From 101a61c612d7cf19f4bdd055bd47a973600c5cb4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=98topepo=E2=80=99?= <mxkuhn@gmail.com>
Date: Sun, 30 Nov 2025 17:08:15 -0500
Subject: [PATCH 1/8] doc updates

---
 man/details_bart_dbarts.Rd                 |  7 ++++--
 man/details_boost_tree_h2o.Rd              |  6 +++--
 man/details_boost_tree_xgboost.Rd          |  5 +++-
 man/details_decision_tree_partykit.Rd      | 16 ++++++-------
 man/details_proportional_hazards_glmnet.Rd | 17 ++++++++++++++
 man/details_rand_forest_aorsf.Rd           | 24 +++++++++----------
 man/details_rand_forest_partykit.Rd        | 16 ++++++-------
 man/details_svm_rbf_kernlab.Rd             | 24 +++++++++++--------
 man/rmd/decision_tree_partykit.md          | 16 ++++++-------
 man/rmd/rand_forest_aorsf.Rmd              |  3 ++-
 man/rmd/rand_forest_aorsf.md               | 27 +++++++++++-----------
 man/rmd/rand_forest_partykit.md            | 16 ++++++-------
 vignettes/parsnip.Rmd                      |  2 +-
 13 files changed, 105 insertions(+), 74 deletions(-)

diff --git a/man/details_bart_dbarts.Rd b/man/details_bart_dbarts.Rd
index 352bc0467..49a2c5685 100644
--- a/man/details_bart_dbarts.Rd
+++ b/man/details_bart_dbarts.Rd
@@ -124,7 +124,8 @@ indicators if the user does not create them first.
 \subsection{Prediction types}{
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{parsnip:::get_from_env("bart_predict") |>
-  dplyr::select(mode, type)
+  dplyr::select(mode, type) |> 
+  print(n = Inf)
 }\if{html}{\out{</div>}}
 
 \if{html}{\out{<div class="sourceCode">}}\preformatted{## # A tibble: 9 x 2
@@ -136,7 +137,9 @@ indicators if the user does not create them first.
 ## 4 regression     pred_int
 ## 5 classification class   
 ## 6 classification prob    
-## # i 3 more rows
+## 7 classification conf_int
+## 8 classification pred_int
+## 9 classification raw
 }\if{html}{\out{</div>}}
 }
 
diff --git a/man/details_boost_tree_h2o.Rd b/man/details_boost_tree_h2o.Rd
index 94ad47e74..801067b33 100644
--- a/man/details_boost_tree_h2o.Rd
+++ b/man/details_boost_tree_h2o.Rd
@@ -160,7 +160,8 @@ within \verb{[0, 1]}.
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{parsnip:::get_from_env("boost_tree_predict") |>
   dplyr::filter(stringr::str_starts(engine, "h2o")) |>
-  dplyr::select(mode, type)
+  dplyr::select(mode, type) |> 
+  print(n = Inf)
 }\if{html}{\out{</div>}}
 
 \if{html}{\out{<div class="sourceCode">}}\preformatted{## # A tibble: 8 x 2
@@ -172,7 +173,8 @@ within \verb{[0, 1]}.
 ## 4 classification prob   
 ## 5 regression     numeric
 ## 6 regression     raw    
-## # i 2 more rows
+## 7 classification class  
+## 8 classification prob
 }\if{html}{\out{</div>}}
 }
 
diff --git a/man/details_boost_tree_xgboost.Rd b/man/details_boost_tree_xgboost.Rd
index dca1b8843..f68aa83f4 100644
--- a/man/details_boost_tree_xgboost.Rd
+++ b/man/details_boost_tree_xgboost.Rd
@@ -9,7 +9,10 @@ ensemble. Each tree depends on the results of previous trees. All trees in
 the ensemble are combined to produce a final prediction.
 }
 \details{
-For this engine, there are multiple modes: classification and regression
+For this engine, there are multiple modes: classification and
+regression. Note that in late 2025, a new version of xgboost was
+released with differences in its interface and model objects. This
+version of parsnip should work with either version.
 \subsection{Tuning Parameters}{
 
 This model has 8 tuning parameters:
diff --git a/man/details_decision_tree_partykit.Rd b/man/details_decision_tree_partykit.Rd
index 0d0ef6027..279df8fec 100644
--- a/man/details_decision_tree_partykit.Rd
+++ b/man/details_decision_tree_partykit.Rd
@@ -8,14 +8,14 @@
 tree-based structure using hypothesis testing methods.
 }
 \details{
-For this engine, there are multiple modes: censored regression,
-regression, and classification
+For this engine, there are multiple modes: regression, classification,
+and censored regression
 \subsection{Tuning Parameters}{
 
