From 09e50865be882ff11d4d269905a821dcfd5d8e42 Mon Sep 17 00:00:00 2001
From: Billy Buchanan <william@williambuchanan.net>
Date: Thu, 29 Feb 2024 06:05:45 -0500
Subject: [PATCH] Upgraded the function signature for the metric functions to
 allow passing optional arguments to the functions.  If options are not
 supported by the underlying function, nothing is done with the optional
 argument.  Updated help files to reflect that change and added titles to each
 of the help files.  Added some more robustness checks for syntax for the xv
 and xvloo commands.  Updated validate it to handle parsing optional arguments
 passed to metrics/monitors (still need to test).  Added tests for libxv to
 check that passing optional arguments would not affect existing functions
 that do not support them.  Updated helpfiles for the main commands to include
 titles.  Updated the README.  Updated the distribution date for the package
 file.  Recompiled the Mata library in Stata 15.

---
 README.md           |   7 ++-
 crossvalidate.mata  | 147 +++++++++++++++++++++++++++++---------------
 crossvalidate.pkg   |   2 +-
 crossvalidate.sthlp |  27 ++++----
 fitit.sthlp         |   3 +-
 libxv.mlib          | Bin 314604 -> 318856 bytes
 predictit.sthlp     |   3 +-
 splitit.sthlp       |   3 +-
 test/libxvtests.do  |   4 ++
 test/xvlootests.do  |  50 ++++++++++++++-
 test/xvtests.do     |  49 +++++++++++++--
 validateit.ado      |  10 ++-
 validateit.sthlp    |  76 ++++++++++++++---------
 xv.ado              |   6 +-
 xv.sthlp            |   1 +
 xvloo.ado           |   6 +-
 xvloo.sthlp         |   3 +-
 17 files changed, 282 insertions(+), 115 deletions(-)

diff --git a/README.md b/README.md
index f365673..d81a6f1 100644
--- a/README.md
+++ b/README.md
@@ -59,6 +59,8 @@ collections for Stata >= 17 and modify help files related to collections.
 - [x] Modify the replay option to adjust for the collection thing above
 - [x] Write a function that can do the same thing as `assertnested` and will 
 work in Stata 15
+- [x] Update function signature to provide support for optional arguments
+- [x] Compile Mata library in Stata 15
 - [ ] Standardize language in help files
 - [ ] Finish writing test cases for Mata functions
 - [ ] Finish writing test cases for ADO commands
@@ -78,11 +80,12 @@ The program will allow users to define their own metrics/monitors that are not
 contained in libcrossvalidate.  In order to do this, users must implement a 
 specific method/function signature:
 
-`real scalar metric(string scalar pred, string scalar obs, string scalar touse)`
+`real scalar metric(string scalar pred, string scalar obs, string scalar touse, | transmorphic matrix opts)`
 
 The function must return a real valued scalar and take three arguments.  The 
 three arguments are used to access the data that would be used to compute the 
-metrics/monitors.  
+metrics/monitors and to provide a method to pass optional arguments to the 
+underlying functions if supported.
 
 ### Data access
 Within the function body, we recommend using the following pattern to access 
diff --git a/crossvalidate.mata b/crossvalidate.mata
index 987b127..6402507 100644
--- a/crossvalidate.mata
+++ b/crossvalidate.mata
@@ -243,9 +243,14 @@ struct Crosstab scalar xtab(string scalar pred, string scalar obs, 			 ///
 		// Gets the total number of cases observed in the ith class
 		colm[1, i] = rows(selectindex(y :== vals[i, 1]))
 		
-		// Loop over the values of the observed variable and count the number of 
-		// cases with the ith predicted value that have the jth observed value
-		for(j = 1; j <= levs; j++) conf[i, j] = rows(selectindex(y[idx, 1] :== vals[j, 1]))
+		// Loop over the values of the observed variable
+		for(j = 1; j <= levs; j++) {
+			
+			// count the number of cases with the ith predicted value that have 
+			// the jth observed value
+			conf[i, j] = rows(selectindex(y[idx, 1] :== vals[j, 1]))
+			
+		} // End Loop over the observed variable values	
 		
 	} // End Loop over the rows of the confusion matrix
 	
@@ -333,7 +338,8 @@ mata:
 // For sensitivity, specificity, prevalence, ppv, and npv see:
 // https://yardstick.tidymodels.org/reference/ppv.html	
 // For others in this section see other pages from above			
-real scalar sens(string scalar pred, string scalar obs, string scalar touse) {
+real scalar sens(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Creates the struct that gets returned
 	struct Crosstab scalar c
@@ -358,7 +364,8 @@ real scalar sens(string scalar pred, string scalar obs, string scalar touse) {
 					
 // Function to compute precision from a confusion matrix.  See:
 // https://yardstick.tidymodels.org/reference/precision.html for the formula
-real scalar prec(string scalar pred, string scalar obs, string scalar touse) {
+real scalar prec(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Creates the struct that gets returned
 	struct Crosstab scalar c
@@ -383,7 +390,8 @@ real scalar prec(string scalar pred, string scalar obs, string scalar touse) {
 
 // Function to compute recall, which appears to be a synonym for sensitivity.  
 // See https://yardstick.tidymodels.org/reference/precision.html for formula					  
-real scalar recall(string scalar pred, string scalar obs, string scalar touse) {
+real scalar recall(string scalar pred, string scalar obs, 					 ///   
+				   string scalar touse, | transmorphic matrix opts) {
 		
 	// Declares a scalar to store the result
 	real scalar result
@@ -398,8 +406,8 @@ real scalar recall(string scalar pred, string scalar obs, string scalar touse) {
 				
 // Defines function to compute specificity from a confusion matrix.  
 // See: https://yardstick.tidymodels.org/reference/ppv.html for the formula
-real scalar spec(string scalar pred, string scalar obs, 				 ///   
-					  string scalar touse) {
+real scalar spec(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Creates the struct that gets returned
 	struct Crosstab scalar c
@@ -424,7 +432,8 @@ real scalar spec(string scalar pred, string scalar obs, 				 ///
 
