Package: mvPred 0.1.0
Billy Ouattara
mvPred: Methods for Handling Missing Values in Linear Modeling
Provides user-friendly methods for handling missing data in regression modeling, including available-case linear regression, multiple imputation, random-forest imputation, and the Tower method. Implemented approaches include chained-equation imputation described by van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>, multiple imputation described by Honaker, King and Blackwell (2011) <doi:10.18637/jss.v045.i07>, random-forest imputation described by Stekhoven and Buehlmann (2012) <doi:10.1093/bioinformatics/btr597>, and the Tower method described by Matloff and Mohanty (2023) <https://CRAN.R-project.org/package=toweranNA>.
Authors:
mvPred_0.1.0.tar.gz
mvPred_0.1.0.zip(r-4.7-any)mvPred_0.1.0.zip(r-4.6-any)mvPred_0.1.0.zip(r-4.5-any)
mvPred_0.1.0.tgz(r-4.6-any)mvPred_0.1.0.tgz(r-4.5-any)
mvPred_0.1.0.tar.gz(r-4.7-any)mvPred_0.1.0.tar.gz(r-4.6-any)
mvPred_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mvPred/json (API)
| # Install 'mvPred' in R: |
| install.packages('mvPred', repos = c('https://matloff.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/matloff/mvpred/issues
Last updated from:5021b9ac21. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 212 | ||
| source / vignettes | OK | 318 | ||
| linux-release | OK | 237 | ||
| macos-release | OK | 125 | ||
| macos-oldrel | OK | 166 | ||
| windows-devel | OK | 127 | ||
| windows-release | OK | 136 | ||
| windows-oldrel | OK | 127 | ||
| wasm-release | OK | 184 |
Exports:ac_mulac_vecaddListElementbootstrapcompCaseslm_aclm_comparelm_prefilllm_towerqeMLna
Dependencies:abindAmeliabackportsbase64encBHbitbit64bootbroombslibcachemcarcarDataclicliprcodetoolscolorspacecowplotcpp11crayondata.tableDerivDiceKrigingdigestdoBydoRNGdplyrevaluatefarverfastmapFNNFOCIfontawesomeforcatsforeachforecastforeignFormulafracdifffsgbmgenericsggplot2glmnetgluegmpgrfgtablegtoolshavenhighrhmshtmltoolsisobanditeratorsitertoolsjomojquerylibjsonliteknitrlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmemoisemgcvmicemimeminqamissForestmitmlmodelrmvtnormnlmenloptrNLPnnetnumDerivordinalpanpartoolspbkrtestpdistpillarpkgconfigpolyregprettyunitsprogressproxypurrrqeMLquantregR.methodsS3R.ooR.utilsR6randomForestrangerRANNrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrreformulasregtoolsrjerlangrmarkdownrngtoolsrpartrpart.plotS7sandwichsassscalesshapeslamSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDatetinytextmtoweranNAtuftetzdbucminfurcautf8vctrsviridisLitevroomwithrxfunxml2yamlzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Datasets Included in mvPred | auto_mpg english NHkids |
| Cross-Validation for Predictive Modeling with Missing Data | bootstrap |
| Extract Complete Cases from a Data Frame | compCases |
| Linear Regression Using Available Cases Methods | ac_mul ac_vec lm_ac predict.lm_ac summary.lm_ac |
| Compare Missing-Data Methods with Bootstrap Summaries | lm_compare |
| Linear Model Fitting with Pre-Imputation of Missing Data | lm_prefill predict.lm_prefill summary.lm_prefill |
| Fit Linear Models with Missing Data Using the Tower Method | lm_tower predict.lm_tower summary.lm_tower |
| Interface qeML Models with Missing-Value Preprocessing | addListElement qeMLna |