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:Jared Fowler [cre, aut]

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prettyglm.pdf |prettyglm.html✨
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NEWS

Peer review:

Bug tracker:https://github.com/jared-fowler/prettyglm/issues

On CRAN:

classificationclassification-modeldata-sciencedata-visualizationglmlinear-modelsregressionregression-analysisregression-modelregression-modelsstatistical-models

8 exports 3 stars 1.19 score 105 dependencies 36 scripts 278 downloads

Last updated 8 months agofrom:ffe3d35876. Checks:OK: 7. Indexed: yes.

Exports:actual_expected_bucketedclean_coefficientscut3one_way_avepredict_outcomepretty_coefficientspretty_relativitiessplineit

Dependencies:abindaskpassbackportsbase64encbootbroombslibcachemcarcarDataclicodetoolscolorspacecowplotcpp11crosstalkcurldata.tableDerivdigestdoBydplyrevaluatefansifarverfastmapfontawesomeforcatsforeachfsgenericsggplot2gluegtablehardhathighrhtmltoolshtmlwidgetshttrisobanditeratorsjquerylibjsonlitekableExtraknitrlabelinglaterlatticelazyevallifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmunsellnlmenloptrnnetnumDerivopensslpbkrtestpillarpkgconfigplotlypromisespurrrquantregR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownrstudioapisassscalesSparseMstringistringrsurvivalsvglitesyssystemfontstibbletidycattidyrtidyselecttinytexutf8vctrsvipviridisLitewithrxfunxml2yamlyardstick