 // Defines a function to compute prevalence.  
 // See: https://yardstick.tidymodels.org/reference/ppv.html for the formula
-real scalar prev(string scalar pred, string scalar obs, string scalar touse) {
+real scalar prev(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Creates the struct that gets returned
 	struct Crosstab scalar c
@@ -449,7 +458,8 @@ real scalar prev(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines a function to compute positive predictive value.
 // See: https://yardstick.tidymodels.org/reference/ppv.html for the formula
-real scalar ppv(string scalar pred, string scalar obs, string scalar touse) {
+real scalar ppv(string scalar pred, string scalar obs, string scalar touse,  ///   
+					| transmorphic matrix opts) {
 	
 	// Declares a scalar to store the resulting metric, sensitivity, 
 	// specificity, and prevalence (used to compute the metric)
@@ -474,7 +484,8 @@ real scalar ppv(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines a function to compute negative predictive value.
 // See: https://yardstick.tidymodels.org/reference/ppv.html for the formula
-real scalar npv(string scalar pred, string scalar obs, string scalar touse) {
+real scalar npv(string scalar pred, string scalar obs, string scalar touse,  ///   
+					| transmorphic matrix opts) {
 	
 	// Declares a scalar to store the resulting metric, sensitivity, 
 	// specificity, and prevalence (used to compute the metric)
@@ -498,7 +509,8 @@ real scalar npv(string scalar pred, string scalar obs, string scalar touse) {
 } // End of function definition for negative predictive value
 
 // Defines a function to compute accuracy.
-real scalar acc(string scalar pred, string scalar obs, string scalar touse) {
+real scalar acc(string scalar pred, string scalar obs, string scalar touse,  ///   
+					| transmorphic matrix opts) {
 	
 	// Creates the struct that gets returned
 	struct Crosstab scalar c
@@ -519,7 +531,8 @@ real scalar acc(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines a function to compute "balanced" accuracy.  
 // See https://yardstick.tidymodels.org/reference/bal_accuracy.html for more info
-real scalar bacc(string scalar pred, string scalar obs, string scalar touse) {
+real scalar bacc(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Declares a scalar to store the resulting metric, sensitivity, 
 	// specificity, and prevalence (used to compute the metric)
@@ -541,7 +554,8 @@ real scalar bacc(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines function to compute the F1 statistic
 // Based on second equation here: https://www.v7labs.com/blog/f1-score-guide
-real scalar f1(string scalar pred, string scalar obs, string scalar touse) {
+real scalar f1(string scalar pred, string scalar obs, string scalar touse,   ///   
+					| transmorphic matrix opts) {
 	
 	// Declares a scalar to store the resulting metric, precision, and recall
 	real scalar result, prec, rec
@@ -562,7 +576,8 @@ real scalar f1(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines J-index (Youden's J statistic)
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/class-j_index.R
-real scalar jindex(string scalar pred, string scalar obs, string scalar touse) {
+real scalar jindex(string scalar pred, string scalar obs, 					 ///   
+				   string scalar touse, | transmorphic matrix opts) {
 
 	// Return the micro averaged detection prevalence
 	return(sens(pred, obs, touse) + spec(pred, obs, touse) - 1)
@@ -570,7 +585,8 @@ real scalar jindex(string scalar pred, string scalar obs, string scalar touse) {
 } // End of function definition for j-index
 
 // Defines a binary R^2 (tetrachoric correlation)
-real scalar binr2(string scalar pred, string scalar obs, string scalar touse) {
+real scalar binr2(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 
 	// Call the tetrachoric command in Stata
 	stata("cap: qui: tetrachoric " + pred + " " + obs + " if " + touse + ", ed")
@@ -582,7 +598,8 @@ real scalar binr2(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines Matthews correlation coefficient
 // based on: https://en.wikipedia.org/wiki/Phi_coefficient#Multiclass_case
-real scalar mcc(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcc(string scalar pred, string scalar obs, string scalar touse,  ///   
+					| transmorphic matrix opts) {
 		
 	// Creates the struct that gets returned
 	struct Crosstab scalar c
@@ -651,7 +668,8 @@ mata:
 
 // Defines multiclass specificity
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/class-spec.R
-real scalar mcspec(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcspec(string scalar pred, string scalar obs, 					 ///   
+				   string scalar touse, | transmorphic matrix opts) {
 	
 	// Creates the struct that gets returned
 	struct Crosstab scalar c
@@ -692,7 +710,8 @@ real scalar mcspec(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines multiclass sensitivity
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/class-sens.R
-real scalar mcsens(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcsens(string scalar pred, string scalar obs, 					 ///   
+				   string scalar touse, | transmorphic matrix opts) {
 	
 	// Creates the struct that gets returned
 	struct Crosstab scalar c
@@ -707,7 +726,8 @@ real scalar mcsens(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines multiclass recall
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/class-recall.R
-real scalar mcrecall(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcrecall(string scalar pred, string scalar obs, 				 ///   
+					 string scalar touse, | transmorphic matrix opts) {
 	
 	// Return the micro averaged recall
 	return(mcsens(pred, obs, touse))
@@ -716,7 +736,8 @@ real scalar mcrecall(string scalar pred, string scalar obs, string scalar touse)
 
 // Defines multiclass precision
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/class-precision.R
-real scalar mcprec(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcprec(string scalar pred, string scalar obs, 					 ///   
+				   string scalar touse, | transmorphic matrix opts) {
 	
 	// Creates the struct that gets returned
 	struct Crosstab scalar c
@@ -731,7 +752,8 @@ real scalar mcprec(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines multiclass positive predictive value
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/class-ppv.R
-real scalar mcppv(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcppv(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Return the micro averaged PPV
 	// Lines 176-178 indicate that multiclass PPV should be equal to precision 
@@ -744,7 +766,8 @@ real scalar mcppv(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines multiclass negative predictive value
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/class-npv.R
-real scalar mcnpv(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcnpv(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Creates the struct that gets returned
 	struct Crosstab scalar c
@@ -788,7 +811,8 @@ real scalar mcnpv(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines multiclass F1 statistic
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/class-f_meas.R
-real scalar mcf1(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcf1(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Declares scalars to store intermediate results
 	real scalar prec, sens
@@ -806,7 +830,8 @@ real scalar mcf1(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines multiclass Detection Prevalence
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/class-detection_prevalence.R
-real scalar mcdetect(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcdetect(string scalar pred, string scalar obs, 				 ///   
+					 string scalar touse, | transmorphic matrix opts) {
 	
