# Cross-Validation for Model Selection

## Documentation for package ‘cvms’ version 2.0.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

cvms-package | cvms: A package for cross-validating regression and classification models
as.character.process_info_binomial | A set of process information object constructors
as.character.process_info_gaussian | A set of process information object constructors
as.character.process_info_multinomial | A set of process information object constructors
baseline | Create baseline evaluations
baseline_binomial | Create baseline evaluations for binary classification
baseline_gaussian | Create baseline evaluations for regression models
baseline_multinomial | Create baseline evaluations
binomial_metrics | Select metrics for binomial evaluation
combine_predictors | Generate model formulas by combining predictors
compatible.formula.terms | Compatible formula terms
confusion_matrix | Create a confusion matrix
cross_validate | Cross-validate regression models for model selection
cross_validate_fn | Cross-validate custom model functions for model selection
cvms | cvms: A package for cross-validating regression and classification models
dynamic_font_color_settings | Create a list of dynamic font color settings for plots
evaluate | Evaluate your model's performance
evaluate_residuals | Evaluate residuals from a regression task
font | Create a list of font settings for plots
gaussian_metrics | Select metrics for Gaussian evaluation
generate_formulas | Generate model formulas by combining predictors
hardest | Find the data points that were hardest to predict
model_functions | Examples of model_fn functions
most_challenging | Find the data points that were hardest to predict
multiclass_probability_tibble | Generate a multiclass probability tibble
multinomial_metrics | Select metrics for multinomial evaluation
musicians | Musician groups
participant.scores | Participant scores
plot_confusion_matrix | Plot a confusion matrix
plot_metric_density | Density plot for a metric
precomputed.formulas | Precomputed formulas
predicted.musicians | Predicted musician groups
predict_functions | Examples of predict_fn functions
preprocess_functions | Examples of preprocess_fn functions
print.process_info_binomial | A set of process information object constructors
print.process_info_gaussian | A set of process information object constructors
print.process_info_multinomial | A set of process information object constructors
process_info_binomial | A set of process information object constructors
process_info_gaussian | A set of process information object constructors
process_info_multinomial | A set of process information object constructors
reconstruct_formulas | Reconstruct model formulas from results tibbles
select_definitions | Select model definition columns
select_metrics | Select columns with evaluation metrics and model definitions
simplify_formula | Simplify formula with inline functions
summarize_metrics | Summarize metrics with common descriptors
sum_tile_settings | Create a list of settings for the sum tiles in plot_confusion_matrix()
update_hyperparameters | Check and update hyperparameters
validate | Validate regression models on a test set
validate_fn | Validate a custom model function on a test set
wines | Wine varieties