
+The \strong{multilevelmod} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
 
 linear_reg() \%>\% 
   set_engine("lmer") \%>\% 
@@ -38,7 +37,7 @@ This model can use subject-specific coefficient estimates to make
 predictions (i.e. partial pooling). For example, this equation shows the
 linear predictor (\emph{η}) for a random intercept:
 
-\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}}+\emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
+\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}} + \emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
 
 where \emph{i} denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
diff --git a/man/details_linear_reg_spark.Rd b/man/details_linear_reg_spark.Rd
index 6853f7041..5ed759dd1 100644
--- a/man/details_linear_reg_spark.Rd
+++ b/man/details_linear_reg_spark.Rd
@@ -45,8 +45,8 @@ A value of \code{mixture = 1} corresponds to a pure lasso model, while
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_linear_reg_stan.Rd b/man/details_linear_reg_stan.Rd
index 9e12761ec..048ad28ba 100644
--- a/man/details_linear_reg_stan.Rd
+++ b/man/details_linear_reg_stan.Rd
@@ -57,8 +57,8 @@ process. Change this value in \code{set_engine()} to show the MCMC logs.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 }
 
 \subsection{Other details}{
diff --git a/man/details_linear_reg_stan_glmer.Rd b/man/details_linear_reg_stan_glmer.Rd
index 32b8b4536..78c966e08 100644
--- a/man/details_linear_reg_stan_glmer.Rd
+++ b/man/details_linear_reg_stan_glmer.Rd
@@ -36,8 +36,7 @@ See \code{?rstanarm::stan_glmer} and \code{?rstan::sampling} for more informatio
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{multilevelmod}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
+The \strong{multilevelmod} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
 
 linear_reg() \%>\% 
   set_engine("stan_glmer") \%>\% 
@@ -59,7 +58,7 @@ This model can use subject-specific coefficient estimates to make
 predictions (i.e. partial pooling). For example, this equation shows the
 linear predictor (\emph{η}) for a random intercept:
 
-\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}}+\emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
+\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}} + \emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
 
 where \emph{i} denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
diff --git a/man/details_logistic_reg_LiblineaR.Rd b/man/details_logistic_reg_LiblineaR.Rd
index 57c7a64e1..c255ebff6 100644
--- a/man/details_logistic_reg_LiblineaR.Rd
+++ b/man/details_logistic_reg_LiblineaR.Rd
@@ -50,8 +50,8 @@ parameter estimates.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_logistic_reg_brulee.Rd b/man/details_logistic_reg_brulee.Rd
index d908b5486..ee103c3ea 100644
--- a/man/details_logistic_reg_brulee.Rd
+++ b/man/details_logistic_reg_brulee.Rd
@@ -59,8 +59,8 @@ no improvement before stopping. (default: 5L).
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_logistic_reg_gee.Rd b/man/details_logistic_reg_gee.Rd
index fc06b3b53..8de14d748 100644
--- a/man/details_logistic_reg_gee.Rd
+++ b/man/details_logistic_reg_gee.Rd
@@ -20,8 +20,7 @@ values.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{multilevelmod}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
+The \strong{multilevelmod} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
 
 logistic_reg() \%>\% 
   set_engine("gee") \%>\% 
@@ -83,7 +82,7 @@ fit(gee_wflow, data = toenail)
 }\if{html}{\out{</div>}}
 
 The \code{gee::gee()} function always prints out warnings and output even
-when \code{silent = TRUE}. The parsnip “gee” engine, by contrast, silences
+when \code{silent = TRUE}. The parsnip \code{"gee"} engine, by contrast, silences
 all console output coming from \code{gee::gee()}, even if \code{silent = FALSE}.
 
 Also, because of issues with the \code{gee()} function, a supplementary call
diff --git a/man/details_logistic_reg_glm.Rd b/man/details_logistic_reg_glm.Rd
index 755d9cc60..50a303716 100644
--- a/man/details_logistic_reg_glm.Rd
+++ b/man/details_logistic_reg_glm.Rd
@@ -49,8 +49,8 @@ To use a non-default \code{family} and/or \code{link}, pass in as an argument to
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 }
 
 \subsection{Examples}{
diff --git a/man/details_logistic_reg_glmer.Rd b/man/details_logistic_reg_glmer.Rd
index 73ee741e2..dcc386ae3 100644
--- a/man/details_logistic_reg_glmer.Rd
+++ b/man/details_logistic_reg_glmer.Rd
@@ -16,8 +16,7 @@ This model has no tuning parameters.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{multilevelmod}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
+The \strong{multilevelmod} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
 
 logistic_reg() \%>\% 
   set_engine("glmer") \%>\% 
@@ -37,7 +36,7 @@ This model can use subject-specific coefficient estimates to make
 predictions (i.e. partial pooling). For example, this equation shows the
 linear predictor (\emph{η}) for a random intercept:
 
-\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}}+\emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
+\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}} + \emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
 
 where \emph{i} denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
diff --git a/man/details_logistic_reg_glmnet.Rd b/man/details_logistic_reg_glmnet.Rd
index c4f0e9ab2..7fb503d01 100644
--- a/man/details_logistic_reg_glmnet.Rd
+++ b/man/details_logistic_reg_glmnet.Rd
@@ -48,8 +48,8 @@ see \link{glmnet-details}.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_logistic_reg_keras.Rd b/man/details_logistic_reg_keras.Rd
index dc4c2dfc2..6bf922f0f 100644
--- a/man/details_logistic_reg_keras.Rd
+++ b/man/details_logistic_reg_keras.Rd
@@ -45,8 +45,8 @@ single hidden unit.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_logistic_reg_spark.Rd b/man/details_logistic_reg_spark.Rd
index 899ba1370..ee470475f 100644
--- a/man/details_logistic_reg_spark.Rd
+++ b/man/details_logistic_reg_spark.Rd
@@ -47,8 +47,8 @@ A value of \code{mixture = 1} corresponds to a pure lasso model, while
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_logistic_reg_stan.Rd b/man/details_logistic_reg_stan.Rd
index a6c87e9a3..683f61a39 100644
--- a/man/details_logistic_reg_stan.Rd
+++ b/man/details_logistic_reg_stan.Rd
@@ -58,8 +58,8 @@ process. Change this value in \code{set_engine()} to show the MCMC logs.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 }
 
 \subsection{Other details}{
diff --git a/man/details_logistic_reg_stan_glmer.Rd b/man/details_logistic_reg_stan_glmer.Rd
index fb0e716cf..3e89ce7c6 100644
--- a/man/details_logistic_reg_stan_glmer.Rd
+++ b/man/details_logistic_reg_stan_glmer.Rd
@@ -36,8 +36,7 @@ See \code{?rstanarm::stan_glmer} and \code{?rstan::sampling} for more informatio
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{multilevelmod}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
+The \strong{multilevelmod} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
 
 logistic_reg() \%>\% 
   set_engine("stan_glmer") \%>\% 
@@ -58,7 +57,7 @@ This model can use subject-specific coefficient estimates to make
 predictions (i.e. partial pooling). For example, this equation shows the
 linear predictor (\emph{η}) for a random intercept:
 
-\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}}+\emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
+\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}} + \emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
 
 where \emph{i} denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
diff --git a/man/details_mars_earth.Rd b/man/details_mars_earth.Rd
index d99c15cd2..db85fac4f 100644
--- a/man/details_mars_earth.Rd
+++ b/man/details_mars_earth.Rd
@@ -76,8 +76,8 @@ in \code{\link[=discrim_flexible]{discrim_flexible()}}.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 }
 