 This model has 2 tuning parameters:
 \itemize{
-\item \code{tree_depth}: Tree Depth (type: integer, default: see below)
 \item \code{min_n}: Minimal Node Size (type: integer, default: 20L)
+\item \code{tree_depth}: Tree Depth (type: integer, default: see below)
 }
 
 The \code{tree_depth} parameter defaults to \code{0} which means no restrictions
@@ -135,11 +135,11 @@ are not required for this model.
 \if{html}{\out{<div class="sourceCode">}}\preformatted{## # A tibble: 5 x 2
 ##   mode                type    
 ##   <chr>               <chr>   
-## 1 censored regression time    
-## 2 censored regression survival
-## 3 regression          numeric 
-## 4 classification      class   
-## 5 classification      prob
+## 1 regression          numeric 
+## 2 classification      class   
+## 3 classification      prob    
+## 4 censored regression time    
+## 5 censored regression survival
 }\if{html}{\out{</div>}}
 }
 
diff --git a/man/details_proportional_hazards_glmnet.Rd b/man/details_proportional_hazards_glmnet.Rd
index 3322a7c0c..7dcd2ff1a 100644
--- a/man/details_proportional_hazards_glmnet.Rd
+++ b/man/details_proportional_hazards_glmnet.Rd
@@ -154,6 +154,23 @@ predictions. When saving the model for the purpose of prediction, the
 size of the saved object might be substantially reduced by using
 functions from the \href{https://butcher.tidymodels.org}{butcher} package.
 }
+
+\subsection{Prediction types}{
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{parsnip:::get_from_env("proportional_hazards_predict") |>
+  dplyr::filter(engine == "glmnet") |>
+  dplyr::select(mode, type)
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## # A tibble: 4 x 2
+##   mode                type       
+##   <chr>               <chr>      
+## 1 censored regression linear_pred
+## 2 censored regression survival   
+## 3 censored regression time       
+## 4 censored regression raw
+}\if{html}{\out{</div>}}
+}
 }
 \section{References}{
 \itemize{
diff --git a/man/details_rand_forest_aorsf.Rd b/man/details_rand_forest_aorsf.Rd
index 6ccb4d7d6..c263f25ad 100644
--- a/man/details_rand_forest_aorsf.Rd
+++ b/man/details_rand_forest_aorsf.Rd
@@ -9,16 +9,16 @@ trees, each de-correlated from the others. The final prediction uses all
 predictions from the individual trees and combines them.
 }
 \details{
-For this engine, there are multiple modes: censored regression,
-classification, and regression
+For this engine, there are multiple modes: classification, regression,
+and censored regression
 \subsection{Tuning Parameters}{
 
 This model has 3 tuning parameters:
 \itemize{
-\item \code{trees}: # Trees (type: integer, default: 500L)
-\item \code{min_n}: Minimal Node Size (type: integer, default: 5L)
 \item \code{mtry}: # Randomly Selected Predictors (type: integer, default:
 ceiling(sqrt(n_predictors)))
+\item \code{trees}: # Trees (type: integer, default: 500L)
+\item \code{min_n}: Minimal Node Size (type: integer, default: 5L)
 }
 
 Additionally, this model has one engine-specific tuning parameter:
@@ -123,14 +123,14 @@ The \code{fit()} and \code{fit_xy()} arguments have arguments called
 }\if{html}{\out{</div>}}
 
 \if{html}{\out{<div class="sourceCode">}}\preformatted{## # A tibble: 7 x 2
-##   mode                type    
-##   <chr>               <chr>   
-## 1 censored regression time    
-## 2 censored regression survival
-## 3 classification      class   
-## 4 classification      prob    
-## 5 classification      raw     
-## 6 regression          numeric 
+##   mode                type   
+##   <chr>               <chr>  
+## 1 classification      class  
+## 2 classification      prob   
+## 3 classification      raw    
+## 4 regression          numeric
+## 5 regression          raw    
+## 6 censored regression time   
 ## # i 1 more row
 }\if{html}{\out{</div>}}
 }
diff --git a/man/details_rand_forest_partykit.Rd b/man/details_rand_forest_partykit.Rd
index 4af378e97..a6f392a53 100644
--- a/man/details_rand_forest_partykit.Rd
+++ b/man/details_rand_forest_partykit.Rd
@@ -9,15 +9,15 @@ trees, each independent of the others. The final prediction uses all
 predictions from the individual trees and combines them.
 }
 \details{
-For this engine, there are multiple modes: censored regression,
-regression, and classification
+For this engine, there are multiple modes: regression, classification,
+and censored regression
 \subsection{Tuning Parameters}{
 
