diff --git a/DESCRIPTION b/DESCRIPTION
index 9edc33a..5cf6bb1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -4,7 +4,7 @@ Version: 0.0.1
 Authors@R: 
     person("Luke", "Duttweiler", , "lduttweiler@hsph.harvard.edu", role = c("aut", "cre", 'cph'),
            comment = c(ORCID = "0000-0002-0467-995X"))
-Description: Implements a Bayesian hierarchical model designed to identify skips in mobiel menstrual cycle self-tracking on mobiel apps. Future developments will allow for the inclusion of covariates affecting cycle mean and regularity, as well as extra information regarding tracking non-adherence. Main methods to be outlined in a forthcoming paper, with alternative models from Li et al. (2022) <doi:10.1093/jamia/ocab182>.
+Description: Implements a Bayesian hierarchical model designed to identify skips in mobile menstrual cycle self-tracking on mobile apps. Future developments will allow for the inclusion of covariates affecting cycle mean and regularity, as well as extra information regarding tracking non-adherence. Main methods to be outlined in a forthcoming paper, with alternative models from Li et al. (2022) <doi:10.1093/jamia/ocab182>.
 License: MIT + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
diff --git a/R/diagnostics.R b/R/diagnostics.R
index 2072eb3..ce12a0a 100644
--- a/R/diagnostics.R
+++ b/R/diagnostics.R
@@ -28,8 +28,8 @@
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #'
 #' #Get diagnostics for cijs
 #' skipTrack.diagnostics(modFit, 'cijs')
diff --git a/R/fit.R b/R/fit.R
index 405e6dd..c4a0f44 100644
--- a/R/fit.R
+++ b/R/fit.R
@@ -17,8 +17,8 @@
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #' modFit
 #'
 skipTrack.fit <- function(Y,cluster,
diff --git a/R/results.R b/R/results.R
index 8191e7a..e34e553 100644
--- a/R/results.R
+++ b/R/results.R
@@ -18,17 +18,19 @@
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #' modFit
 #'
 #' # If using simulated data (which includes access to ground truth):
 #' #
-#' skipTrack.results(modFit, trueVals = simDat, burnIn = 250)
+#' skipTrack.results(modFit, trueVals = simDat, burnIn = 25)
+#' #Recommended burnIn with real data is at least 750
 #' #
 #' # If not using simulated data:
 #' #
-#  skipTrack.results(modFit, burnIn = 250)
+#' skipTrack.results(modFit, burnIn = 25)
+#' #Recommended burnIn with real data is at least 750
 #'
 #' @export
 skipTrack.results <- function(stFit, trueVals = NULL, burnIn = 750){
diff --git a/R/utils.R b/R/utils.R
index c65d284..ec9a22f 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -12,8 +12,8 @@
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #' plot(modFit)
 #'
 plot.skipTrack.model <- function(x, ...){
@@ -35,8 +35,8 @@ plot.skipTrack.model <- function(x, ...){
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #' print(modFit)
 #'
 #'
@@ -58,7 +58,8 @@ print.skipTrack.model <- function(x, ...){
 
 #' Report skipTrack.model structure to console
 #'
-#' @param x skipTrack.model object from the function skipTrack.fit
+#' @param object skipTrack.model object from the function skipTrack.fit
+#' @param ... To match other str calls
 #'
 #' @return Invisible NULL. Prints structure of skipTrack.model object
 #' @export
@@ -67,15 +68,15 @@ print.skipTrack.model <- function(x, ...){
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #' str(modFit)
 #'
 #'
-str.skipTrack.model <- function(x){
+str.skipTrack.model <- function(object, ...){
   cat(paste0('skipTrack.model S3 Object (also a list)\n\n',
-             'NChains:    ', length(x$fit), '\n',
-             'Model Type: ', x$model))
+             'NChains:    ', length(object$fit), '\n',
+             'Model Type: ', object$model))
   return(invisible())
 }
 
@@ -92,9 +93,9 @@ str.skipTrack.model <- function(x){
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
-#' summary(modFit, burnIn = 250)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
+#' summary(modFit, burnIn = 25) #Recommended burnIn with real data is at least 750
 #'
 #'
 summary.skipTrack.model <- function(object, ...){
diff --git a/R/visualization.R b/R/visualization.R
index d1263d0..3e75f57 100644
--- a/R/visualization.R
+++ b/R/visualization.R
@@ -20,8 +20,8 @@ if(getRversion() >= "2.15.1")  utils::globalVariables(c("ys", "cijs", 'taus',
 #' #Simulated data
 #' simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 #'
-#' #Run model fit
-#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#' #Run model fit (should typically run with much more than 50 reps)
+#' modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 #'
 #' #Visualize results
 #' skipTrack.visualize(modFit)
diff --git a/man/plot.skipTrack.model.Rd b/man/plot.skipTrack.model.Rd
index 4cf6e4f..156c742 100644
--- a/man/plot.skipTrack.model.Rd
+++ b/man/plot.skipTrack.model.Rd
@@ -21,8 +21,8 @@ Plot skipTrack.model objects
 #Simulated data
 simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 
-#Run model fit
-modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#Run model fit (should typically run with much more than 50 reps)
+modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 plot(modFit)
 