 \subsection{Examples}{
diff --git a/man/details_mlp_brulee.Rd b/man/details_mlp_brulee.Rd
index 384fbce1b..deb3e578a 100644
--- a/man/details_mlp_brulee.Rd
+++ b/man/details_mlp_brulee.Rd
@@ -108,8 +108,8 @@ layer.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_mlp_keras.Rd b/man/details_mlp_keras.Rd
index 681e723d9..0a5f4935c 100644
--- a/man/details_mlp_keras.Rd
+++ b/man/details_mlp_keras.Rd
@@ -81,8 +81,8 @@ This model has 5 tuning parameters:
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_mlp_nnet.Rd b/man/details_mlp_nnet.Rd
index e290d269c..9bf5fb15e 100644
--- a/man/details_mlp_nnet.Rd
+++ b/man/details_mlp_nnet.Rd
@@ -78,8 +78,8 @@ layer.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_multinom_reg_brulee.Rd b/man/details_multinom_reg_brulee.Rd
index 6baf695a3..ed15244c0 100644
--- a/man/details_multinom_reg_brulee.Rd
+++ b/man/details_multinom_reg_brulee.Rd
@@ -58,8 +58,8 @@ no improvement before stopping. (default: 5L).
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_multinom_reg_glmnet.Rd b/man/details_multinom_reg_glmnet.Rd
index e58d12343..6c73fc3d9 100644
--- a/man/details_multinom_reg_glmnet.Rd
+++ b/man/details_multinom_reg_glmnet.Rd
@@ -47,8 +47,8 @@ see \link{glmnet-details}.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_multinom_reg_keras.Rd b/man/details_multinom_reg_keras.Rd
index 705adaba9..648209fad 100644
--- a/man/details_multinom_reg_keras.Rd
+++ b/man/details_multinom_reg_keras.Rd
@@ -44,8 +44,8 @@ single hidden unit.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_multinom_reg_nnet.Rd b/man/details_multinom_reg_nnet.Rd
index 868256f25..5d2875d3c 100644
--- a/man/details_multinom_reg_nnet.Rd
+++ b/man/details_multinom_reg_nnet.Rd
@@ -40,8 +40,8 @@ For \code{penalty}, the amount of regularization includes only the L2 penalty
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_multinom_reg_spark.Rd b/man/details_multinom_reg_spark.Rd
index 798462aa8..9ba725e06 100644
--- a/man/details_multinom_reg_spark.Rd
+++ b/man/details_multinom_reg_spark.Rd
@@ -46,8 +46,8 @@ A value of \code{mixture = 1} corresponds to a pure lasso model, while
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_naive_Bayes_klaR.Rd b/man/details_naive_Bayes_klaR.Rd
index c967156c0..182adffa4 100644
--- a/man/details_naive_Bayes_klaR.Rd
+++ b/man/details_naive_Bayes_klaR.Rd
@@ -22,8 +22,7 @@ Note that \code{usekernel} is always set to \code{TRUE} for the \code{klaR} engi
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{discrim}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
+The \strong{discrim} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
 
 naive_Bayes(smoothness = numeric(0), Laplace = numeric(0)) \%>\% 
   set_engine("klaR") \%>\% 
diff --git a/man/details_naive_Bayes_naivebayes.Rd b/man/details_naive_Bayes_naivebayes.Rd
index 222dd3c33..118aed909 100644
--- a/man/details_naive_Bayes_naivebayes.Rd
+++ b/man/details_naive_Bayes_naivebayes.Rd
@@ -20,8 +20,7 @@ This model has 2 tuning parameter:
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{discrim}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
+The \strong{discrim} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(discrim)
 
 naive_Bayes(smoothness = numeric(0), Laplace = numeric(0)) \%>\% 
   set_engine("naivebayes") \%>\% 
diff --git a/man/details_nearest_neighbor_kknn.Rd b/man/details_nearest_neighbor_kknn.Rd
index 0323a1b47..5b3c45205 100644
--- a/man/details_nearest_neighbor_kknn.Rd
+++ b/man/details_nearest_neighbor_kknn.Rd
@@ -73,8 +73,8 @@ it is not consistent with the actual data dimensions.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_pls_mixOmics.Rd b/man/details_pls_mixOmics.Rd
index 878971706..1444f2c30 100644
--- a/man/details_pls_mixOmics.Rd
+++ b/man/details_pls_mixOmics.Rd
@@ -20,8 +20,7 @@ see below)
 
 \subsection{Translation from parsnip to the underlying model call (regression)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{plsmod}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(plsmod)
+The \strong{plsmod} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(plsmod)
 
 pls(num_comp = integer(1), predictor_prop = double(1)) \%>\%
   set_engine("mixOmics") \%>\%
@@ -54,8 +53,7 @@ for sparse models.
 
 \subsection{Translation from parsnip to the underlying model call (classification)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{plsmod}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(plsmod)
+The \strong{plsmod} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(plsmod)
 
 pls(num_comp = integer(1), predictor_prop = double(1)) \%>\%
   set_engine("mixOmics") \%>\%
@@ -74,17 +72,18 @@ pls(num_comp = integer(1), predictor_prop = double(1)) \%>\%
 ##     ncomp = integer(1))
 }
 
-In this case, \code{\link[plsmod:pls_fit]{plsmod::pls_fit()}} has the same role
-as above but eventually targets \code{\link[mixOmics:plsda]{mixOmics::plsda()}}
-or \code{\link[mixOmics:splsda]{mixOmics::splsda()}} .
+In this case, \code{\link[plsmod:pls_fit]{plsmod::pls_fit()}} has the same
+role as above but eventually targets
+\code{\link[mixOmics:plsda]{mixOmics::plsda()}} or
+\code{\link[mixOmics:splsda]{mixOmics::splsda()}} .
 }
 
 \subsection{Preprocessing requirements}{
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Variance calculations are used in these computations so \emph{zero-variance}
 predictors (i.e., with a single unique value) should be eliminated
diff --git a/man/details_poisson_reg_gee.Rd b/man/details_poisson_reg_gee.Rd
index cdaf927ae..39eceea63 100644
--- a/man/details_poisson_reg_gee.Rd
+++ b/man/details_poisson_reg_gee.Rd
@@ -20,8 +20,7 @@ values.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{multilevelmod}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
+The \strong{multilevelmod} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
 
 poisson_reg(engine = "gee") \%>\% 
   set_engine("gee") \%>\% 
@@ -81,9 +80,9 @@ gee_wflow <-
 fit(gee_wflow, data = longitudinal_counts)
 }\if{html}{\out{</div>}}
 
-\code{gee()} always prints out warnings and output even when \code{silent = TRUE}.
-When using the \code{gee} engine, it will never produce output, even if
-\code{silent = FALSE}.
+The \code{gee::gee()} function always prints out warnings and output even
+when \code{silent = TRUE}. The parsnip \code{"gee"} engine, by contrast, silences
+all console output coming from \code{gee::gee()}, even if \code{silent = FALSE}.
 
 Also, because of issues with the \code{gee()} function, a supplementary call
 to \code{glm()} is needed to get the rank and QR decomposition objects so
diff --git a/man/details_poisson_reg_glm.Rd b/man/details_poisson_reg_glm.Rd
index e28cc33b4..3db81a0da 100644
--- a/man/details_poisson_reg_glm.Rd
+++ b/man/details_poisson_reg_glm.Rd
@@ -15,8 +15,7 @@ This engine has no tuning parameters.
 
 \subsection{Translation from parsnip to the underlying model call (regression)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{poissonreg}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(poissonreg)
+The \strong{poissonreg} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(poissonreg)
 
 poisson_reg() \%>\%
   set_engine("glm") \%>\%
@@ -35,8 +34,8 @@ poisson_reg() \%>\%
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 }
 }
 \keyword{internal}
diff --git a/man/details_poisson_reg_glmer.Rd b/man/details_poisson_reg_glmer.Rd
index 17b1c84ee..761e11a05 100644
--- a/man/details_poisson_reg_glmer.Rd
+++ b/man/details_poisson_reg_glmer.Rd
@@ -16,8 +16,7 @@ This model has no tuning parameters.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{multilevelmod}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
+The \strong{multilevelmod} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
 
 poisson_reg(engine = "glmer") \%>\% 
   set_engine("glmer") \%>\% 
@@ -37,7 +36,7 @@ This model can use subject-specific coefficient estimates to make
 predictions (i.e. partial pooling). For example, this equation shows the
 linear predictor (\emph{η}) for a random intercept:
 
-\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}}+\emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
+\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}} + \emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
 
 where \emph{i} denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
diff --git a/man/details_poisson_reg_glmnet.Rd b/man/details_poisson_reg_glmnet.Rd
index 5581c5be9..ab5a75e81 100644
--- a/man/details_poisson_reg_glmnet.Rd
+++ b/man/details_poisson_reg_glmnet.Rd
@@ -28,8 +28,7 @@ see \link{glmnet-details}.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{poissonreg}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(poissonreg)
+The \strong{poissonreg} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(poissonreg)
 
 poisson_reg(penalty = double(1), mixture = double(1)) \%>\% 
   set_engine("glmnet") \%>\% 
@@ -52,8 +51,8 @@ poisson_reg(penalty = double(1), mixture = double(1)) \%>\%
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_poisson_reg_hurdle.Rd b/man/details_poisson_reg_hurdle.Rd
index f4503d7fb..6274682d0 100644
--- a/man/details_poisson_reg_hurdle.Rd
+++ b/man/details_poisson_reg_hurdle.Rd
@@ -17,8 +17,7 @@ This engine has no tuning parameters.
 