 	// Creates the struct that gets returned
 	struct Crosstab scalar c
@@ -821,7 +846,8 @@ real scalar mcdetect(string scalar pred, string scalar obs, string scalar touse)
 
 // Defines multiclass J-index (Youden's J statistic)
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/class-j_index.R
-real scalar mcjindex(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcjindex(string scalar pred, string scalar obs, 				 ///   
+					 string scalar touse, | transmorphic matrix opts) {
 
 	// Return the micro averaged detection prevalence
 	return(mcsens(pred, obs, touse) + mcspec(pred, obs, touse) - 1)
@@ -830,7 +856,8 @@ real scalar mcjindex(string scalar pred, string scalar obs, string scalar touse)
 
 // Defines multiclass accuracy
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/class-accuracy.R
-real scalar mcacc(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcacc(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Return the accuracy
 	return(acc(pred, obs, touse))
@@ -839,7 +866,8 @@ real scalar mcacc(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines multiclass Balanced Accuracy
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/class-bal_accuracy.R
-real scalar mcbacc(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcbacc(string scalar pred, string scalar obs, 					 ///   
+				   string scalar touse, | transmorphic matrix opts) {
 	
 	// Declares scalars to store the intermediate results
 	real scalar rec, sens
@@ -858,7 +886,8 @@ real scalar mcbacc(string scalar pred, string scalar obs, string scalar touse) {
 // Defines multiclass Kappa
 // similar to accuracy, but normalized by accuracy expected by random chance
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/class-kap.R
-real scalar mckappa(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mckappa(string scalar pred, string scalar obs, 					 ///   
+					string scalar touse, | transmorphic matrix opts) {
 	
 	// Creates the struct that gets returned
 	struct Crosstab scalar c
@@ -888,7 +917,8 @@ real scalar mckappa(string scalar pred, string scalar obs, string scalar touse)
 
 // Defines multiclass Mathews correlation coefficient
 // based on: https://github.com/tidymodels/yardstick/blob/main/src/mcc-multiclass.c
-real scalar mcmcc(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcmcc(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Call the other implementation
 	return(mcc(pred, obs, touse))
@@ -896,7 +926,8 @@ real scalar mcmcc(string scalar pred, string scalar obs, string scalar touse) {
 } // End of function definition for multiclass MCC
 
 // Defines a multiclass R^2 (polychoric correlation)
-real scalar mcordr2(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mcordr2(string scalar pred, string scalar obs, 					 ///   
+					string scalar touse, | transmorphic matrix opts) {
 
 	// Call the tetrachoric command in Stata
 	stata("cap: qui: polychoric " + pred + " " + obs + " if " + touse)
@@ -921,7 +952,8 @@ mata:
 
 // Defines function to compute mean squared error from predicted and observed 
 // outcomes
-real scalar mse(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mse(string scalar pred, string scalar obs, string scalar touse,  ///   
+					| transmorphic matrix opts) {
 	
 	// Column vector to store the squared difference of pred - obs
 	real colvector sqdiff
@@ -942,7 +974,8 @@ real scalar mse(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines function to compute mean absolute error from predicted and observed 
 // outcomes
-real scalar mae(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mae(string scalar pred, string scalar obs, string scalar touse,  ///   
+					| transmorphic matrix opts) {
 	
 	// Column vector to store the absolute difference of pred - obs
 	real colvector absdiff
@@ -963,7 +996,8 @@ real scalar mae(string scalar pred, string scalar obs, string scalar touse) {
 
 // Metric based on definition here:
 // https://developer.nvidia.com/blog/a-comprehensive-overview-of-regression-evaluation-metrics/
-real scalar bias(string scalar pred, string scalar obs, string scalar touse) {
+real scalar bias(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Returns the sum of residuals
 	return(sum(st_data(., obs, touse) - st_data(., pred, touse)))
@@ -972,7 +1006,8 @@ real scalar bias(string scalar pred, string scalar obs, string scalar touse) {
 
 // Metric based on definition here:
 // https://developer.nvidia.com/blog/a-comprehensive-overview-of-regression-evaluation-metrics/
-real scalar mbe(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mbe(string scalar pred, string scalar obs, string scalar touse,  ///   
+					| transmorphic matrix opts) {
 	
 	// Returns the sum of residuals
 	return(sum(st_data(., obs, touse) - st_data(., pred, touse)) /			 ///   
@@ -982,7 +1017,8 @@ real scalar mbe(string scalar pred, string scalar obs, string scalar touse) {
 
 // Metric based on definition here:
 // https://github.com/tidymodels/yardstick/blob/main/R/num-rsq.R
-real scalar r2(string scalar pred, string scalar obs, string scalar touse) {
+real scalar r2(string scalar pred, string scalar obs, string scalar touse,   ///   
+					| transmorphic matrix opts) {
 	
 	// Returns the correlation between the predicted and observed variable
 	return(corr(variance((st_data(., pred, touse), ///   
@@ -991,7 +1027,8 @@ real scalar r2(string scalar pred, string scalar obs, string scalar touse) {
 } // End of function definition for R^2
 
 // Creates function for root mean squared error
-real scalar rmse(string scalar pred, string scalar obs, string scalar touse) {
+real scalar rmse(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 
 	// Returns the square root of the mean squared error
 	return(sqrt(mse(pred, obs, touse)))
@@ -1000,7 +1037,8 @@ real scalar rmse(string scalar pred, string scalar obs, string scalar touse) {
 
 // Metric based on definition of mean absolute percentage error here:
 // https://developer.nvidia.com/blog/a-comprehensive-overview-of-regression-evaluation-metrics/
-real scalar mape(string scalar pred, string scalar obs, string scalar touse) {
+real scalar mape(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 
 	// Allocates a column vector to store the differences
 	real colvector diff
@@ -1017,7 +1055,8 @@ real scalar mape(string scalar pred, string scalar obs, string scalar touse) {
 