 }
diff --git a/man/print.skipTrack.model.Rd b/man/print.skipTrack.model.Rd
index 42011a4..ab25c75 100644
--- a/man/print.skipTrack.model.Rd
+++ b/man/print.skipTrack.model.Rd
@@ -21,8 +21,8 @@ Print skipTrack.model to console
 #Simulated data
 simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 
-#Run model fit
-modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#Run model fit (should typically run with much more than 50 reps)
+modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 print(modFit)
 
 
diff --git a/man/skipTrack-package.Rd b/man/skipTrack-package.Rd
index 9b9ff8b..abb002a 100644
--- a/man/skipTrack-package.Rd
+++ b/man/skipTrack-package.Rd
@@ -6,7 +6,7 @@
 \alias{skipTrack-package}
 \title{skipTrack: A Bayesian Hierarchical Model that Controls for Non-Adherence in Mobile Menstrual Cycle Tracking}
 \description{
-Implements a Bayesian hierarchical model designed to identify skips in mobiel menstrual cycle self-tracking on mobiel apps. Future developments will allow for the inclusion of covariates affecting cycle mean and regularity, as well as extra information regarding tracking non-adherence. Main methods to be outlined in a forthcoming paper, with alternative models from Li et al. (2022) \doi{10.1093/jamia/ocab182}.
+Implements a Bayesian hierarchical model designed to identify skips in mobile menstrual cycle self-tracking on mobile apps. Future developments will allow for the inclusion of covariates affecting cycle mean and regularity, as well as extra information regarding tracking non-adherence. Main methods to be outlined in a forthcoming paper, with alternative models from Li et al. (2022) \doi{10.1093/jamia/ocab182}.
 }
 \seealso{
 Useful links:
diff --git a/man/skipTrack.diagnostics.Rd b/man/skipTrack.diagnostics.Rd
index c7483ff..92e15f5 100644
--- a/man/skipTrack.diagnostics.Rd
+++ b/man/skipTrack.diagnostics.Rd
@@ -47,8 +47,8 @@ Details on the genDiagnostic function can be found in the genMCMCDiag package.
 #Simulated data
 simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 
-#Run model fit
-modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#Run model fit (should typically run with much more than 50 reps)
+modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 
 #Get diagnostics for cijs
 skipTrack.diagnostics(modFit, 'cijs')
diff --git a/man/skipTrack.fit.Rd b/man/skipTrack.fit.Rd
index 0d1279a..102cf4d 100644
--- a/man/skipTrack.fit.Rd
+++ b/man/skipTrack.fit.Rd
@@ -52,8 +52,8 @@ This function fits the model using multiple instances of skipTrack.MCMC, either
 #Simulated data
 simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 
-#Run model fit
-modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#Run model fit (should typically run with much more than 50 reps)
+modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 modFit
 
 }
diff --git a/man/skipTrack.results.Rd b/man/skipTrack.results.Rd
index 2c4c151..a44f51d 100644
--- a/man/skipTrack.results.Rd
+++ b/man/skipTrack.results.Rd
@@ -29,15 +29,18 @@ Note that true values and converage are included in the output if trueVals is su
 #Simulated data
 simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 
-#Run model fit
-modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#Run model fit (should typically run with much more than 50 reps)
+modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 modFit
 
 # If using simulated data (which includes access to ground truth):
 #
-skipTrack.results(modFit, trueVals = simDat, burnIn = 250)
+skipTrack.results(modFit, trueVals = simDat, burnIn = 25)
+#Recommended burnIn with real data is at least 750
 #
 # If not using simulated data:
 #
+skipTrack.results(modFit, burnIn = 25)
+#Recommended burnIn with real data is at least 750
 
 }
diff --git a/man/skipTrack.visualize.Rd b/man/skipTrack.visualize.Rd
index eaaa670..eb4351f 100644
--- a/man/skipTrack.visualize.Rd
+++ b/man/skipTrack.visualize.Rd
@@ -24,8 +24,8 @@ This function takes results from skipTrack.fit and produces several helpful visu
 #Simulated data
 simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 
-#Run model fit
-modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#Run model fit (should typically run with much more than 50 reps)
+modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 
 #Visualize results
 skipTrack.visualize(modFit)
diff --git a/man/str.skipTrack.model.Rd b/man/str.skipTrack.model.Rd
index edcae63..dd6e0ff 100644
--- a/man/str.skipTrack.model.Rd
+++ b/man/str.skipTrack.model.Rd
@@ -4,10 +4,12 @@
 \alias{str.skipTrack.model}
 \title{Report skipTrack.model structure to console}
 \usage{
-\method{str}{skipTrack.model}(x)
+\method{str}{skipTrack.model}(object, ...)
 }
 \arguments{
-\item{x}{skipTrack.model object from the function skipTrack.fit}
+\item{object}{skipTrack.model object from the function skipTrack.fit}
+
+\item{...}{To match other str calls}
 }
 \value{
 Invisible NULL. Prints structure of skipTrack.model object
@@ -19,8 +21,8 @@ Report skipTrack.model structure to console
 #Simulated data
 simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 
-#Run model fit
-modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
+#Run model fit (should typically run with much more than 50 reps)
+modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
 str(modFit)
 
 
diff --git a/man/summary.skipTrack.model.Rd b/man/summary.skipTrack.model.Rd
index f0ee29d..f3bbea7 100644
--- a/man/summary.skipTrack.model.Rd
+++ b/man/summary.skipTrack.model.Rd
@@ -21,9 +21,9 @@ Report skipTrack.model results to console
 #Simulated data
 simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))
 
-#Run model fit
-modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 500)
-summary(modFit, burnIn = 250)
+#Run model fit (should typically run with much more than 50 reps)
+modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)
+summary(modFit, burnIn = 25) #Recommended burnIn with real data is at least 750
 
 
 }