 \subsection{Translation from parsnip to the underlying model call (regression)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{poissonreg}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(poissonreg)
+The \strong{poissonreg} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(poissonreg)
 
 poisson_reg() \%>\%
   set_engine("hurdle") \%>\%
@@ -36,8 +35,8 @@ poisson_reg() \%>\%
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 For this particular model, a special formula is used to specify which
 columns affect the counts and which affect the model for the probability
diff --git a/man/details_poisson_reg_stan.Rd b/man/details_poisson_reg_stan.Rd
index f24c77f56..ccfc6f404 100644
--- a/man/details_poisson_reg_stan.Rd
+++ b/man/details_poisson_reg_stan.Rd
@@ -39,8 +39,7 @@ and other options.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{poissonreg}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(poissonreg)
+The \strong{poissonreg} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(poissonreg)
 
 poisson_reg() \%>\% 
   set_engine("stan") \%>\% 
@@ -62,8 +61,8 @@ process. Change this value in \code{set_engine()} to show the MCMC logs.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 }
 
 \subsection{Other details}{
diff --git a/man/details_poisson_reg_stan_glmer.Rd b/man/details_poisson_reg_stan_glmer.Rd
index e77757818..4703ceb16 100644
--- a/man/details_poisson_reg_stan_glmer.Rd
+++ b/man/details_poisson_reg_stan_glmer.Rd
@@ -36,8 +36,7 @@ See \code{?rstanarm::stan_glmer} and \code{?rstan::sampling} for more informatio
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{multilevelmod}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
+The \strong{multilevelmod} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(multilevelmod)
 
 poisson_reg(engine = "stan_glmer") \%>\% 
   set_engine("stan_glmer") \%>\% 
@@ -58,7 +57,7 @@ This model can use subject-specific coefficient estimates to make
 predictions (i.e. partial pooling). For example, this equation shows the
 linear predictor (\emph{η}) for a random intercept:
 
-\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}}+\emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
+\emph{η}\if{html}{\out{<sub>}}\emph{i}\if{html}{\out{</sub>}} = (\emph{β}\if{html}{\out{<sub>}}0\if{html}{\out{</sub>}} + \emph{b}\if{html}{\out{<sub>}}0\emph{i}\if{html}{\out{</sub>}}) + \emph{β}\if{html}{\out{<sub>}}1\if{html}{\out{</sub>}}\emph{x}\if{html}{\out{<sub>}}\emph{i}1\if{html}{\out{</sub>}}
 
 where \emph{i} denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
diff --git a/man/details_poisson_reg_zeroinfl.Rd b/man/details_poisson_reg_zeroinfl.Rd
index 298ca88d2..2261d4a19 100644
--- a/man/details_poisson_reg_zeroinfl.Rd
+++ b/man/details_poisson_reg_zeroinfl.Rd
@@ -17,8 +17,7 @@ This engine has no tuning parameters.
 
 \subsection{Translation from parsnip to the underlying model call (regression)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{poissonreg}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(poissonreg)
+The \strong{poissonreg} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(poissonreg)
 
 poisson_reg() \%>\%
   set_engine("zeroinfl") \%>\%
@@ -37,8 +36,8 @@ poisson_reg() \%>\%
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 For this particular model, a special formula is used to specify which
 columns affect the counts and which affect the model for the probability
diff --git a/man/details_proportional_hazards_glmnet.Rd b/man/details_proportional_hazards_glmnet.Rd
index eaa584b45..f23032f1c 100644
--- a/man/details_proportional_hazards_glmnet.Rd
+++ b/man/details_proportional_hazards_glmnet.Rd
@@ -27,8 +27,7 @@ see \link{glmnet-details}.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{censored}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
+The \strong{censored} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
 
 proportional_hazards(penalty = double(1), mixture = double(1)) \%>\% 
   set_engine("glmnet") \%>\% 
@@ -51,8 +50,8 @@ proportional_hazards(penalty = double(1), mixture = double(1)) \%>\%
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_proportional_hazards_survival.Rd b/man/details_proportional_hazards_survival.Rd
index 84fe2fb40..6caaf7cd4 100644
--- a/man/details_proportional_hazards_survival.Rd
+++ b/man/details_proportional_hazards_survival.Rd
@@ -15,8 +15,7 @@ This model has no tuning parameters.
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{censored}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
+The \strong{censored} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
 
 proportional_hazards() \%>\% 
   set_engine("survival") \%>\% 
diff --git a/man/details_rand_forest_party.Rd b/man/details_rand_forest_party.Rd
index d5fb9b486..6951629bf 100644
--- a/man/details_rand_forest_party.Rd
+++ b/man/details_rand_forest_party.Rd
@@ -22,8 +22,7 @@ This model has 3 tuning parameters:
 
 \subsection{Translation from parsnip to the original package (censored regression)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{censored}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
+The \strong{censored} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
 
 rand_forest() \%>\% 
   set_engine("party") \%>\% 
diff --git a/man/details_rule_fit_xrf.Rd b/man/details_rule_fit_xrf.Rd
index 86e2ada73..3febb5875 100644
--- a/man/details_rule_fit_xrf.Rd
+++ b/man/details_rule_fit_xrf.Rd
@@ -30,8 +30,7 @@ default: 1.0)
 
 \subsection{Translation from parsnip to the underlying model call (regression)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{rules}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(rules)
+The \strong{rules} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(rules)
 
 rule_fit(
   mtry = numeric(1),
@@ -70,8 +69,7 @@ rule_fit(
 
 \subsection{Translation from parsnip to the underlying model call (classification)}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{rules}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(rules)
+The \strong{rules} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(rules)
 
 rule_fit(
   mtry = numeric(1),
@@ -133,8 +131,8 @@ whereas \strong{xrf} uses an internal 5-fold cross-validation to determine it
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 }
 
 \subsection{References}{
diff --git a/man/details_survival_reg_flexsurv.Rd b/man/details_survival_reg_flexsurv.Rd
index 25f3a705e..084ff7ecf 100644
--- a/man/details_survival_reg_flexsurv.Rd
+++ b/man/details_survival_reg_flexsurv.Rd
@@ -18,8 +18,7 @@ This model has 1 tuning parameters:
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{censored}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
+The \strong{censored} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
 
 survival_reg(dist = character(1)) \%>\% 
   set_engine("flexsurv") \%>\% 
diff --git a/man/details_survival_reg_survival.Rd b/man/details_survival_reg_survival.Rd
index 26fe8558e..e84e7f5fd 100644
--- a/man/details_survival_reg_survival.Rd
+++ b/man/details_survival_reg_survival.Rd
@@ -18,8 +18,7 @@ This model has 1 tuning parameters:
 
 \subsection{Translation from parsnip to the original package}{
 
-There is a parsnip extension package required to fit this model to this
-mode: \strong{censored}.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
+The \strong{censored} extension package is required to fit this model.\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(censored)
 
 survival_reg(dist = character(1)) \%>\% 
   set_engine("survival") \%>\% 
diff --git a/man/details_svm_linear_LiblineaR.Rd b/man/details_svm_linear_LiblineaR.Rd
index e2cd2ab83..c3ef574ee 100644
--- a/man/details_svm_linear_LiblineaR.Rd
+++ b/man/details_svm_linear_LiblineaR.Rd
@@ -76,8 +76,8 @@ class predictions (e.g., accuracy).
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_svm_linear_kernlab.Rd b/man/details_svm_linear_kernlab.Rd
index dd6726a5e..bcd98b23b 100644
--- a/man/details_svm_linear_kernlab.Rd
+++ b/man/details_svm_linear_kernlab.Rd
@@ -73,8 +73,8 @@ by R’s random number stream.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_svm_poly_kernlab.Rd b/man/details_svm_poly_kernlab.Rd
index ed1d80f78..f28b8cef5 100644
--- a/man/details_svm_poly_kernlab.Rd
+++ b/man/details_svm_poly_kernlab.Rd
@@ -85,8 +85,8 @@ by R’s random number stream.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/details_svm_rbf_kernlab.Rd b/man/details_svm_rbf_kernlab.Rd
index 05f4f8e30..076699ea5 100644
--- a/man/details_svm_rbf_kernlab.Rd
+++ b/man/details_svm_rbf_kernlab.Rd
@@ -85,8 +85,8 @@ by R’s random number stream.
 