 // Metric based on definition of symmetric mean absolute percentage error here:
 // https://github.com/tidymodels/yardstick/blob/main/R/num-smape.R
-real scalar smape(string scalar pred, string scalar obs, string scalar touse) {
+real scalar smape(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 
 	// Allocates a column vector to store the differences
 	real colvector num, denom
@@ -1038,7 +1077,8 @@ real scalar smape(string scalar pred, string scalar obs, string scalar touse) {
 // Defines function to compute mean squared log error from predicted and observed 
 // outcomes.  Based on definition here:
 // https://developer.nvidia.com/blog/a-comprehensive-overview-of-regression-evaluation-metrics/
-real scalar msle(string scalar pred, string scalar obs, string scalar touse) {
+real scalar msle(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Column vector to store the squared difference of pred - obs
 	real colvector sqdiff
@@ -1060,7 +1100,8 @@ real scalar msle(string scalar pred, string scalar obs, string scalar touse) {
 // Defines function to compute the root mean squared log error.  Based on 
 // definition here:
 // https://developer.nvidia.com/blog/a-comprehensive-overview-of-regression-evaluation-metrics/
-real scalar rmsle(string scalar pred, string scalar obs, string scalar touse) {
+real scalar rmsle(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 
 	// Returns the square root of the mean squared log error
 	return(sqrt(msle(pred, obs, touse)))
@@ -1069,7 +1110,8 @@ real scalar rmsle(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines function for the ratio of performance to deviation
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/num-rpd.R
-real scalar rpd(string scalar pred, string scalar obs, string scalar touse) {
+real scalar rpd(string scalar pred, string scalar obs, string scalar touse,  ///   
+					| transmorphic matrix opts) {
 	
 	// Returns the ratio of the SD of predicted to the RMSE
 	return(sqrt(variance(st_data(., pred, touse))) / rmse(pred, obs, touse))
@@ -1078,7 +1120,8 @@ real scalar rpd(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines function for the Index of ideality of correlation
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/num-iic.R
-real scalar iic(string scalar pred, string scalar obs, string scalar touse) {
+real scalar iic(string scalar pred, string scalar obs, string scalar touse,  ///   
+					| transmorphic matrix opts) {
 	
 	// Declares some columnvectors
 	real colvector neg, pos, delta
@@ -1111,7 +1154,8 @@ real scalar iic(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines function for the Concordance Correlation Coefficient
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/num-ccc.R
-real scalar ccc(string scalar pred, string scalar obs, string scalar touse) {
+real scalar ccc(string scalar pred, string scalar obs, string scalar touse,  ///   
+					| transmorphic matrix opts) {
 	
 	// Declares scalars needed
 	real scalar mupred, muobs, varpred, varobs, cov
@@ -1138,7 +1182,8 @@ real scalar ccc(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines function for Pseudo-Huber Loss
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/num-pseudo_huber_loss.R
-real scalar phl(string scalar pred, string scalar obs, string scalar touse) {
+real scalar phl(string scalar pred, string scalar obs, string scalar touse,  ///   
+					| transmorphic matrix opts) {
 	
 	// Declares a column vector to store the errors
 	real colvector a
@@ -1153,7 +1198,8 @@ real scalar phl(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines function for Huber Loss
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/num-huber_loss.R
-real scalar huber(string scalar pred, string scalar obs, string scalar touse) {
+real scalar huber(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Declares a column vector to store the errors and absolute errors
 	real colvector a, absa 
@@ -1172,7 +1218,8 @@ real scalar huber(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines function for Poisson Log Loss
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/num-poisson_log_loss.R
-real scalar pll(string scalar pred, string scalar obs, string scalar touse) {
+real scalar pll(string scalar pred, string scalar obs, string scalar touse,  ///   
+					| transmorphic matrix opts) {
 	
 	// Returns the mean of the negative log poisson density
 	return(mean(-dpois(st_data(., obs, touse), st_data(., pred, touse), 1)))
@@ -1181,7 +1228,8 @@ real scalar pll(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines function for ratio of performance to interquartile range
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/num-rpiq.R
-real scalar rpiq(string scalar pred, string scalar obs, string scalar touse) {
+real scalar rpiq(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 
 	// Declares scalar to store the iqr
 	real scalar iqr
@@ -1199,7 +1247,8 @@ real scalar rpiq(string scalar pred, string scalar obs, string scalar touse) {
 
 // Defines function for "Traditional" R^2
 // based on: https://github.com/tidymodels/yardstick/blob/main/R/num-rsq_trad.R
-real scalar r2ss(string scalar pred, string scalar obs, string scalar touse) {
+real scalar r2ss(string scalar pred, string scalar obs, string scalar touse, ///   
+					| transmorphic matrix opts) {
 	
 	// Declares a scalar to store the mean of the observed values
 	real scalar mu
diff --git a/crossvalidate.pkg b/crossvalidate.pkg
index 22edda3..a9f2eac 100644
--- a/crossvalidate.pkg
+++ b/crossvalidate.pkg
@@ -20,7 +20,7 @@ d KW: evaluation
 d
 d Requires: Stata version 15
 d
-d Distribution-Date: 20240228
+d Distribution-Date: 20240229
 d
 d Author: Billy Buchanan, Ph.D.
 d         Sr. Research Scientist, SAG Corporation
diff --git a/crossvalidate.sthlp b/crossvalidate.sthlp
index d326f9d..1308dd2 100644
--- a/crossvalidate.sthlp
+++ b/crossvalidate.sthlp
@@ -1,5 +1,5 @@
 {smcl}
-{* *! version 0.0.1 17feb2024}{...}
+{* *! version 0.0.2 29feb2024}{...}
 {vieweralsosee "[R] predict" "mansection R predict"}{...}
 {vieweralsosee "[R] estat classification" "mansection R estat_classification"}{...}
 {vieweralsosee "[P] creturn" "mansection P creturn"}{...}
@@ -8,9 +8,9 @@
 {viewerjumpto "Commands" "crossvalidate##cmds"}{...}
 {viewerjumpto "Additional Information" "crossvalidate##additional"}{...}
 {viewerjumpto "Contact" "crossvalidate##contact"}{...}
+{title:Cross-Validation in Stata}
 
-{marker overview}
-{title:Overview}
+{marker overview}{title:Overview}
 
 {pstd}
 The crossvalidate package includes several commands and a Mata library that 
@@ -27,25 +27,24 @@ This help file provides an overview of the commands included in the crossvalidat
 package.  We leave detailed information to the documentation for each of the 
 individual commands.
 