 This model has 3 tuning parameters:
 \itemize{
-\item \code{trees}: # Trees (type: integer, default: 500L)
 \item \code{min_n}: Minimal Node Size (type: integer, default: 20L)
 \item \code{mtry}: # Randomly Selected Predictors (type: integer, default: 5L)
+\item \code{trees}: # Trees (type: integer, default: 500L)
 }
 }
 
@@ -110,11 +110,11 @@ are not required for this model.
 \if{html}{\out{<div class="sourceCode">}}\preformatted{## # A tibble: 5 x 2
 ##   mode                type    
 ##   <chr>               <chr>   
-## 1 censored regression time    
-## 2 censored regression survival
-## 3 regression          numeric 
-## 4 classification      class   
-## 5 classification      prob
+## 1 regression          numeric 
+## 2 classification      class   
+## 3 classification      prob    
+## 4 censored regression time    
+## 5 censored regression survival
 }\if{html}{\out{</div>}}
 }
 
diff --git a/man/details_svm_rbf_kernlab.Rd b/man/details_svm_rbf_kernlab.Rd
index 897538684..5b9946557 100644
--- a/man/details_svm_rbf_kernlab.Rd
+++ b/man/details_svm_rbf_kernlab.Rd
@@ -111,19 +111,23 @@ The underlying model implementation does not allow for case weights.
 \subsection{Prediction types}{
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{parsnip:::get_from_env("svm_rbf_predict") |>
-  dplyr::select(mode, type)
+  dplyr::select(mode, type) |> 
+  print(n = Inf)
 }\if{html}{\out{</div>}}
 
 \if{html}{\out{<div class="sourceCode">}}\preformatted{## # A tibble: 10 x 2
-##   mode           type   
-##   <chr>          <chr>  
-## 1 regression     numeric
-## 2 regression     raw    
-## 3 classification class  
-## 4 classification prob   
-## 5 classification raw    
-## 6 regression     numeric
-## # i 4 more rows
+##    mode           type   
+##    <chr>          <chr>  
+##  1 regression     numeric
+##  2 regression     raw    
+##  3 classification class  
+##  4 classification prob   
+##  5 classification raw    
+##  6 regression     numeric
+##  7 regression     raw    
+##  8 classification class  
+##  9 classification prob   
+## 10 classification raw
 }\if{html}{\out{</div>}}
 }
 
diff --git a/man/rmd/decision_tree_partykit.md b/man/rmd/decision_tree_partykit.md
index 9d712e4e2..2cce9c12d 100644
--- a/man/rmd/decision_tree_partykit.md
+++ b/man/rmd/decision_tree_partykit.md
@@ -1,7 +1,7 @@
 
 
 
-For this engine, there are multiple modes: regression, classification, and censored regression
+For this engine, there are multiple modes: censored regression, regression, and classification
 
 ## Tuning Parameters
 
@@ -9,10 +9,10 @@ For this engine, there are multiple modes: regression, classification, and censo
 
 This model has 2 tuning parameters:
 
-- `min_n`: Minimal Node Size (type: integer, default: 20L)
-
 - `tree_depth`: Tree Depth (type: integer, default: see below)
 
+- `min_n`: Minimal Node Size (type: integer, default: 20L)
+
 The `tree_depth` parameter defaults to `0` which means no restrictions are applied to tree depth.
 