 Factor/categorical predictors need to be converted to numeric values
 (e.g., dummy or indicator variables) for this engine. When using the
-formula method via \code{\link[=fit.model_spec]{fit.model_spec()}},
-parsnip will convert factor columns to indicators.
+formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip
+will convert factor columns to indicators.
 
 Predictors should have the same scale. One way to achieve this is to
 center and scale each so that each predictor has mean zero and a
diff --git a/man/rmd/C5_rules_C5.0.md b/man/rmd/C5_rules_C5.0.md
index 631a299fa..99f14ae5c 100644
--- a/man/rmd/C5_rules_C5.0.md
+++ b/man/rmd/C5_rules_C5.0.md
@@ -15,7 +15,7 @@ Note that C5.0 has a tool for _early stopping_ during boosting where less iterat
 
 ## Translation from parsnip to the underlying model call  (classification)
 
-There is a parsnip extension package required to fit this model to this mode: **rules**.
+The **rules** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/aaa.Rmd b/man/rmd/aaa.Rmd
index d1af9ab90..ce4cf2485 100644
--- a/man/rmd/aaa.Rmd
+++ b/man/rmd/aaa.Rmd
@@ -127,13 +127,14 @@ uses_extension <- function(mod, eng, mod_mode) {
     purrr::pluck("pkg")
   
   num_ext <- length(exts)
+  if (num_ext > 1) {
+    rlang::abort(c("There are more than one extension packages for:",
+                   mod, eng, mod_mode))
+  }
   if (num_ext > 0) {
-    res <- paste0("**", exts, "**", collapse = ", ")
-    x <-
-      ifelse(num_ext > 1,
-             "There are parsnip extension packages ",
-             "There is a parsnip extension package ")
-    res <- paste0(x, "required to fit this model to this mode: ", res, ".")
+    res <- paste0("The **", 
+                  exts, 
+                  "** extension package is required to fit this model.")
   } else {
     res <- ""
   }
diff --git a/man/rmd/bag_mars_earth.md b/man/rmd/bag_mars_earth.md
index 466f3d43c..35a5db199 100644
--- a/man/rmd/bag_mars_earth.md
+++ b/man/rmd/bag_mars_earth.md
@@ -19,7 +19,7 @@ The default value of `num_terms` depends on the number of predictor columns. For
 
 ## Translation from parsnip to the original package (regression)
 
-There is a parsnip extension package required to fit this model to this mode: **baguette**.
+The **baguette** extension package is required to fit this model.
 
 
 ```r
@@ -47,7 +47,7 @@ bag_mars(num_terms = integer(1), prod_degree = integer(1), prune_method = charac
 
 ## Translation from parsnip to the original package (classification)
 
-There is a parsnip extension package required to fit this model to this mode: **baguette**.
+The **baguette** extension package is required to fit this model.
 
 
 ```r
@@ -82,7 +82,7 @@ bag_mars(
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 ## References
 
diff --git a/man/rmd/bag_tree_C5.0.md b/man/rmd/bag_tree_C5.0.md
index d5a1597b2..08bccb732 100644
--- a/man/rmd/bag_tree_C5.0.md
+++ b/man/rmd/bag_tree_C5.0.md
@@ -13,7 +13,7 @@ This model has 1 tuning parameters:
 
 ## Translation from parsnip to the original package (classification)
 
-There is a parsnip extension package required to fit this model to this mode: **baguette**.
+The **baguette** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/bag_tree_rpart.md b/man/rmd/bag_tree_rpart.md
index 33c684c3e..a47072d0b 100644
--- a/man/rmd/bag_tree_rpart.md
+++ b/man/rmd/bag_tree_rpart.md
@@ -22,7 +22,7 @@ For the `class_cost` parameter, the value can be a non-negative scalar for a cla
 
 ## Translation from parsnip to the original package (classification)
 
-There is a parsnip extension package required to fit this model to this mode: **baguette**.
+The **baguette** extension package is required to fit this model.
 
 
 ```r
@@ -53,7 +53,7 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1
 
 ## Translation from parsnip to the original package (regression)
 
-There is a parsnip extension package required to fit this model to this mode: **baguette**.
+The **baguette** extension package is required to fit this model.
 
 
 ```r
@@ -83,7 +83,7 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1
 
 ## Translation from parsnip to the original package (censored regression)
 
-There is a parsnip extension package required to fit this model to this mode: **censored**.
+The **censored** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/bart_dbarts.md b/man/rmd/bart_dbarts.md
index ea8595476..05b962991 100644
--- a/man/rmd/bart_dbarts.md
+++ b/man/rmd/bart_dbarts.md
@@ -103,7 +103,7 @@ bart(
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 [dbarts::bart()] will also convert the factors to indicators if the user does not create them first. 
 
diff --git a/man/rmd/boost_tree_mboost.md b/man/rmd/boost_tree_mboost.md
index 04f751d7f..6bacca070 100644
--- a/man/rmd/boost_tree_mboost.md
+++ b/man/rmd/boost_tree_mboost.md
@@ -23,7 +23,7 @@ The `mtry` parameter is related to the number of predictors. The default is to u
 
 ## Translation from parsnip to the original package (censored regression)
 
-There is a parsnip extension package required to fit this model to this mode: **censored**.
+The **censored** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/cubist_rules_Cubist.md b/man/rmd/cubist_rules_Cubist.md
index 1680737a4..4ce1e2f33 100644
--- a/man/rmd/cubist_rules_Cubist.md
+++ b/man/rmd/cubist_rules_Cubist.md
@@ -18,7 +18,7 @@ This model has 3 tuning parameters:
 
 ## Translation from parsnip to the underlying model call  (regression)
 
-There is a parsnip extension package required to fit this model to this mode: **rules**.
+The **rules** extension package is required to fit this model.
 
 
 ```r
@@ -46,8 +46,7 @@ cubist_rules(
 ## 
 ## Model fit template:
 ## rules::cubist_fit(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
-##     committees = integer(1), neighbors = integer(1), max_rules = integer(1), 
-##     composite = TRUE)
+##     committees = integer(1), neighbors = integer(1), max_rules = integer(1))
 ```
 
 ## Preprocessing requirements
diff --git a/man/rmd/decision_tree_party.md b/man/rmd/decision_tree_party.md
index 3d11deee1..acb160d67 100644
--- a/man/rmd/decision_tree_party.md
+++ b/man/rmd/decision_tree_party.md
@@ -21,7 +21,7 @@ An engine-specific parameter for this model is:
 
 ## Translation from parsnip to the original package (censored regression)
 
-There is a parsnip extension package required to fit this model to this mode: **censored**.
+The **censored** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/decision_tree_rpart.md b/man/rmd/decision_tree_rpart.md
index 65aafff4b..042f71726 100644
--- a/man/rmd/decision_tree_rpart.md
+++ b/man/rmd/decision_tree_rpart.md
@@ -71,7 +71,7 @@ decision_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = dou
 
 ## Translation from parsnip to the original package (censored regression)
 
-There is a parsnip extension package required to fit this model to this mode: **censored**.
+The **censored** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/discrim_flexible_earth.md b/man/rmd/discrim_flexible_earth.md
index 2dc556ca2..8fed9cda4 100644
--- a/man/rmd/discrim_flexible_earth.md
+++ b/man/rmd/discrim_flexible_earth.md
@@ -19,7 +19,7 @@ The default value of `num_terms` depends on the number of columns (`p`): `min(20
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **discrim**.
+The **discrim** extension package is required to fit this model.
 
