[R-pkgs] new cv package: cross-validation of regression models

[John Fox]

Georges Monette and I would like to announce a new package, cv, now on 
CRAN, which implements cross-validation of regression models.

Some of the functions supplied by the package:

-   cv() is a generic function with a default method and computationally 
efficient "lm" and "glm" methods, along with a method for a list of 
competing models. There are also experimental "merMod", "lme", and 
"glmmTMB" methods for mixed-effects models. cv() supports parallel 
computations.

-   mse() (mean-squared error) and BayesRule() are cross-validation 
criteria ("cost functions"), suitable for use with cv().

-   cvSelect() cross-validates a selection procedure for a regression 
model. cvSelect() also supports parallel computations.

-   selectStepAIC() is a model-selection procedure, suitable for use 
with cvSelect(), based on the stepAIC() function in the MASS package.

-   selectTrans() is a procedure for selecting predictor and response 
transformations in regression, also suitable for use with cvSelect(), 
based on the powerTransform() function in the car package.

For additional information on using the cv package, see the 
"Cross-validation of regression models" vignette, in the package and at 
<https://cran.r-project.org/web/packages/cv/vignettes/cv.html>. The cv 
package is designed to be extensible to other classes of regression 
models and other model-selection procedures; for details, see the 
"Extending the cv package" vignette, also in the package and at 
<https://cran.r-project.org/web/packages/cv/vignettes/cv-extend.html>.

Comments and suggestions would be appreciated. Bug reports and problems 
can be filed at <https://github.com/gmonette/cv/issues>.

Thank you for your attention,
  John and Georges


-- 
John Fox, Professor Emeritus
McMaster University
Hamilton, Ontario, Canada
web: https://www.john-fox.ca/