 An engine-specific parameter for this model is: 
@@ -128,11 +128,11 @@ parsnip:::get_from_env("decision_tree_predict") |>
 ## # A tibble: 5 x 2
 ##   mode                type    
 ##   <chr>               <chr>   
-## 1 regression          numeric 
-## 2 classification      class   
-## 3 classification      prob    
-## 4 censored regression time    
-## 5 censored regression survival
+## 1 censored regression time    
+## 2 censored regression survival
+## 3 regression          numeric 
+## 4 classification      class   
+## 5 classification      prob
 ```
 
 ## Other details
diff --git a/man/rmd/rand_forest_aorsf.Rmd b/man/rmd/rand_forest_aorsf.Rmd
index 349a31f68..42f5a0276 100644
--- a/man/rmd/rand_forest_aorsf.Rmd
+++ b/man/rmd/rand_forest_aorsf.Rmd
@@ -96,7 +96,8 @@ rand_forest() |>
 
 parsnip:::get_from_env("rand_forest_predict") |>
   dplyr::filter(engine == "aorsf") |>
-  dplyr::select(mode, type)
+  dplyr::select(mode, type)|> 
+  print(n = Inf)
 
 ```
 
diff --git a/man/rmd/rand_forest_aorsf.md b/man/rmd/rand_forest_aorsf.md
index 2c8a53b4f..7773a2d55 100644
--- a/man/rmd/rand_forest_aorsf.md
+++ b/man/rmd/rand_forest_aorsf.md
@@ -1,7 +1,7 @@
 
 
 
-For this engine, there are multiple modes: classification, regression, and censored regression
+For this engine, there are multiple modes: censored regression, classification, and regression
 
 ## Tuning Parameters
 
@@ -9,12 +9,12 @@ For this engine, there are multiple modes: classification, regression, and censo
 
 This model has 3 tuning parameters:
 
-- `mtry`: # Randomly Selected Predictors (type: integer, default: ceiling(sqrt(n_predictors)))
-
 - `trees`: # Trees (type: integer, default: 500L)
 
 - `min_n`: Minimal Node Size (type: integer, default: 5L)
 
+- `mtry`: # Randomly Selected Predictors (type: integer, default: ceiling(sqrt(n_predictors)))
+
 Additionally, this model has one engine-specific tuning parameter:
 
  * `split_min_stat`: Minimum test statistic required to split a node. Defaults are `3.841459` for censored regression (which is roughly a p-value of 0.05) and `0` for classification and regression. For classification, this tuning parameter should be between 0 and 1, and for regression it should be greater than or equal to 0. Higher values of this parameter cause trees grown by `aorsf` to have less depth.
@@ -108,20 +108,21 @@ The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that e
 ``` r
 parsnip:::get_from_env("rand_forest_predict") |>
   dplyr::filter(engine == "aorsf") |>
-  dplyr::select(mode, type)
+  dplyr::select(mode, type)|> 
+  print(n = Inf)
 ```
 
 ```
 ## # A tibble: 7 x 2
-##   mode                type   
-##   <chr>               <chr>  
-## 1 classification      class  
-## 2 classification      prob   
-## 3 classification      raw    
-## 4 regression          numeric
-## 5 regression          raw    
-## 6 censored regression time   
-## # i 1 more row
+##   mode                type    
+##   <chr>               <chr>   
+## 1 censored regression time    
+## 2 censored regression survival
+## 3 classification      class   
+## 4 classification      prob    
+## 5 classification      raw     
+## 6 regression          numeric 
+## 7 regression          raw
 ```
 
 ## Other details
diff --git a/man/rmd/rand_forest_partykit.md b/man/rmd/rand_forest_partykit.md
index a5e76e1b8..35b9db9be 100644
--- a/man/rmd/rand_forest_partykit.md
+++ b/man/rmd/rand_forest_partykit.md
@@ -1,7 +1,7 @@
 
 
 
-For this engine, there are multiple modes: regression, classification, and censored regression
+For this engine, there are multiple modes: censored regression, regression, and classification
 
 ## Tuning Parameters
 
@@ -9,12 +9,12 @@ For this engine, there are multiple modes: regression, classification, and censo
 
 This model has 3 tuning parameters:
 
+- `trees`: # Trees (type: integer, default: 500L)
+
 - `min_n`: Minimal Node Size (type: integer, default: 20L)
 
 - `mtry`: # Randomly Selected Predictors (type: integer, default: 5L)
 
-- `trees`: # Trees (type: integer, default: 500L)
-
 ## Translation from parsnip to the original package (regression)
 