 
 ```r
@@ -51,7 +51,7 @@ discrim_flexible(
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 ## References
diff --git a/man/rmd/discrim_linear_MASS.md b/man/rmd/discrim_linear_MASS.md
index c1a5eb4d6..234a5b64f 100644
--- a/man/rmd/discrim_linear_MASS.md
+++ b/man/rmd/discrim_linear_MASS.md
@@ -9,7 +9,7 @@ This engine has no tuning parameters.
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **discrim**.
+The **discrim** extension package is required to fit this model.
 
 
 ```r
@@ -32,7 +32,7 @@ discrim_linear() %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Variance calculations are used in these computations so _zero-variance_ predictors (i.e., with a single unique value) should be eliminated before fitting the model. 
diff --git a/man/rmd/discrim_linear_mda.md b/man/rmd/discrim_linear_mda.md
index e32de4d70..045d9502b 100644
--- a/man/rmd/discrim_linear_mda.md
+++ b/man/rmd/discrim_linear_mda.md
@@ -14,7 +14,7 @@ This model has 1 tuning parameter:
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **discrim**.
+The **discrim** extension package is required to fit this model.
 
 
 ```r
@@ -41,7 +41,7 @@ discrim_linear(penalty = numeric(0)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Variance calculations are used in these computations so _zero-variance_ predictors (i.e., with a single unique value) should be eliminated before fitting the model. 
diff --git a/man/rmd/discrim_linear_sda.md b/man/rmd/discrim_linear_sda.md
index 2ee3511fa..b94cb28c9 100644
--- a/man/rmd/discrim_linear_sda.md
+++ b/man/rmd/discrim_linear_sda.md
@@ -19,7 +19,7 @@ However, there are a few engine-specific parameters that can be set or optimized
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **discrim**.
+The **discrim** extension package is required to fit this model.
 
 
 ```r
@@ -42,7 +42,7 @@ discrim_linear() %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Variance calculations are used in these computations so _zero-variance_ predictors (i.e., with a single unique value) should be eliminated before fitting the model. 
diff --git a/man/rmd/discrim_linear_sparsediscrim.md b/man/rmd/discrim_linear_sparsediscrim.md
index fed84696d..2ea48fb2d 100644
--- a/man/rmd/discrim_linear_sparsediscrim.md
+++ b/man/rmd/discrim_linear_sparsediscrim.md
@@ -20,7 +20,7 @@ The possible values of this parameter, and the functions that they execute, are:
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **discrim**.
+The **discrim** extension package is required to fit this model.
 
 
 ```r
@@ -47,7 +47,7 @@ discrim_linear(regularization_method = character(0)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Variance calculations are used in these computations so _zero-variance_ predictors (i.e., with a single unique value) should be eliminated before fitting the model. 
diff --git a/man/rmd/discrim_quad_MASS.md b/man/rmd/discrim_quad_MASS.md
index 9bf02973d..bbf121130 100644
--- a/man/rmd/discrim_quad_MASS.md
+++ b/man/rmd/discrim_quad_MASS.md
@@ -9,7 +9,7 @@ This engine has no tuning parameters.
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **discrim**.
+The **discrim** extension package is required to fit this model.
 
 
 ```r
@@ -32,7 +32,7 @@ discrim_quad() %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Variance calculations are used in these computations within each outcome class. For this reason,  _zero-variance_ predictors (i.e., with a single unique value) within each class should be eliminated before fitting the model. 
diff --git a/man/rmd/discrim_quad_sparsediscrim.md b/man/rmd/discrim_quad_sparsediscrim.md
index efcb5f158..a3cc0d175 100644
--- a/man/rmd/discrim_quad_sparsediscrim.md
+++ b/man/rmd/discrim_quad_sparsediscrim.md
@@ -19,7 +19,7 @@ The possible values of this parameter, and the functions that they execute, are:
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **discrim**.
+The **discrim** extension package is required to fit this model.
 
 
 ```r
@@ -46,7 +46,7 @@ discrim_quad(regularization_method = character(0)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Variance calculations are used in these computations within each outcome class. For this reason,  _zero-variance_ predictors (i.e., with a single unique value) within each class should be eliminated before fitting the model. 
diff --git a/man/rmd/discrim_regularized_klaR.md b/man/rmd/discrim_regularized_klaR.md
index 63238fd8b..96f8a93a1 100644
--- a/man/rmd/discrim_regularized_klaR.md
+++ b/man/rmd/discrim_regularized_klaR.md
@@ -24,7 +24,7 @@ Some special cases for the RDA model:
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **discrim**.
+The **discrim** extension package is required to fit this model.
 
 
 ```r
@@ -52,7 +52,7 @@ discrim_regularized(frac_identity = numeric(0), frac_common_cov = numeric(0)) %>
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Variance calculations are used in these computations within each outcome class. For this reason,  _zero-variance_ predictors (i.e., with a single unique value) within each class should be eliminated before fitting the model. 
diff --git a/man/rmd/gen_additive_mod_mgcv.md b/man/rmd/gen_additive_mod_mgcv.md
index 727f21039..b78e38603 100644
--- a/man/rmd/gen_additive_mod_mgcv.md
+++ b/man/rmd/gen_additive_mod_mgcv.md
@@ -98,7 +98,7 @@ The smoothness of the terms will need to be manually specified (e.g., using `s(x
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 ## References
 
diff --git a/man/rmd/linear_reg_brulee.md b/man/rmd/linear_reg_brulee.md
index 92e3fea84..d5d7d4fc6 100644
--- a/man/rmd/linear_reg_brulee.md
+++ b/man/rmd/linear_reg_brulee.md
@@ -51,7 +51,7 @@ linear_reg(penalty = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/linear_reg_gee.Rmd b/man/rmd/linear_reg_gee.Rmd
index 55c73a7cd..5e6fe8603 100644
--- a/man/rmd/linear_reg_gee.Rmd
+++ b/man/rmd/linear_reg_gee.Rmd
@@ -64,8 +64,8 @@ gee_wflow <-
 