-{marker cmds}
-{title:Commands}
+{marker cmds}{title:Commands}
 
 {synoptset 15 tabbed}{...}
 {synoptline}
 {synopthdr:Command Name}
 {synoptline}
 {syntab:Prefix Commands}
-{synopt :{opt xv}}Cross-Validation{p_end}
-{synopt :{opt xvloo}}Leave-One-Out Cross-Validation{p_end}
+{synopt :{opt {help xv}}}Cross-Validation{p_end}
+{synopt :{opt {help xvloo}}}Leave-One-Out Cross-Validation{p_end}
 {syntab:Lower Level Commands}
-{synopt :{opt splitit}}Splits the dataset into train/test or train/validation/test splits{p_end}
-{synopt :{opt fitit}}Calls the estimation command on the appropriate split{p_end}
-{synopt :{opt predictit}}Predicts the outcome on the appropriate split{p_end}
-{synopt :{opt validateit}}Computes {p_end}
+{synopt :{opt {help splitit}}}Splits the dataset into train/test or train/validation/test splits{p_end}
+{synopt :{opt {help fitit}}}Calls the estimation command on the appropriate split{p_end}
+{synopt :{opt {help predictit}}}Predicts the outcome on the appropriate split{p_end}
+{synopt :{opt {help validateit}}}Computes {p_end}
 {syntab:Utility Commands}
-{synopt :{opt classify}}Used to manage {p_end}
-{synopt :{opt cmdmod}}Used for metaprogramming tasks in commands above{p_end}
-{synopt :{opt state}}Retrieves current settings and binds to the dataset{p_end}
+{synopt :{opt {help classify}}}Used to manage {p_end}
+{synopt :{opt {help cmdmod}}}Used for metaprogramming tasks in commands above{p_end}
+{synopt :{opt {help state}}}Retrieves current settings and binds to the dataset{p_end}
 {synoptline}
 
 {dlgtab:Prefix Commands}
diff --git a/fitit.sthlp b/fitit.sthlp
index be6f926..5dfb661 100644
--- a/fitit.sthlp
+++ b/fitit.sthlp
@@ -1,5 +1,5 @@
 {smcl}
-{* *! version 0.0.6 28feb2024}{...}
+{* *! version 0.0.7 29feb2024}{...}
 {vieweralsosee "[R] estat classification" "mansection R estat_classification"}{...}
 {vieweralsosee "" "--"}{...}
 {viewerjumpto "Syntax" "fitit##syntax"}{...}
@@ -9,6 +9,7 @@
 {viewerjumpto "Returned Values" "fitit##retvals"}{...}
 {viewerjumpto "Additional Information" "fitit##additional"}{...}
 {viewerjumpto "Contact" "fitit##contact"}{...}
+{title:Model Fitting for Cross-Validation in Stata}
 
 {marker syntax}{...}
 {title:Syntax}
diff --git a/libxv.mlib b/libxv.mlib
index 8cc271be615ebe5dd881b223792140d97c2d5c51..19eeb2af1dffd20c32cdf666c67418109ec1909e 100644
GIT binary patch
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diff --git a/predictit.sthlp b/predictit.sthlp
index e3b2549..0406d26 100644
--- a/predictit.sthlp
+++ b/predictit.sthlp
@@ -1,5 +1,5 @@
 {smcl}
-{* *! version 0.0.3 23feb2024}{...}
+{* *! version 0.0.4 29feb2024}{...}
 {vieweralsosee "[R] estat classification" "mansection R estat_classification"}{...}
 {vieweralsosee "" "--"}{...}
 {viewerjumpto "Syntax" "predictit##syntax"}{...}
@@ -8,6 +8,7 @@
 {viewerjumpto "Examples" "predictit##examples"}{...}
 {viewerjumpto "Additional Information" "predictit##additional"}{...}
 {viewerjumpto "Contact" "predictit##contact"}{...}
+{title:Generating and Managing Model Predictions for Cross-Validation in Stata}
 
 {marker syntax}{...}
 {title:Syntax}
diff --git a/splitit.sthlp b/splitit.sthlp
index 93b2040..15f554c 100644
--- a/splitit.sthlp
+++ b/splitit.sthlp
@@ -1,5 +1,5 @@
 {smcl}
-{* *! version 0.0.5 28feb2024}{...}
+{* *! version 0.0.6 29feb2024}{...}
 {viewerjumpto "Syntax" "splitit##syntax"}{...}
 {viewerjumpto "Description" "splitit##description"}{...}
 {viewerjumpto "Options" "splitit##options"}{...}
@@ -7,6 +7,7 @@
 {viewerjumpto "Returned Values" "splitit##retvals"}{...}
 {viewerjumpto "Additional Information" "splitit##additional"}{...}
 {viewerjumpto "Contact" "splitit##contact"}{...}
+{title:Dataset Splitting and Folding for Cross-Validation in Stata}
 
 {marker syntax}{...}
 {title:Syntax}
diff --git a/test/libxvtests.do b/test/libxvtests.do
index ac3574c..29fde31 100644
--- a/test/libxvtests.do
+++ b/test/libxvtests.do
@@ -223,6 +223,10 @@ asserteq(round(fxbinr2, rf), round(binr2("pred", "obs", "touse"), rf))
 // Test equality of Matthews Correlation Coefficient
 asserteq(round(fxmcc, rf), round(mcc("pred", "obs", "touse"), rf))
 
+// Test equality of F1 metrics with new function signature
+asserteq(round(fxf1, rf), round(f1("pred", "obs", "touse", (1, 2, 3)), rf))
+asserteq(round(fxf1, rf), round(f1("pred", "obs", "touse", ("yes", "no")), rf))
+
 // End the Mata session
 end
 
diff --git a/test/xvlootests.do b/test/xvlootests.do
index 58f859c..8d3871f 100644
--- a/test/xvlootests.do
+++ b/test/xvlootests.do
@@ -18,6 +18,9 @@ run crossvalidate.mata
 * individual commands that are called subsequently in the prefix.              *
 *******************************************************************************/
 