 The **bonsai** extension package is required to fit this model.
@@ -110,11 +110,11 @@ parsnip:::get_from_env("rand_forest_predict") |>
 ## # A tibble: 5 x 2
 ##   mode                type    
 ##   <chr>               <chr>   
-## 1 regression          numeric 
-## 2 classification      class   
-## 3 classification      prob    
-## 4 censored regression time    
-## 5 censored regression survival
+## 1 censored regression time    
+## 2 censored regression survival
+## 3 regression          numeric 
+## 4 classification      class   
+## 5 classification      prob
 ```
 
 ## Other details
diff --git a/vignettes/parsnip.Rmd b/vignettes/parsnip.Rmd
index a2474bceb..3718eec1a 100644
--- a/vignettes/parsnip.Rmd
+++ b/vignettes/parsnip.Rmd
@@ -159,7 +159,7 @@ rf_with_seed |>
 
 Note that the call objects show `num.trees = ~2000`. The tilde is the consequence of `parsnip` using [quosures](https://adv-r.hadley.nz/evaluation.html#quosures) to process the model specification's arguments. 
 
-Normally, when a function is executed, the function's arguments are immediately evaluated. In the case of parsnip, the model specification's arguments are _not_; the [expression is captured](https://www.tidyverse.org/blog/2019/04/parsnip-internals/) along with the environment where it should be evaluated. That is what a quosure does. 
+Normally, when a function is executed, the function's arguments are immediately evaluated. In the case of parsnip, the model specification's arguments are _not_; the [expression is captured](https://tidyverse.org/blog/2019/04/parsnip-internals/) along with the environment where it should be evaluated. That is what a quosure does. 
 
 parsnip uses these expressions to make a model fit call that is evaluated. The tilde in the call above reflects that the argument was captured using a quosure. 
 

From 1f1835b0941a896ed177e3a6f44d3c435fb9516a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=98topepo=E2=80=99?= <mxkuhn@gmail.com>
Date: Sun, 30 Nov 2025 17:09:19 -0500
Subject: [PATCH 2/8] verison bumps

---
 DESCRIPTION | 2 +-
 NEWS.md     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 74f62fa96..44d20ca1a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
-Version: 1.3.3.9001
+Version: 1.4.0
 Authors@R: c(
     person("Max", "Kuhn", , "max@posit.co", role = c("aut", "cre")),
     person("Davis", "Vaughan", , "davis@posit.co", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index d52d6950b..b88d5eee0 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,4 @@
-# parsnip (development version)
+# parsnip 1.4.0
 
 * Fixes issue with running predictions for Decision Trees in Spark (#1309)
 

From f6f124039769e2e82a0807993e26d319ac176ff8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=98topepo=E2=80=99?= <mxkuhn@gmail.com>
Date: Sun, 30 Nov 2025 17:09:43 -0500
Subject: [PATCH 3/8] updated tsv

---
 inst/models.tsv | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/inst/models.tsv b/inst/models.tsv
index 2c39fff0c..a1ab034e7 100644
--- a/inst/models.tsv
+++ b/inst/models.tsv
@@ -14,11 +14,13 @@
 "bart"	"regression"	"dbarts"	NA
 "boost_tree"	"censored regression"	"mboost"	"censored"
 "boost_tree"	"classification"	"C5.0"	NA
+"boost_tree"	"classification"	"catboost"	"bonsai"
 "boost_tree"	"classification"	"h2o"	"agua"
 "boost_tree"	"classification"	"h2o_gbm"	"agua"
 "boost_tree"	"classification"	"lightgbm"	"bonsai"
 "boost_tree"	"classification"	"spark"	NA
 "boost_tree"	"classification"	"xgboost"	NA
+"boost_tree"	"regression"	"catboost"	"bonsai"
 "boost_tree"	"regression"	"h2o"	"agua"
 "boost_tree"	"regression"	"h2o_gbm"	"agua"
 "boost_tree"	"regression"	"lightgbm"	"bonsai"

From b34bdeab64a21e56b6911f2eeb7f1e9e841fdecd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=98topepo=E2=80=99?= <mxkuhn@gmail.com>
Date: Sun, 30 Nov 2025 17:15:43 -0500
Subject: [PATCH 4/8] more doc updates

---
 man/details_decision_tree_partykit.Rd | 16 +++++++--------
 man/details_rand_forest_aorsf.Rd      | 29 ++++++++++++++-------------
 man/details_rand_forest_partykit.Rd   | 16 +++++++--------
 3 files changed, 31 insertions(+), 30 deletions(-)