 fit(gee_wflow, data = warpbreaks)
 ```
-
-The `gee::gee()` function always prints out warnings and output even when `silent = TRUE`. The parsnip "gee" engine, by contrast, silences all console output coming from `gee::gee()`, even if `silent = FALSE`.
+```{r child = "template-gee-silent.Rmd"}
+```
 
 Also, because of issues with the `gee()` function, a supplementary call to `glm()` is needed to get the rank and QR decomposition objects so that `predict()` can be used.
 
diff --git a/man/rmd/linear_reg_gee.md b/man/rmd/linear_reg_gee.md
index e4012adb1..835542c94 100644
--- a/man/rmd/linear_reg_gee.md
+++ b/man/rmd/linear_reg_gee.md
@@ -9,7 +9,7 @@ This model has no formal tuning parameters. It may be beneficial to determine th
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **multilevelmod**.
+The **multilevelmod** extension package is required to fit this model.
 
 
 ```r
@@ -76,7 +76,7 @@ gee_wflow <-
 fit(gee_wflow, data = warpbreaks)
 ```
 
-The `gee::gee()` function always prints out warnings and output even when `silent = TRUE`. The parsnip "gee" engine, by contrast, silences all console output coming from `gee::gee()`, even if `silent = FALSE`.
+The `gee::gee()` function always prints out warnings and output even when `silent = TRUE`. The parsnip `"gee"` engine, by contrast, silences all console output coming from `gee::gee()`, even if `silent = FALSE`.
 
 Also, because of issues with the `gee()` function, a supplementary call to `glm()` is needed to get the rank and QR decomposition objects so that `predict()` can be used.
 
diff --git a/man/rmd/linear_reg_glm.md b/man/rmd/linear_reg_glm.md
index 908014743..15410f4d5 100644
--- a/man/rmd/linear_reg_glm.md
+++ b/man/rmd/linear_reg_glm.md
@@ -51,7 +51,7 @@ linear_reg() %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 ## Examples 
 
diff --git a/man/rmd/linear_reg_glmnet.md b/man/rmd/linear_reg_glmnet.md
index 8835ebde4..377c93b1f 100644
--- a/man/rmd/linear_reg_glmnet.md
+++ b/man/rmd/linear_reg_glmnet.md
@@ -43,7 +43,7 @@ linear_reg(penalty = double(1), mixture = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/linear_reg_gls.md b/man/rmd/linear_reg_gls.md
index 5731f09fe..68116d07f 100644
--- a/man/rmd/linear_reg_gls.md
+++ b/man/rmd/linear_reg_gls.md
@@ -9,7 +9,7 @@ This model has no tuning parameters.
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **multilevelmod**.
+The **multilevelmod** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/linear_reg_keras.md b/man/rmd/linear_reg_keras.md
index 756913fe3..e50251812 100644
--- a/man/rmd/linear_reg_keras.md
+++ b/man/rmd/linear_reg_keras.md
@@ -40,7 +40,7 @@ linear_reg(penalty = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/linear_reg_lm.md b/man/rmd/linear_reg_lm.md
index 67716b628..d8b203c7d 100644
--- a/man/rmd/linear_reg_lm.md
+++ b/man/rmd/linear_reg_lm.md
@@ -28,7 +28,7 @@ linear_reg() %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 ## Examples 
 
diff --git a/man/rmd/linear_reg_lme.md b/man/rmd/linear_reg_lme.md
index ba323d310..c49497d28 100644
--- a/man/rmd/linear_reg_lme.md
+++ b/man/rmd/linear_reg_lme.md
@@ -9,7 +9,7 @@ This model has no tuning parameters.
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **multilevelmod**.
+The **multilevelmod** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/linear_reg_lmer.md b/man/rmd/linear_reg_lmer.md
index 396770813..ff42e214a 100644
--- a/man/rmd/linear_reg_lmer.md
+++ b/man/rmd/linear_reg_lmer.md
@@ -9,7 +9,7 @@ This model has no tuning parameters.
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **multilevelmod**.
+The **multilevelmod** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/linear_reg_spark.md b/man/rmd/linear_reg_spark.md
index 75ace131f..7db4bfa74 100644
--- a/man/rmd/linear_reg_spark.md
+++ b/man/rmd/linear_reg_spark.md
@@ -43,7 +43,7 @@ linear_reg(penalty = double(1), mixture = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/linear_reg_stan.md b/man/rmd/linear_reg_stan.md
index 40b6e8a38..0f7b8bc60 100644
--- a/man/rmd/linear_reg_stan.md
+++ b/man/rmd/linear_reg_stan.md
@@ -44,7 +44,7 @@ Note that the `refresh` default prevents logging of the estimation process. Chan
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 ## Other details
 
diff --git a/man/rmd/linear_reg_stan_glmer.md b/man/rmd/linear_reg_stan_glmer.md
index f6cbb7898..9dd2403de 100644
--- a/man/rmd/linear_reg_stan_glmer.md
+++ b/man/rmd/linear_reg_stan_glmer.md
@@ -22,7 +22,7 @@ See `?rstanarm::stan_glmer` and `?rstan::sampling` for more information.
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **multilevelmod**.
+The **multilevelmod** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/logistic_reg_LiblineaR.md b/man/rmd/logistic_reg_LiblineaR.md
index 194ab9b47..e0d308e0f 100644
--- a/man/rmd/logistic_reg_LiblineaR.md
+++ b/man/rmd/logistic_reg_LiblineaR.md
@@ -43,7 +43,7 @@ logistic_reg(penalty = double(1), mixture = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/logistic_reg_brulee.md b/man/rmd/logistic_reg_brulee.md
index 50c9879ab..ca79d0192 100644
--- a/man/rmd/logistic_reg_brulee.md
+++ b/man/rmd/logistic_reg_brulee.md
@@ -50,7 +50,7 @@ logistic_reg(penalty = double(1)) %>%
 
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/logistic_reg_gee.Rmd b/man/rmd/logistic_reg_gee.Rmd
index ca02f34b4..219750979 100644
--- a/man/rmd/logistic_reg_gee.Rmd
+++ b/man/rmd/logistic_reg_gee.Rmd
@@ -64,8 +64,8 @@ gee_wflow <-
 
 fit(gee_wflow, data = toenail)
 ```
-
-The `gee::gee()` function always prints out warnings and output even when `silent = TRUE`. The parsnip "gee" engine, by contrast, silences all console output coming from `gee::gee()`, even if `silent = FALSE`.
+```{r child = "template-gee-silent.Rmd"}
+```
 
 Also, because of issues with the `gee()` function, a supplementary call to `glm()` is needed to get the rank and QR decomposition objects so that `predict()` can be used.
 
diff --git a/man/rmd/logistic_reg_gee.md b/man/rmd/logistic_reg_gee.md
index cb4a83f59..84f7de293 100644
--- a/man/rmd/logistic_reg_gee.md
+++ b/man/rmd/logistic_reg_gee.md
@@ -9,7 +9,7 @@ This model has no formal tuning parameters. It may be beneficial to determine th
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **multilevelmod**.
+The **multilevelmod** extension package is required to fit this model.
 
 
 ```r
@@ -76,7 +76,7 @@ gee_wflow <-
 fit(gee_wflow, data = toenail)
 ```
 
-The `gee::gee()` function always prints out warnings and output even when `silent = TRUE`. The parsnip "gee" engine, by contrast, silences all console output coming from `gee::gee()`, even if `silent = FALSE`.
+The `gee::gee()` function always prints out warnings and output even when `silent = TRUE`. The parsnip `"gee"` engine, by contrast, silences all console output coming from `gee::gee()`, even if `silent = FALSE`.
 
 Also, because of issues with the `gee()` function, a supplementary call to `glm()` is needed to get the rank and QR decomposition objects so that `predict()` can be used.
 
diff --git a/man/rmd/logistic_reg_glm.md b/man/rmd/logistic_reg_glm.md
index eb03e06c7..af17affb4 100644
--- a/man/rmd/logistic_reg_glm.md
+++ b/man/rmd/logistic_reg_glm.md
@@ -51,7 +51,7 @@ linear_reg() %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 ## Examples 
 
diff --git a/man/rmd/logistic_reg_glmer.md b/man/rmd/logistic_reg_glmer.md
index c71ee2988..2a374bdc7 100644
--- a/man/rmd/logistic_reg_glmer.md
+++ b/man/rmd/logistic_reg_glmer.md
@@ -9,7 +9,7 @@ This model has no tuning parameters.
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **multilevelmod**.
+The **multilevelmod** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/logistic_reg_glmnet.md b/man/rmd/logistic_reg_glmnet.md
index f52582fb8..304b7f657 100644
--- a/man/rmd/logistic_reg_glmnet.md
+++ b/man/rmd/logistic_reg_glmnet.md
@@ -43,7 +43,7 @@ logistic_reg(penalty = double(1), mixture = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/logistic_reg_keras.md b/man/rmd/logistic_reg_keras.md
index db24ece0a..a51c0fba6 100644
--- a/man/rmd/logistic_reg_keras.md
+++ b/man/rmd/logistic_reg_keras.md
@@ -40,7 +40,7 @@ logistic_reg(penalty = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/logistic_reg_spark.md b/man/rmd/logistic_reg_spark.md
index f9c9f252d..90d98c2e1 100644
--- a/man/rmd/logistic_reg_spark.md
+++ b/man/rmd/logistic_reg_spark.md
@@ -44,7 +44,7 @@ logistic_reg(penalty = double(1), mixture = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/logistic_reg_stan.md b/man/rmd/logistic_reg_stan.md
index b244d592e..11190f9e2 100644
--- a/man/rmd/logistic_reg_stan.md
+++ b/man/rmd/logistic_reg_stan.md
@@ -44,7 +44,7 @@ Note that the `refresh` default prevents logging of the estimation process. Chan
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 ## Other details
 
diff --git a/man/rmd/logistic_reg_stan_glmer.md b/man/rmd/logistic_reg_stan_glmer.md
index 32f139df5..953b552df 100644
--- a/man/rmd/logistic_reg_stan_glmer.md
+++ b/man/rmd/logistic_reg_stan_glmer.md
@@ -22,7 +22,7 @@ See `?rstanarm::stan_glmer` and `?rstan::sampling` for more information.
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **multilevelmod**.
+The **multilevelmod** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/mars_earth.md b/man/rmd/mars_earth.md
index c3a5f5f3e..e8d8688be 100644
--- a/man/rmd/mars_earth.md
+++ b/man/rmd/mars_earth.md
@@ -78,7 +78,7 @@ An alternate method for using MARs for categorical outcomes can be found in [dis
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 ## Examples 
 
diff --git a/man/rmd/mlp_brulee.md b/man/rmd/mlp_brulee.md
index 520f4c473..cd86bf9f4 100644
--- a/man/rmd/mlp_brulee.md
+++ b/man/rmd/mlp_brulee.md
@@ -113,7 +113,7 @@ mlp(
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/mlp_keras.md b/man/rmd/mlp_keras.md
index 4f42ade7b..628bf56f4 100644
--- a/man/rmd/mlp_keras.md
+++ b/man/rmd/mlp_keras.md
@@ -91,7 +91,7 @@ mlp(
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/mlp_nnet.md b/man/rmd/mlp_nnet.md
index 6776d9347..6866b1516 100644
--- a/man/rmd/mlp_nnet.md
+++ b/man/rmd/mlp_nnet.md
@@ -84,7 +84,7 @@ mlp(
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/multinom_reg_brulee.md b/man/rmd/multinom_reg_brulee.md
index 8c78c3632..8cfbc5f42 100644
--- a/man/rmd/multinom_reg_brulee.md
+++ b/man/rmd/multinom_reg_brulee.md
@@ -50,7 +50,7 @@ multinom_reg(penalty = double(1)) %>%
 
