Package: prettyglm 1.0.1
prettyglm: Pretty Summaries of Generalized Linear Model Coefficients
One of the main advantages of using Generalised Linear Models is their interpretability. The goal of 'prettyglm' is to provide a set of functions which easily create beautiful coefficient summaries which can readily be shared and explained. 'prettyglm' helps users create coefficient summaries which include categorical base levels, variable importance and type III p.values. 'prettyglm' also creates beautiful relativity plots for categorical, continuous and splined coefficients.
Authors:
prettyglm_1.0.1.tar.gz
prettyglm_1.0.1.zip(r-4.5)prettyglm_1.0.1.zip(r-4.4)prettyglm_1.0.1.zip(r-4.3)
prettyglm_1.0.1.tgz(r-4.4-any)prettyglm_1.0.1.tgz(r-4.3-any)
prettyglm_1.0.1.tar.gz(r-4.5-noble)prettyglm_1.0.1.tar.gz(r-4.4-noble)
prettyglm_1.0.1.tgz(r-4.4-emscripten)prettyglm_1.0.1.tgz(r-4.3-emscripten)
prettyglm.pdf |prettyglm.html✨
prettyglm/json (API)
NEWS
# Install 'prettyglm' in R: |
install.packages('prettyglm', repos = c('https://jared-fowler.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jared-fowler/prettyglm/issues
classificationclassification-modeldata-sciencedata-visualizationglmlinear-modelsregressionregression-analysisregression-modelregression-modelsstatistical-models
Last updated 10 months agofrom:ffe3d35876. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 01 2024 |
R-4.5-win | OK | Nov 01 2024 |
R-4.5-linux | OK | Nov 01 2024 |
R-4.4-win | OK | Nov 01 2024 |
R-4.4-mac | OK | Nov 01 2024 |
R-4.3-win | OK | Nov 01 2024 |
R-4.3-mac | OK | Nov 01 2024 |
Exports:actual_expected_bucketedclean_coefficientscut3one_way_avepredict_outcomepretty_coefficientspretty_relativitiessplineit
Dependencies:abindaskpassbackportsbase64encbootbroombslibcachemcarcarDataclicodetoolscolorspacecowplotcpp11crosstalkcurldata.tableDerivdigestdoBydplyrevaluatefansifarverfastmapfontawesomeforcatsforeachFormulafsgenericsggplot2gluegtablehardhathighrhtmltoolshtmlwidgetshttrisobanditeratorsjquerylibjsonlitekableExtraknitrlabelinglaterlatticelazyevallifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmunsellnlmenloptrnnetnumDerivopensslpbkrtestpillarpkgconfigplotlypromisespurrrquantregR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownrstudioapisassscalesSparseMstringistringrsurvivalsvglitesyssystemfontstibbletidycattidyrtidyselecttinytexutf8vctrsvipviridisLitewithrxfunxml2yamlyardstick
Readme and manuals
Help Manual
Help page | Topics |
---|---|
actual_expected_bucketed | actual_expected_bucketed |
Bank marketing campaigns data set analysis | bank_data |
clean_coefficients | clean_coefficients |
cut3 | cut3 |
one_way_ave | one_way_ave |
predict_outcome | predict_outcome |
pretty_coefficients | pretty_coefficients |
pretty_relativities | pretty_relativities |
splineit | splineit |
Titanic Data | titanic |