diff --git a/man/details_decision_tree_partykit.Rd b/man/details_decision_tree_partykit.Rd
index 279df8fec..0d0ef6027 100644
--- a/man/details_decision_tree_partykit.Rd
+++ b/man/details_decision_tree_partykit.Rd
@@ -8,14 +8,14 @@
 tree-based structure using hypothesis testing methods.
 }
 \details{
-For this engine, there are multiple modes: regression, classification,
-and censored regression
+For this engine, there are multiple modes: censored regression,
+regression, and classification
 \subsection{Tuning Parameters}{
 
 This model has 2 tuning parameters:
 \itemize{
-\item \code{min_n}: Minimal Node Size (type: integer, default: 20L)
 \item \code{tree_depth}: Tree Depth (type: integer, default: see below)
+\item \code{min_n}: Minimal Node Size (type: integer, default: 20L)
 }
 
 The \code{tree_depth} parameter defaults to \code{0} which means no restrictions
@@ -135,11 +135,11 @@ are not required for this model.
 \if{html}{\out{<div class="sourceCode">}}\preformatted{## # A tibble: 5 x 2
 ##   mode                type    
 ##   <chr>               <chr>   
-## 1 regression          numeric 
-## 2 classification      class   
-## 3 classification      prob    
-## 4 censored regression time    
-## 5 censored regression survival
+## 1 censored regression time    
+## 2 censored regression survival
+## 3 regression          numeric 
+## 4 classification      class   
+## 5 classification      prob
 }\if{html}{\out{</div>}}
 }
 
diff --git a/man/details_rand_forest_aorsf.Rd b/man/details_rand_forest_aorsf.Rd
index c263f25ad..8b2124cdc 100644
--- a/man/details_rand_forest_aorsf.Rd
+++ b/man/details_rand_forest_aorsf.Rd
@@ -9,16 +9,16 @@ trees, each de-correlated from the others. The final prediction uses all
 predictions from the individual trees and combines them.
 }
 \details{
-For this engine, there are multiple modes: classification, regression,
-and censored regression
+For this engine, there are multiple modes: censored regression,
+classification, and regression
 \subsection{Tuning Parameters}{
 
 This model has 3 tuning parameters:
 \itemize{
-\item \code{mtry}: # Randomly Selected Predictors (type: integer, default:
-ceiling(sqrt(n_predictors)))
 \item \code{trees}: # Trees (type: integer, default: 500L)
 \item \code{min_n}: Minimal Node Size (type: integer, default: 5L)
+\item \code{mtry}: # Randomly Selected Predictors (type: integer, default:
+ceiling(sqrt(n_predictors)))
 }
 
 Additionally, this model has one engine-specific tuning parameter:
@@ -119,19 +119,20 @@ The \code{fit()} and \code{fit_xy()} arguments have arguments called
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{parsnip:::get_from_env("rand_forest_predict") |>
   dplyr::filter(engine == "aorsf") |>
-  dplyr::select(mode, type)
+  dplyr::select(mode, type)|> 
+  print(n = Inf)
 }\if{html}{\out{</div>}}
 
 \if{html}{\out{<div class="sourceCode">}}\preformatted{## # A tibble: 7 x 2
-##   mode                type   
-##   <chr>               <chr>  
-## 1 classification      class  
-## 2 classification      prob   
-## 3 classification      raw    
-## 4 regression          numeric
-## 5 regression          raw    
-## 6 censored regression time   
-## # i 1 more row
+##   mode                type    
+##   <chr>               <chr>   
+## 1 censored regression time    
+## 2 censored regression survival
+## 3 classification      class   
+## 4 classification      prob    
+## 5 classification      raw     
+## 6 regression          numeric 
+## 7 regression          raw
 }\if{html}{\out{</div>}}
 }
 
diff --git a/man/details_rand_forest_partykit.Rd b/man/details_rand_forest_partykit.Rd
index a6f392a53..4af378e97 100644
--- a/man/details_rand_forest_partykit.Rd
+++ b/man/details_rand_forest_partykit.Rd
@@ -9,15 +9,15 @@ trees, each independent of the others. The final prediction uses all
 predictions from the individual trees and combines them.
 }
 \details{
-For this engine, there are multiple modes: regression, classification,
-and censored regression
+For this engine, there are multiple modes: censored regression,
+regression, and classification
 \subsection{Tuning Parameters}{
 