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/multinom_reg_glmnet.md b/man/rmd/multinom_reg_glmnet.md
index 0d82f43b1..abad257f7 100644
--- a/man/rmd/multinom_reg_glmnet.md
+++ b/man/rmd/multinom_reg_glmnet.md
@@ -43,7 +43,7 @@ multinom_reg(penalty = double(1), mixture = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/multinom_reg_keras.md b/man/rmd/multinom_reg_keras.md
index 928f1836a..6a88fde7c 100644
--- a/man/rmd/multinom_reg_keras.md
+++ b/man/rmd/multinom_reg_keras.md
@@ -40,7 +40,7 @@ multinom_reg(penalty = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/multinom_reg_nnet.md b/man/rmd/multinom_reg_nnet.md
index bbc882edf..f7072b433 100644
--- a/man/rmd/multinom_reg_nnet.md
+++ b/man/rmd/multinom_reg_nnet.md
@@ -38,7 +38,7 @@ multinom_reg(penalty = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/multinom_reg_spark.md b/man/rmd/multinom_reg_spark.md
index 14a1cf2dd..4ee148d72 100644
--- a/man/rmd/multinom_reg_spark.md
+++ b/man/rmd/multinom_reg_spark.md
@@ -44,7 +44,7 @@ multinom_reg(penalty = double(1), mixture = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/naive_Bayes_klaR.md b/man/rmd/naive_Bayes_klaR.md
index 2c70b328c..c71f3e031 100644
--- a/man/rmd/naive_Bayes_klaR.md
+++ b/man/rmd/naive_Bayes_klaR.md
@@ -18,7 +18,7 @@ Note that `usekernel` is always set to `TRUE` for the `klaR` engine.
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **discrim**.
+The **discrim** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/naive_Bayes_naivebayes.md b/man/rmd/naive_Bayes_naivebayes.md
index d01c32b24..41818ed2b 100644
--- a/man/rmd/naive_Bayes_naivebayes.md
+++ b/man/rmd/naive_Bayes_naivebayes.md
@@ -16,7 +16,7 @@ This model has 2 tuning parameter:
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **discrim**.
+The **discrim** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/nearest_neighbor_kknn.md b/man/rmd/nearest_neighbor_kknn.md
index d1f594265..f39c597bd 100644
--- a/man/rmd/nearest_neighbor_kknn.md
+++ b/man/rmd/nearest_neighbor_kknn.md
@@ -78,7 +78,7 @@ nearest_neighbor(
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/pls_mixOmics.md b/man/rmd/pls_mixOmics.md
index 9f678cfca..c38c0ea26 100644
--- a/man/rmd/pls_mixOmics.md
+++ b/man/rmd/pls_mixOmics.md
@@ -16,7 +16,7 @@ This model has 2 tuning parameters:
 
 ## Translation from parsnip to the underlying model call  (regression)
 
-There is a parsnip extension package required to fit this model to this mode: **plsmod**.
+The **plsmod** extension package is required to fit this model.
 
 
 ```r
@@ -51,7 +51,7 @@ pls(num_comp = integer(1), predictor_prop = double(1)) %>%
 
 ## Translation from parsnip to the underlying model call  (classification)
 
-There is a parsnip extension package required to fit this model to this mode: **plsmod**.
+The **plsmod** extension package is required to fit this model.
 
 
 ```r
@@ -82,7 +82,7 @@ In this case, [plsmod::pls_fit()] has the same role as above but eventually targ
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Variance calculations are used in these computations so _zero-variance_ predictors (i.e., with a single unique value) should be eliminated before fitting the model. 
diff --git a/man/rmd/poisson_reg_gee.Rmd b/man/rmd/poisson_reg_gee.Rmd
index 0c80fcd17..393c43e7b 100644
--- a/man/rmd/poisson_reg_gee.Rmd
+++ b/man/rmd/poisson_reg_gee.Rmd
@@ -63,8 +63,8 @@ gee_wflow <-
 
 fit(gee_wflow, data = longitudinal_counts)
 ```
-
-`gee()` always prints out warnings and output even when `silent = TRUE`. When using the `gee` engine, it will never produce output, even if `silent = FALSE`.
+```{r child = "template-gee-silent.Rmd"}
+```
 
 Also, because of issues with the `gee()` function, a supplementary call to `glm()` is needed to get the rank and QR decomposition objects so that `predict()` can be used.
 
diff --git a/man/rmd/poisson_reg_gee.md b/man/rmd/poisson_reg_gee.md
index ca4f79028..5a37ca8b3 100644
--- a/man/rmd/poisson_reg_gee.md
+++ b/man/rmd/poisson_reg_gee.md
@@ -9,7 +9,7 @@ This model has no formal tuning parameters. It may be beneficial to determine th
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **multilevelmod**.
+The **multilevelmod** extension package is required to fit this model.
 
 
 ```r
@@ -75,7 +75,7 @@ gee_wflow <-
 fit(gee_wflow, data = longitudinal_counts)
 ```
 
-`gee()` always prints out warnings and output even when `silent = TRUE`. When using the `gee` engine, it will never produce output, even if `silent = FALSE`.
+The `gee::gee()` function always prints out warnings and output even when `silent = TRUE`. The parsnip `"gee"` engine, by contrast, silences all console output coming from `gee::gee()`, even if `silent = FALSE`.
 
 Also, because of issues with the `gee()` function, a supplementary call to `glm()` is needed to get the rank and QR decomposition objects so that `predict()` can be used.
 
diff --git a/man/rmd/poisson_reg_glm.md b/man/rmd/poisson_reg_glm.md
index 2c96860c1..0473be15f 100644
--- a/man/rmd/poisson_reg_glm.md
+++ b/man/rmd/poisson_reg_glm.md
@@ -9,7 +9,7 @@ This engine has no tuning parameters.
 
 ## Translation from parsnip to the underlying model call  (regression)
 
-There is a parsnip extension package required to fit this model to this mode: **poissonreg**.
+The **poissonreg** extension package is required to fit this model.
 
 
 ```r
@@ -33,6 +33,6 @@ poisson_reg() %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
diff --git a/man/rmd/poisson_reg_glmer.md b/man/rmd/poisson_reg_glmer.md
index 7f87c57b0..6a23a873f 100644
--- a/man/rmd/poisson_reg_glmer.md
+++ b/man/rmd/poisson_reg_glmer.md
@@ -9,7 +9,7 @@ This model has no tuning parameters.
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **multilevelmod**.
+The **multilevelmod** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/poisson_reg_glmnet.md b/man/rmd/poisson_reg_glmnet.md
index ec0f4daf0..8119746ab 100644
--- a/man/rmd/poisson_reg_glmnet.md
+++ b/man/rmd/poisson_reg_glmnet.md
@@ -19,7 +19,7 @@ The `penalty` parameter has no default and requires a single numeric value. For
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **poissonreg**.
+The **poissonreg** extension package is required to fit this model.
 