+// Test that correct error code is thrown when there are no results to replay
+rcof "xvloo 0.8, replay: mlogit industry south" == 119
+
 // Test that correct error code is thrown when 
 rcof "xvloo 0.98, pstub(pred) metric(mse) classes(12): mlogit industry south" == 1002
 
@@ -30,6 +33,21 @@ rcof "xvloo 0.7, metric(mse): reg wage i.industry" == 198
 // Test that error code is thrown if the user tries to specify K-Folds
 rcof "xvloo 0.7, pstub(pred) metric(mse) kfold(4): reg wage i.industry " == 184
 
+// Create a variable that should trigger an error for an existing `pstub'all 
+// variable
+qui: g byte predall = rbinomial(3, 0.5)
+
+// Test that error code is thrown if the user is trying to create the `pstub'all
+// variable if it already exists
+rcof "xvloo 0.7, pstub(pred) metric(mse): reg wage i.industry " == 110
+
+// Now test that the same will happen if the pstub variable already exists
+rename predall pred
+
+// Test that error code is thrown if the user is trying to create the `pstub'
+// variable if it already exists
+rcof "xvloo 0.7, pstub(pred) metric(mse): reg wage i.industry " == 110
+
 // Clear everything
 clear all
 
@@ -56,6 +74,32 @@ xvloo 0.8, metric(mse) pstub(pred) monitors(mae mape) display retain: reg mpg pr
 // There should be 59 stored estimation results, pred, predall, and _xvsplit 
 // added as variables
 
-// There should be values in e(splitter), e(training), e(validation), 
-// e(stype), e(flavor), e(estresname), e(estresall)
-
+// Make sure the returned values are populated
+assert !mi(`e(rng)')
+assert !mi(`e(rngcurrent)')
+assert !mi(`e(rngstate)')
+assert !mi(`e(rngseed)')
+assert !mi(`e(rngstream)')
+assert !mi(`e(filename)')
+assert !mi(`e(filedate)')
+assert !mi(`e(version)')
+assert !mi(`e(currentdate)')
+assert !mi(`e(currenttime)')
+assert !mi(`e(stflavor)')
+assert !mi(`e(processors)')
+assert !mi(`e(hostname)')
+assert !mi(`e(machinetype)')
+assert !mi(`e(splitter)')
+assert !mi(`e(training)')
+assert !mi(`e(validation)')
+assert !mi(`e(testing)')
+assert !mi(`e(stype)')
+assert !mi(`e(flavor)')
+assert mi(`e(forecastset)')
+assert !mi(`e(estresnames)')
+assert !mi(`e(estresall)')
+assert !mi(`e(fitnm)')
+assert !mi(`e(valnm)')
+assert !mi(`e(xv)')
+
+assert `"`e(stype)'"' == "Leave One Out"
diff --git a/test/xvtests.do b/test/xvtests.do
index 4bcbdca..b340550 100644
--- a/test/xvtests.do
+++ b/test/xvtests.do
@@ -13,6 +13,8 @@ run crossvalidate.mata
 * individual commands that are called subsequently in the prefix.              *
 *******************************************************************************/
 
+// Test that correct error code is thrown when there are no results to replay
+rcof "xvloo 0.8, replay: mlogit industry south" == 119
 
 // Test that error code is thrown for missing metric   
 rcof "xv 0.7, pstub(pred): reg wage i.industry" == 198
@@ -20,6 +22,21 @@ rcof "xv 0.7, pstub(pred): reg wage i.industry" == 198
 // Test that error code is thrown for missing predicted value stub 
 rcof "xv 0.7, metric(mse): reg wage i.industry" == 198
 
+// Create a variable that should trigger an error for an existing `pstub'all 
+// variable
+qui: g byte predall = rbinomial(3, 0.5)
+
+// Test that error code is thrown if the user is trying to create the `pstub'all
+// variable if it already exists
+rcof "xv 0.7, pstub(pred) metric(mse): reg wage i.industry " == 110
+
+// Now test that the same will happen if the pstub variable already exists
+rename predall pred
+
+// Test that error code is thrown if the user is trying to create the `pstub'
+// variable if it already exists
+rcof "xv 0.7, pstub(pred) metric(mse): reg wage i.industry " == 110
+
 // Clear everything
 clear all
 
@@ -40,9 +57,31 @@ run crossvalidate.mata
 // variables 
 xv 0.8, metric(r2) pstub(pred) monitors() display retain: reg mpg price i.rep78, vce(rob)
 
-// There should be N stored estimation results, pred, predall, and _xvsplit 
-// added as variables
-
-// There should be values in e(splitter), e(training), e(validation), 
-// e(stype), e(flavor), e(estresname), e(estresall)
 
+// Make sure the returned values are populated
+assert !mi(`e(rng)')
+assert !mi(`e(rngcurrent)')
+assert !mi(`e(rngstate)')
+assert !mi(`e(rngseed)')
+assert !mi(`e(rngstream)')
+assert !mi(`e(filename)')
+assert !mi(`e(filedate)')
+assert !mi(`e(version)')
+assert !mi(`e(currentdate)')
+assert !mi(`e(currenttime)')
+assert !mi(`e(stflavor)')
+assert !mi(`e(processors)')
+assert !mi(`e(hostname)')
+assert !mi(`e(machinetype)')
+assert !mi(`e(splitter)')
+assert !mi(`e(training)')
+assert !mi(`e(validation)')
+assert !mi(`e(testing)')
+assert !mi(`e(stype)')
+assert !mi(`e(flavor)')
+assert mi(`e(forecastset)')
+assert !mi(`e(estresnames)')
+assert !mi(`e(estresall)')
+assert !mi(`e(fitnm)')
+assert !mi(`e(valnm)')
+assert !mi(`e(xv)')
diff --git a/validateit.ado b/validateit.ado
index 966a682..332b0bf 100644
--- a/validateit.ado
+++ b/validateit.ado
@@ -404,8 +404,11 @@ prog def getstats, rclass
 		// Get the name of the function for monitoring
 		loc monnm : word `i' of `monitors'
 		