 This model has 3 tuning parameters:
 \itemize{
+\item \code{trees}: # Trees (type: integer, default: 500L)
 \item \code{min_n}: Minimal Node Size (type: integer, default: 20L)
 \item \code{mtry}: # Randomly Selected Predictors (type: integer, default: 5L)
-\item \code{trees}: # Trees (type: integer, default: 500L)
 }
 }
 
@@ -110,11 +110,11 @@ are not required for this model.
 \if{html}{\out{<div class="sourceCode">}}\preformatted{## # A tibble: 5 x 2
 ##   mode                type    
 ##   <chr>               <chr>   
-## 1 regression          numeric 
-## 2 classification      class   
-## 3 classification      prob    
-## 4 censored regression time    
-## 5 censored regression survival
+## 1 censored regression time    
+## 2 censored regression survival
+## 3 regression          numeric 
+## 4 classification      class   
+## 5 classification      prob
 }\if{html}{\out{</div>}}
 }
 

From db67f2beb59f5c316edb887e25c369492709c5fc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=98topepo=E2=80=99?= <mxkuhn@gmail.com>
Date: Sun, 30 Nov 2025 18:10:17 -0500
Subject: [PATCH 5/8] update parsnip add-in code and db

---
 data/model_db.rda              | Bin 2778 -> 3335 bytes
 inst/add-in/parsnip_model_db.R |  79 ++++++++++++++++++++++++---------
 2 files changed, 59 insertions(+), 20 deletions(-)

diff --git a/data/model_db.rda b/data/model_db.rda
index 8168b7e2265f9e150207ee5a4d16f6ae09319eb7..784b7dc06b87232a70a103a417f9e809b474e460 100644
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diff --git a/inst/add-in/parsnip_model_db.R b/inst/add-in/parsnip_model_db.R
index b3cf38b0b..c5bec0d66 100644
--- a/inst/add-in/parsnip_model_db.R
+++ b/inst/add-in/parsnip_model_db.R
@@ -9,28 +9,66 @@ library(usethis)
 # also requires installation of:
 packages <- c(
   "parsnip",
-  "discrim",
-  "plsmod",
-  "rules",
-  "baguette",
-  "poissonreg",
-  "multilevelmod",
-  "modeltime",
-  "modeltime.gluonts"
+  parsnip:::extensions(),
+  "modeltime"
+  # "modeltime.gluonts" # required python packages to create spec
 )
 
+loaded <- map(packages, library, character.only = TRUE)
+
 # ------------------------------------------------------------------------------
 
-# Detects model specifications via their print methods
-print_methods <- function(x) {
-  require(x, character.only = TRUE)
-  ns <- asNamespace(ns = x)
-  mthds <- ls(envir = ns, pattern = "^print\\.")
-  mthds <- gsub("^print\\.", "", mthds)
-  purrr::map(mthds, get_engines) |>
+get_model <- function(x) {
+  res <- get_from_env(x)
+  if (!is.null(res)) {
+    res <- dplyr::mutate(res, model = x)
+  }
+  res
+}
+
+get_packages <- function(x) {
+  res <- get_from_env(paste0(x, "_pkgs"))
+  if (is.null(res)) {
+    return(res)
+  }
+  res <-
+    res |>
+    tidyr::unnest(pkg) |>
+    dplyr::mutate(
+      model = x
+    )
+
+  res
+}
+
+get_models <- function() {
+  res <- ls(envir = get_model_env(), pattern = "_fit$")
+  models <- gsub("_fit$", "", res)
+  models <-
+    purrr::map(models, get_model) |>
+    purrr::list_rbind()
+
+  # get source package
+  pkgs <- gsub("_fit$", "_pkgs", res)
+  pkgs <-
+    unique(models$model) |>
+    purrr::map(get_packages) |>
     purrr::list_rbind() |>
-    dplyr::mutate(package = x)
+    dplyr::filter(pkg %in% packages)
+  dplyr::left_join(models, pkgs, by = dplyr::join_by(engine, mode, model)) |>
+    dplyr::rename(package = pkg) |>
+    dplyr::mutate(
+      package = dplyr::if_else(is.na(package), "parsnip", package),
+      call_from_parsnip = package %in% parsnip:::extensions(),
+      caller_package = dplyr::if_else(
+        call_from_parsnip,
+        "parsnip",
+        package
+      )
+    )
 }
+
+
 get_engines <- function(x) {
   eng <- try(parsnip::show_engines(x), silent = TRUE)
   if (inherits(eng, "try-error")) {
@@ -77,8 +115,8 @@ get_tunable_param <- function(mode, package, model, engine) {
 # ------------------------------------------------------------------------------
 
 model_db <-
-  purrr::map(packages, print_methods) |>
-  purrr::list_rbind() |>
+  get_models() |>
+  dplyr::filter(mode %in% c("regression", "classification")) |>