 
 ```r
@@ -47,7 +47,7 @@ poisson_reg(penalty = double(1), mixture = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/poisson_reg_hurdle.md b/man/rmd/poisson_reg_hurdle.md
index f16bb7f91..25c9fde6f 100644
--- a/man/rmd/poisson_reg_hurdle.md
+++ b/man/rmd/poisson_reg_hurdle.md
@@ -9,7 +9,7 @@ This engine has no tuning parameters.
 
 ## Translation from parsnip to the underlying model call  (regression)
 
-There is a parsnip extension package required to fit this model to this mode: **poissonreg**.
+The **poissonreg** extension package is required to fit this model.
 
 
 ```r
@@ -32,7 +32,7 @@ poisson_reg() %>%
 ## Preprocessing and special formulas for zero-inflated Poisson models
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 For this particular model, a special formula is used to specify which columns affect the counts and which affect the model for the probability of zero counts. These sets of terms are separated by a bar. For example, `y ~ x | z`. This type of formula is not used by the base R infrastructure (e.g. `model.matrix()`)
 
diff --git a/man/rmd/poisson_reg_stan.md b/man/rmd/poisson_reg_stan.md
index 941a0bbef..d4542bd53 100644
--- a/man/rmd/poisson_reg_stan.md
+++ b/man/rmd/poisson_reg_stan.md
@@ -22,7 +22,7 @@ See [rstan::sampling()] and [rstanarm::priors()] for more information on these a
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **poissonreg**.
+The **poissonreg** extension package is required to fit this model.
 
 
 ```r
@@ -48,7 +48,7 @@ Note that the `refresh` default prevents logging of the estimation process. Chan
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 ## Other details
 
diff --git a/man/rmd/poisson_reg_stan_glmer.md b/man/rmd/poisson_reg_stan_glmer.md
index 14f544f90..b1724addf 100644
--- a/man/rmd/poisson_reg_stan_glmer.md
+++ b/man/rmd/poisson_reg_stan_glmer.md
@@ -22,7 +22,7 @@ See `?rstanarm::stan_glmer` and `?rstan::sampling` for more information.
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **multilevelmod**.
+The **multilevelmod** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/poisson_reg_zeroinfl.md b/man/rmd/poisson_reg_zeroinfl.md
index f285745da..43b6b2281 100644
--- a/man/rmd/poisson_reg_zeroinfl.md
+++ b/man/rmd/poisson_reg_zeroinfl.md
@@ -9,7 +9,7 @@ This engine has no tuning parameters.
 
 ## Translation from parsnip to the underlying model call  (regression)
 
-There is a parsnip extension package required to fit this model to this mode: **poissonreg**.
+The **poissonreg** extension package is required to fit this model.
 
 
 ```r
@@ -33,7 +33,7 @@ poisson_reg() %>%
 ## Preprocessing and special formulas for zero-inflated Poisson models
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 For this particular model, a special formula is used to specify which columns affect the counts and which affect the model for the probability of zero counts. These sets of terms are separated by a bar. For example, `y ~ x | z`. This type of formula is not used by the base R infrastructure (e.g. `model.matrix()`)
 
diff --git a/man/rmd/proportional_hazards_glmnet.md b/man/rmd/proportional_hazards_glmnet.md
index 51309af27..ac3c33974 100644
--- a/man/rmd/proportional_hazards_glmnet.md
+++ b/man/rmd/proportional_hazards_glmnet.md
@@ -19,7 +19,7 @@ The `penalty` parameter has no default and requires a single numeric value. For
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **censored**.
+The **censored** extension package is required to fit this model.
 
 
 ```r
@@ -47,7 +47,7 @@ proportional_hazards(penalty = double(1), mixture = double(1)) %>%
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/proportional_hazards_survival.md b/man/rmd/proportional_hazards_survival.md
index c3f81d09e..6c2ad17e0 100644
--- a/man/rmd/proportional_hazards_survival.md
+++ b/man/rmd/proportional_hazards_survival.md
@@ -9,7 +9,7 @@ This model has no tuning parameters.
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **censored**.
+The **censored** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/rand_forest_party.md b/man/rmd/rand_forest_party.md
index 4e1582836..227641b17 100644
--- a/man/rmd/rand_forest_party.md
+++ b/man/rmd/rand_forest_party.md
@@ -17,7 +17,7 @@ This model has 3 tuning parameters:
 
 ## Translation from parsnip to the original package (censored regression)
 
-There is a parsnip extension package required to fit this model to this mode: **censored**.
+The **censored** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/rule_fit_xrf.md b/man/rmd/rule_fit_xrf.md
index 04582d83f..005106f30 100644
--- a/man/rmd/rule_fit_xrf.md
+++ b/man/rmd/rule_fit_xrf.md
@@ -28,7 +28,7 @@ This model has 8 tuning parameters:
 
 ## Translation from parsnip to the underlying model call  (regression)
 
-There is a parsnip extension package required to fit this model to this mode: **rules**.
+The **rules** extension package is required to fit this model.
 
 
 ```r
@@ -73,7 +73,7 @@ rule_fit(
 
 ## Translation from parsnip to the underlying model call  (classification)
 
-There is a parsnip extension package required to fit this model to this mode: **rules**.
+The **rules** extension package is required to fit this model.
 
 
 
@@ -135,7 +135,7 @@ These differences will create a disparity in the values of the `penalty` argumen
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 ## References
 
diff --git a/man/rmd/survival_reg_flexsurv.md b/man/rmd/survival_reg_flexsurv.md
index 924772b4b..51bf3f9b9 100644
--- a/man/rmd/survival_reg_flexsurv.md
+++ b/man/rmd/survival_reg_flexsurv.md
@@ -13,7 +13,7 @@ This model has 1 tuning parameters:
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **censored**.
+The **censored** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/survival_reg_survival.md b/man/rmd/survival_reg_survival.md
index 9a8132c10..26b78030f 100644
--- a/man/rmd/survival_reg_survival.md
+++ b/man/rmd/survival_reg_survival.md
@@ -13,7 +13,7 @@ This model has 1 tuning parameters:
 
 ## Translation from parsnip to the original package
 
-There is a parsnip extension package required to fit this model to this mode: **censored**.
+The **censored** extension package is required to fit this model.
 
 
 ```r
diff --git a/man/rmd/svm_linear_LiblineaR.md b/man/rmd/svm_linear_LiblineaR.md
index d55d9a1c4..059744312 100644
--- a/man/rmd/svm_linear_LiblineaR.md
+++ b/man/rmd/svm_linear_LiblineaR.md
@@ -74,7 +74,7 @@ Note that the `LiblineaR` engine does not produce class probabilities. When opti
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/svm_linear_kernlab.md b/man/rmd/svm_linear_kernlab.md
index 16f474b0b..399e80d93 100644
--- a/man/rmd/svm_linear_kernlab.md
+++ b/man/rmd/svm_linear_kernlab.md
@@ -72,7 +72,7 @@ Note that the `"kernlab"` engine does not naturally estimate class probabilities
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/svm_poly_kernlab.md b/man/rmd/svm_poly_kernlab.md
index 31e87e4be..1bdf9d6b2 100644
--- a/man/rmd/svm_poly_kernlab.md
+++ b/man/rmd/svm_poly_kernlab.md
@@ -86,7 +86,7 @@ Note that the `"kernlab"` engine does not naturally estimate class probabilities
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/svm_rbf_kernlab.md b/man/rmd/svm_rbf_kernlab.md
index 4610a0e8d..8eaa338b1 100644
--- a/man/rmd/svm_rbf_kernlab.md
+++ b/man/rmd/svm_rbf_kernlab.md
@@ -80,7 +80,7 @@ Note that the `"kernlab"` engine does not naturally estimate class probabilities
 ## Preprocessing requirements
 
 
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 
 Predictors should have the same scale. One way to achieve this is to center and 
diff --git a/man/rmd/template-gee-silent.Rmd b/man/rmd/template-gee-silent.Rmd
new file mode 100644
index 000000000..a66b4ea79
--- /dev/null
+++ b/man/rmd/template-gee-silent.Rmd
@@ -0,0 +1 @@
+The `gee::gee()` function always prints out warnings and output even when `silent = TRUE`. The parsnip `"gee"` engine, by contrast, silences all console output coming from `gee::gee()`, even if `silent = FALSE`.
diff --git a/man/rmd/template-makes-dummies.Rmd b/man/rmd/template-makes-dummies.Rmd
index bbca2037f..24c496237 100644
--- a/man/rmd/template-makes-dummies.Rmd
+++ b/man/rmd/template-makes-dummies.Rmd
@@ -1 +1 @@
-Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit.model_spec()}}, parsnip will convert factor columns to indicators.
+Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.