Package: nestedcv 0.7.12
nestedcv: Nested Cross-Validation with 'glmnet' and 'caret'
Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package <doi:10.1093/bioadv/vbad048>. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.
Authors:
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nestedcv.pdf |nestedcv.html✨
nestedcv/json (API)
NEWS
# Install 'nestedcv' in R: |
install.packages('nestedcv', repos = c('https://myles-lewis.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/myles-lewis/nestedcv/issues
Last updated 1 days agofrom:9c5da9bbe2. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 05 2024 |
R-4.5-win | OK | Nov 05 2024 |
R-4.5-linux | OK | Nov 05 2024 |
R-4.4-win | OK | Nov 05 2024 |
R-4.4-mac | OK | Nov 05 2024 |
R-4.3-win | OK | Nov 05 2024 |
R-4.3-mac | OK | Nov 05 2024 |
Exports:anova_filterbarplot_var_stabilitybin_stat_filterboot_anovaboot_correlboot_filterboot_lmboot_ttestboot_wilcoxonboruta_filterboxplot_expressionclass_balanceclass_stat_filtercollinearcombo_filtercor_stat_filtercorrel_filtercorrels2cv_coefcv_varImpcva.glmnetglmnet_coefsglmnet_filterhist_var_ranksinnercv_predsinnercv_rocinnercv_summarylm_filtermccmcc_multimetricsmodel.hsstannestcv.glmnetnestcv.SuperLearnernestcv.trainone_hotoutercvplot_alphasplot_caretplot_lambdasplot_shap_barplot_shap_beeswarmplot_var_ranksplot_var_stabilityplot_varImppls_filterprcpred_nestcv_glmnetpred_nestcv_glmnet_class1pred_nestcv_glmnet_class2pred_nestcv_glmnet_class3pred_SuperLearnerpred_trainpred_train_class1pred_train_class2pred_train_class3predSummaryrandomsampleranger_filterrelieff_filterrepeatcvrepeatfoldsrf_filterslimsmotestat_filtersummary_varssupervisedPCAtrain_predstrain_roctrain_summaryttest_filtertxtProgressBar2var_directionvar_stabilityweightwilcoxon_filter
Dependencies:bitopscaretcaToolsclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdoParalleldplyre1071fansifarverforeachfuturefuture.applygenericsggplot2glmnetglobalsgluegowergplotsgtablegtoolshardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmatrixStatsmatrixTestsmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppArmadilloRcppEigenRcppGSLRcppParallelRcppZigguratrecipesreshape2RfastRhpcBLASctlrlangROCRrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
Explaining nestedcv models with Shapley values
Rendered fromnestedcv_shap.Rmd
usingknitr::rmarkdown
on Nov 05 2024.Last update: 2024-03-12
Started: 2023-03-31
nestedcv
Rendered fromnestedcv.Rmd
usingknitr::rmarkdown
on Nov 05 2024.Last update: 2024-08-01
Started: 2022-03-14
Using outercv with Bayesian shrinkage models
Rendered fromnestedcv_hsstan.Rmd
usingknitr::rmarkdown
on Nov 05 2024.Last update: 2024-03-12
Started: 2023-03-25