   dplyr::filter(engine != "liquidSVM") |>
   dplyr::filter(model != "surv_reg") |>
   dplyr::filter(engine != "spark") |>
@@ -98,9 +136,10 @@ model_db <-
   dplyr::left_join(model_db, num_modes, by = c("package", "model", "engine")) |>
   dplyr::mutate(
     parameters = purrr::pmap(
-      list(mode, package, model, engine),
+      list(mode, caller_package, model, engine),
       get_tunable_param
     )
-  )
+  ) |>
+  dplyr::select(-call_from_parsnip, -caller_package)
 
 usethis::use_data(model_db, overwrite = TRUE)

From 0e6cad5b63bca612aeedbd5a0d76fe98f013e83a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=98topepo=E2=80=99?= <mxkuhn@gmail.com>
Date: Mon, 1 Dec 2025 12:33:34 -0500
Subject: [PATCH 6/8] added ORCID

---
 DESCRIPTION | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 44d20ca1a..0e2119f76 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,8 @@ Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
 Version: 1.4.0
 Authors@R: c(
-    person("Max", "Kuhn", , "max@posit.co", role = c("aut", "cre")),
+    person("Max", "Kuhn", , "max@posit.co", role = c("cre", "aut"),
+           comment = c(ORCID = "0000-0003-2402-136X")),
     person("Davis", "Vaughan", , "davis@posit.co", role = "aut"),
     person("Emil", "Hvitfeldt", , "emil.hvitfeldt@posit.co", role = "ctb"),
     person("Posit Software, PBC", role = c("cph", "fnd"),

From e8a5314f7b81a0b6b694603daa6ebd9675b9ebce Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=98topepo=E2=80=99?= <mxkuhn@gmail.com>
Date: Mon, 1 Dec 2025 12:46:21 -0500
Subject: [PATCH 7/8] keras install in CI

---
 .github/workflows/R-CMD-check.yaml   |  7 +++----
 .github/workflows/test-coverage.yaml | 15 +++------------
 2 files changed, 6 insertions(+), 16 deletions(-)

diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index 150ed5e7a..135e8834f 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -67,11 +67,10 @@ jobs:
         with:
           python-version: 3.11
 
-      - name: Install TensorFlow
+      - name: Install TensorFlow/Keras
         run: |
-          reticulate::virtualenv_create('r-reticulate', python='3.11')
-          reticulate::use_virtualenv('r-reticulate')
-          tensorflow::install_tensorflow(version='2.16')
+          install.packages(c("keras3"))
+          keras3::install_keras()
         shell: Rscript {0}
 
       - uses: r-lib/actions/check-r-package@v2
diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
index 6e56ef9a4..b29fc83bf 100644
--- a/.github/workflows/test-coverage.yaml
+++ b/.github/workflows/test-coverage.yaml
@@ -27,19 +27,10 @@ jobs:
           extra-packages: any::covr, any::xml2
           needs: coverage
 
-      - name: Install dev reticulate
-        run: pak::pkg_install('rstudio/reticulate')
-        shell: Rscript {0}
-
-      - name: Install Miniconda
-        run: |
-          reticulate::install_miniconda()
-        shell: Rscript {0}
- 
-      - name: Install TensorFlow
+      - name: Install TensorFlow/Keras
         run: |
-          reticulate::conda_create('r-reticulate', packages = c('python==3.11'))
-          tensorflow::install_tensorflow(version='2.16')
+          install.packages(c("keras3"))
+          keras3::install_keras()
         shell: Rscript {0}
 
       - name: Test coverage

From aa11c691506ddd409b1671e79dae0a400fb3c84b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=98topepo=E2=80=99?= <mxkuhn@gmail.com>
Date: Mon, 1 Dec 2025 13:00:59 -0500
Subject: [PATCH 8/8] skip to next version

---
 tests/testthat/test-fit_interfaces.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/testthat/test-fit_interfaces.R b/tests/testthat/test-fit_interfaces.R
index 440ab9a12..31b084582 100644
--- a/tests/testthat/test-fit_interfaces.R
+++ b/tests/testthat/test-fit_interfaces.R
@@ -172,7 +172,7 @@ test_that("overhead of parsnip interface is minimal (#1071)", {
   skip_on_cran()
   skip_on_covr()
   skip_if_not_installed("bench")
-  skip_if_not_installed("parsnip", minimum_version = "1.4.0")
+  skip_if_not_installed("parsnip", minimum_version = "1.5.0")
 
   bm <- bench::mark(
     time_engine = lm(mpg ~ ., mtcars),