+		// Get any arguments passed to the monitor
+		mata: getargs("`monnm'", "mnopt")
+		
 		// Call the mata function
-		mata: `sto'[`i', 1] = `monnm'("`pstub'", "`obs'", "`touse'")
+		mata: `sto'[`i', 1] = `monnm'("`pstub'", "`obs'", "`touse'", `mnopt')
 		
 		// Creates a Stata scalar with the appropriate value
 		mata: st_numscalar("`monnm'`sfx'", `sto'[`i', 1])
@@ -418,8 +421,11 @@ prog def getstats, rclass
 		
 	} // End loop over monitors
 	
+	// Get any arguments passed to the metric
+	mata: getargs("`metric'", "meopt")
+	
 	// Call the mata function for the metric
-	mata: `sto'[`m', 1] = `metric'("`pstub'", "`obs'", "`touse'")
+	mata: `sto'[`m', 1] = `metric'("`pstub'", "`obs'", "`touse'", `meopt')
 	
 	// Push the value into a scalar
 	mata: st_numscalar("`metric'sc", `sto'[`m', 1])
diff --git a/validateit.sthlp b/validateit.sthlp
index d62ada1..16852f1 100644
--- a/validateit.sthlp
+++ b/validateit.sthlp
@@ -1,5 +1,5 @@
 {smcl}
-{* *! version 0.0.8 28feb2024}{...}
+{* *! version 0.0.9 29feb2024}{...}
 {viewerjumpto "Syntax" "validateit##syntax"}{...}
 {viewerjumpto "Description" "validateit##description"}{...}
 {viewerjumpto "Options" "validateit##options"}{...}
@@ -9,6 +9,7 @@
 {viewerjumpto "Returned Values" "validateit##retvals"}{...}
 {viewerjumpto "Additional Information" "validateit##additional"}{...}
 {viewerjumpto "Contact" "validateit##contact"}{...}
+{title:Computing Model Validation Statistics}
 
 {marker syntax}{...}
 {title:Syntax}
@@ -38,7 +39,6 @@
 {synopt :{opt na:me}}is used to name the collection storing the results; default is {cmd:name(xvval)}.{p_end}
 {synoptline}
 
-
 {marker description}{...}
 {title:Description}
 
@@ -54,7 +54,12 @@ INCLUDE help xvphase-validate
 function signature described in {help libxv:help libxv} used to evaluate the fit 
 of the model on the held-out data.  Only a single metric can be specified.  For 
 user's who may be interested in hyperparameter tuning, this would be the value 
-that you would optimize with your hyperparameter tuning algorithm.
+that you would optimize with your hyperparameter tuning algorithm. You may pass 
+optional arguments to metrics that support them by enclosing the arguments in a 
+{help mf_eltype:transmorphic matrix}.  Metrics that support optional arguments 
+will provide guidance on what options are available and how to order/structure 
+the options for use in the function.  For example, this might look like: 
+{opt metric(mse(("yes", "1")))} or {opt metric(mae((1, 2 \ 3, 4)))}.
 
 {phang}
 {opt ps:tub} the stub name for the variable(s) that store the predicted values 
@@ -75,7 +80,13 @@ were created using {help splitit}.
 with the function signature described in {help libxv:help libxv} used to 
 evaluate the fit of the model on the held-out data.  These should not be used 
 when attempting to tune hyper parameters, but can still provide useful 
-information regarding the model fit characteristics.
+information regarding the model fit characteristics. You may pass optional 
+arguments to the metrics you list in the monitors option that support them by 
+enclosing the arguments for each monitor in a 
+{help mf_eltype:transmorphic matrix}.  Metrics that support optional arguments 
+will provide guidance on what options are available and how to order/structure 
+the options for use in the function.  For example, this might look like: 
+{opt monitors(mse(("yes", "1")) mae((1, 2 \ 3, 4)))}.
 
 {phang}
 {opt dis:play} an option to display the metric and monitor values in the results 
@@ -102,12 +113,11 @@ the predicted and observed values for all of the training set units in aggregate
 
 {phang}
 {opt na:me} is an option to pass a name to the collection created to store the 
-results.  When {cmd validateit} is executed, it will initialize a new collection 
+results.  When {cmd:validateit} is executed, it will initialize a new collection 
 or replace the existing collection with the same name.  If you want to retain 
 the validation results from multiple executions, pass an argument to this 
 option.  {it:Note:} this only affects users using Stata 17 or later.
 
-
 {marker custom}{...}
 {title:Custom Metrics and Monitors}
 
@@ -115,15 +125,24 @@ option.  {it:Note:} this only affects users using Stata 17 or later.
 Users may define their own validation metrics to be used by {cmd:validateit}.  
 All metrics and monitors are required to use the same function signature:
 
-{p 12 12 2}{cmd:{it:real scalar metricName(string scalar pred, string scalar obs, string scalar touse)}}{p_end}
+{p 12 12 2}{cmd:{it:real scalar metricName(string scalar pred, string scalar obs, string scalar touse, | transmorphic matrix opts)}}{p_end}
 
 {pstd}
 The first argument passed to your function will be the name of the variable 
 containing the predicted values.  The second argument passed to your function 
-will be the name of the variable containing the observed outcomes.  The last 
-argument in the signature is a variable that identifies the validation/test set, 
-or the K-Fold with the out-of-sample predicted values, to compute the validation 
-metric on.
+will be the name of the variable containing the observed outcomes.  The third 
+argument in the signature is a variable that identifies the appropriate data to 
+use for the metric computation.  The last, and only optional argument, is to 
+allow users greater flexibility in defining custom metrics and to allow our team 
+to further extend the existing metrics.  When passing an argument to the 
+{opt metric} option, you can pass optional arguments to the function in a 
+{help mf_eltype:transmorphic matrix} that you specify.  As a hypothetical 
+example, this could look like: 
+{opt monitors(mse(("yes", "1")) mae((1, 2 \ 3, 4)))}.  {cmd:validateit} will 
+handle parsing and passing the matrix to the underlying Mata function for you.  
+If you wish to allow options for your own custom defined function, remember to 
+document the option specification clearly and keep things as simple as possible 
+to minimize user-frustration and problems.
 
