Package: cv 2.0.3

Georges Monette

cv: Cross-Validating Regression Models

Cross-validation methods of regression models that exploit features of various modeling functions to improve speed. Some of the methods implemented in the package are novel, as described in the package vignettes; for general introductions to cross-validation, see, for example, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2021, ISBN 978-1-0716-1417-4, Secs. 5.1, 5.3), "An Introduction to Statistical Learning with Applications in R, Second Edition", and Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009, ISBN 978-0-387-84857-0, Sec. 7.10), "The Elements of Statistical Learning, Second Edition".

Authors:John Fox [aut], Georges Monette [aut, cre]

cv_2.0.3.tar.gz
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cv_2.0.3.tgz(r-4.4-any)cv_2.0.3.tgz(r-4.3-any)
cv_2.0.3.tar.gz(r-4.5-noble)cv_2.0.3.tar.gz(r-4.4-noble)
cv_2.0.3.tgz(r-4.4-emscripten)cv_2.0.3.tgz(r-4.3-emscripten)
cv.pdf |cv.html
cv/json (API)
NEWS

# Install 'cv' in R:
install.packages('cv', repos = c('https://gmonette.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/gmonette/cv/issues

Datasets:
  • Pigs - Body Weights of 48 Pigs in 9 Successive Weeks

On CRAN:

7.64 score 3 stars 79 scripts 474 downloads 19 exports 71 dependencies

Last updated 1 months agofrom:2c1eedd4ca. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 15 2024
R-4.5-winOKNov 15 2024
R-4.5-linuxOKNov 15 2024
R-4.4-winOKNov 15 2024
R-4.4-macOKNov 15 2024
R-4.3-winOKNov 15 2024
R-4.3-macOKNov 15 2024

Exports:BayesRuleBayesRule2compareFoldscvcvComputecvInfocvMixedcvSelectfoldfoldsGetResponsemedAbsErrmodelsmsermseselectModelListselectStepAICselectTransselectTransStepAIC

Dependencies:abindbackportsbootbroomcarcarDataclicodetoolscolorspacecowplotcpp11DerivdoBydoParalleldplyrfansifarverforeachFormulagenericsggplot2glmmTMBgluegtablegtoolsinsightisobanditeratorslabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpurrrquantregR6rbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangscalesSparseMstringistringrsurvivaltibbletidyrtidyselectTMButf8vctrsviridisLitewithr

Computational and technical notes on cross-validating regression models

Rendered fromcv-notes.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2024-09-02
Started: 2024-04-03

Cross-validating mixed-effects models

Rendered fromcv-mixed.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2024-09-02
Started: 2024-04-03

Cross-validating model selection

Rendered fromcv-selection.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2024-09-15
Started: 2024-04-03

Cross-validating regression models

Rendered fromcv.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2024-09-04
Started: 2023-08-03

Extending the cv package

Rendered fromcv-extend.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2024-09-05
Started: 2023-08-25

Readme and manuals

Help Manual

Help pageTopics
Cross-Validate Regression Modelsas.data.frame.cv as.data.frame.cvList cv cv.default cv.glm cv.lm cv.rlm cvInfo cvInfo.cv cvInfo.cvList cvInfo.cvModList plot.cv plot.cvList print.cv print.cvDataFrame print.cvList summary.cv summary.cvDataFrame summary.cvList
Cross-Validate a Model-Selection Procedurecoef.cvSelect compareFolds cv.function cvInfo.cvSelect selectModelList selectStepAIC selectTrans selectTransStepAIC
Cross-Validate Mixed-Effects Modelcv.glmmTMB cv.lme cv.merMod
Cross-Validate Several Models Fit to the Same Dataas.data.frame.cvModList cv.modList models plot.cvModList print.cvModList summary.cvModList
Utility Functions for the cv PackagecvCompute cvMixed cvSelect fold fold.folds folds GetResponse GetResponse.default GetResponse.glmmTMB GetResponse.lme GetResponse.merMod GetResponse.modList print.folds
Cost Functions for Fitted Regression ModelsBayesRule BayesRule2 costFunctions medAbsErr mse rmse
Body Weights of 48 Pigs in 9 Successive WeeksPigs