Package: weights 1.1.2

weights: Weighting and Weighted Statistics

Provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squared statistics, histograms, and t-tests as well as simple weighting graphics including weighted histograms, box plots, bar plots, and violin plots. Also includes software for quickly recoding survey data and plotting estimates from interaction terms in regressions (and multiply imputed regressions) both with and without weights and summarizing various types of regressions. Some portions of this package were assisted by AI-generated suggestions using OpenAI's GPT model, with human review and integration.

Authors:Josh Pasek [aut, cre], Alex Tahk [ctb], Gene Culter [ctb], Marcus Schwemmle [ctb], Some code modified from R-core [ctb]

weights_1.1.2.tar.gz
weights_1.1.2.zip(r-4.7)weights_1.1.2.zip(r-4.6)weights_1.1.2.zip(r-4.5)
weights_1.1.2.tgz(r-4.6-x86_64)weights_1.1.2.tgz(r-4.6-arm64)weights_1.1.2.tgz(r-4.5-x86_64)weights_1.1.2.tgz(r-4.5-arm64)
weights_1.1.2.tar.gz(r-4.7-arm64)weights_1.1.2.tar.gz(r-4.7-x86_64)weights_1.1.2.tar.gz(r-4.6-arm64)weights_1.1.2.tar.gz(r-4.6-x86_64)
weights_1.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
weights/json (API)

# Install 'weights' in R:
install.packages('weights', repos = c('https://jmping.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • anes04 - Demographic Data From 2004 American National Election Studies

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

6.49 score 44 packages 730 scripts 11k downloads 3 mentions 34 exports 105 dependencies

Last updated from:2d33f5ca38. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK198
linux-devel-x86_64OK181
source / vignettesOK205
linux-release-arm64OK177
linux-release-x86_64OK235
macos-release-arm64OK101
macos-release-x86_64OK244
macos-oldrel-arm64OK112
macos-oldrel-x86_64OK231
windows-develOK180
windows-releaseOK138
windows-oldrelOK129
wasm-releaseOK165

Exports:coefferdummifyfindnfindwtdinteractionfindwtdinteraction.defaultfindwtdinteraction.listfindwtdinteraction.lmerModfindwtdinteraction.mirafindwtdinteraction.multinomnalevsonetableplotinteractpredsplotinteractpreds.defaultplotinteractpreds.interactpredsplotinteractpreds.interactpredsmnlplotwtdinteractionrdstarmakerstdzwpctwtd.anovawtd.barplotwtd.boxplotwtd.chi.sqwtd.corwtd.corswtd.covwtd.histwtd.medianwtd.partial.corwtd.partial.covwtd.t.testwtd.violinplotwtd.xtab

Dependencies:backportsbase64encbitbit64bootbroombslibcachemcheckmateclicliprclustercodetoolscolorspacecpp11crayondata.tabledigestdplyrevaluatefarverfastmapfontawesomeforcatsforeachforeignFormulafsgdatagenericsggplot2glmnetgluegridExtragtablegtoolshavenhighrHmischmshtmlTablehtmltoolshtmlwidgetsisobanditeratorsjomojquerylibjsonliteknitrlabelinglatticelifecyclelme4magrittrMASSMatrixmemoisemicemimeminqamitmlnlmenloptrnnetnumDerivordinalpanpillarpkgconfigprettyunitsprogresspurrrR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackreadrreformulasrlangrmarkdownrpartrstudioapiS7sassscalesshapestringistringrsurvivaltibbletidyrtidyselecttinytextzdbucminfutf8vctrsviridisLitevroomwithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Demographic Data From 2004 American National Election Studies (ANES)anes04
Extract model coefficients with standard errors and significance starscoeffer coeffer.default coeffer.gam coeffer.glmerMod coeffer.glmnet coeffer.lmerMod coeffer.multinom coeffer.polr
Separate a factor into separate dummy variables for each level.dummify
Summarize key model information including sample size and fit statisticsfindn findn.default findn.gam findn.glm findn.glmerMod findn.lm findn.lmerMod findn.multinom findn.polr print.findn
Recode variables to 0-1 scalenalevs
Create a clean regression summary table from one or more modelsonetable
Functions to Identify and Plot Predicted Probabilities As Well As Two- and Three-Way Interactions From Regressions With or Without Weights and Standard Errorsfindwtdinteraction findwtdinteraction.default findwtdinteraction.list findwtdinteraction.lmerMod findwtdinteraction.mira findwtdinteraction.multinom plotinteractpreds plotinteractpreds.default plotinteractpreds.interactpreds plotinteractpreds.interactpredsmnl plotwtdinteraction
Round Numbers To Text With No Leading Zerord
Produce stars from p values for tables.starmaker
Standardizes any numerical vector, with weights.stdz
Provides a weighted table of percentages for any variable.wpct
Weighted one-way ANOVAwtd.anova
Weighted barplotwtd.barplot
Weighted boxplotwtd.boxplot
Produces weighted chi-squared tests.wtd.chi.sq
Produces weighted correlations with standard errors and significance. For a faster version without standard errors and p values, use the 'wtd.cors' function.onecor.wtd wtd.cor
Produces weighted correlations quickly using C.wtd.cors
Produces weighted covariances with standard errors and significance.wtd.cov
Weighted Histogramswtd.hist
Weighted medianwtd.median
Computes weighted partial correlations, controlling for covariateswtd.partial.cor
Computes weighted partial covariances, controlling for covariateswtd.partial.cov
Produces weighted Student's t-tests with standard errors and significance.wtd.t.test
Draw weighted violin plots by groupwtd.violinplot
Weighted cross-tabulations using up to three categorical variableswtd.xtab