 {pstd}
 In your function, you can easily define the vectors that will store the data you 
@@ -136,9 +155,9 @@ need for your computations:
 {pstd}
 With your custom metric function defined in Mata with the signature above, you 
 can use it as a metric or monitor with {cmd:validateit} by passing the function 
-name to the metric or monitors options.  {it:Note, you will need to make sure 
-that the function is defined in Mata prior to using it or ensure that it is 
-defined in a library that Mata will search automatically}.
+name to the metric or monitors options.  {it:Note, you will need to make sure }
+{it:that the function is defined in Mata prior to using it or ensure that it is}
+{it: defined in a library that Mata will search automatically}.
 
 {marker builtin}{...}
 {title:Built-In Metrics and Monitors}
@@ -160,18 +179,15 @@ INCLUDE help xvconttab
 {synoptline}
 {synopt :{opt ***}  {it:Note this requires installation of {search polychoric}}}
 
-{marker binmtrx}
-{title:Binary Metric Details}
+{marker binmtrx}{title:Binary Metric Details}
 
 INCLUDE help xvbinmtrx
 
-{marker mcmtrx}
-{title:Multiclass Metric Details}
+{marker mcmtrx}{title:Multiclass Metric Details}
 
 INCLUDE help xvmcmtrx
 
-{marker contmtrx}
-{title:Continuous Metric Details}
+{marker contmtrx}{title:Continuous Metric Details}
 
 INCLUDE help xvcontmtrx
 
@@ -184,7 +200,6 @@ INCLUDE help xvcontmtrx
 {p 4 4 2}With Monitors{p_end}
 {p 8 4 2}validateit, me(acc) ps(pred) spl(splitvar) mo(npv ppv bacc f1 sens spec){p_end}
 
-
 {marker retvals}{...}
 {title:Returned Values}
 
@@ -216,22 +231,25 @@ the training split.
   
 {marker additional}{...}
 {title:Additional Information}
+
 {pstd}
-The {cmdab dis:play} option in validateit is enabled by 
-{help collect_preview:collect preview}.  In addition to providing a convient way 
-for us to structure the display in a useful way, it also makes it easy for you - 
-the user - to export the validation results into any of several formats.  The 
-results from {help validateit} are all stored in the collection named 
-{cmd:xvval}.  For more information on how to export these results into the 
-format of your choosing, please see {help collect_export:collect export}.
+The {opt dis:play} option in validateit is enabled by 
+{help collect_preview:collect preview} for users running Stata 17 or later.  
+In addition to providing a convient way for us to structure the display in a 
+more aesthetically pleasing way, it also makes it easy for you to export the 
+validation results into any of several formats.  The results from 
+{help validateit} are all stored in the collection named {cmd:xvval} if you do 
+not pass an argument to the {opt name} option.  For more information on how to 
+export these results into the format of your choosing, please see 
+{help collect_export:collect export}.
 
 {pstd}
 If you have questions, comments, or find bugs, please submit an issue in the 
 {browse "https://github.com/wbuchanan/crossvalidate":crossvalidate GitHub repository}.
 
-
 {marker contact}{...}
 {title:Contact}
+
 {p 4 4 8}William R. Buchanan, Ph.D.{p_end}
 {p 4 4 8}Sr. Research Scientist, SAG Corporation{p_end}
 {p 4 4 8}{browse "https://www.sagcorp.com":SAG Corporation}{p_end}
diff --git a/xv.ado b/xv.ado
index 0feebd8..531dc7c 100644
--- a/xv.ado
+++ b/xv.ado
@@ -5,8 +5,8 @@
 *******************************************************************************/
 
 *! xv
-*! v 0.0.5
-*! 28FEB2023
+*! v 0.0.6
+*! 29FEB2023
 
 // Drop program from memory if already loaded
 cap prog drop xv
@@ -92,7 +92,7 @@ prog def xv, eclass properties(prefix xv)
 			"Try refitting the models using {help xv} again."
 			
 			// Throw an error message
-			err 198
+			err 119
 			
 		} // End ELSE Block for no detected collection names
 		
diff --git a/xv.sthlp b/xv.sthlp
index e5ba7be..29ec762 100644
--- a/xv.sthlp
+++ b/xv.sthlp
@@ -17,6 +17,7 @@
 {viewerjumpto "Returned Values" "xv##retvals"}{...}
 {viewerjumpto "Additional Information" "xv##additional"}{...}
 {viewerjumpto "Contact" "xv##contact"}{...}
+{title:Cross-Validation in Stata}
 
 {marker syntax}{...}
 {title:Syntax}
diff --git a/xvloo.ado b/xvloo.ado
index 1badcee..16fd5e6 100644
--- a/xvloo.ado
+++ b/xvloo.ado
@@ -5,8 +5,8 @@
 *******************************************************************************/
 
 *! xvloo
-*! v 0.0.7
-*! 28FEB2023
+*! v 0.0.8
+*! 29FEB2023
 
 // Drop program from memory if already loaded
 cap prog drop xvloo
@@ -96,7 +96,7 @@ prog def xvloo, eclass properties(prefix xv) sortpreserve
 			"Try refitting the models using {help xv} again."
 			
 			// Throw an error message
-			err 198
+			err 119
 			
 		} // End ELSE Block for no detected collection names
 
diff --git a/xvloo.sthlp b/xvloo.sthlp
index 9b2625d..5fea728 100644
--- a/xvloo.sthlp
+++ b/xvloo.sthlp
@@ -1,5 +1,5 @@
 {smcl}
-{* *! version 0.0.3 28feb2024}{...}
+{* *! version 0.0.4 29feb2024}{...}
 {vieweralsosee "[R] predict" "mansection R predict"}{...}
 {vieweralsosee "[R] estat classification" "mansection R estat_classification"}{...}
 {vieweralsosee "[P] creturn" "mansection P creturn"}{...}
@@ -22,6 +22,7 @@
 {viewerjumpto "Returned Values" "xv##looretvals"}{...}
 {viewerjumpto "Additional Information" "xvloo##additional"}{...}
 {viewerjumpto "Contact" "xvloo##contact"}{...}
+{title:Leave-One-Out Cross-Validation in Stata}
 
 {marker syntax}{...}
 {title:Syntax}