Package: SIMPLE.REGRESSION 0.2.1

SIMPLE.REGRESSION: OLS, Moderated, Logistic, and Count Regressions Made Simple

Provides SPSS- and SAS-like output for least squares multiple regression, logistic regression, and count variable regressions. Detailed output is also provided for OLS moderated regression, interaction plots, and Johnson-Neyman regions of significance. The output includes standardized coefficients, partial and semi-partial correlations, collinearity diagnostics, plots of residuals, and detailed information about simple slopes for interactions. The output for some functions includes Bayes Factors and, if requested, regression coefficients from Bayesian Markov Chain Monte Carlo analyses. There are numerous options for model plots. The REGIONS_OF_SIGNIFICANCE function also provides Johnson-Neyman regions of significance and plots of interactions for both lm and lme models.

Authors:Brian P. O'Connor [aut, cre]

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SIMPLE.REGRESSION.pdf |SIMPLE.REGRESSION.html
SIMPLE.REGRESSION/json (API)

# Install 'SIMPLE.REGRESSION' in R:
install.packages('SIMPLE.REGRESSION', repos = c('https://bpoconnor.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

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

1.91 score 4.1k downloads 8 exports 126 dependencies

Last updated 1 months agofrom:b49be40672. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 11 2025
R-4.5-winOKFeb 11 2025
R-4.5-macOKFeb 11 2025
R-4.5-linuxOKFeb 11 2025
R-4.4-winOKFeb 11 2025
R-4.4-macOKFeb 11 2025
R-4.3-winOKFeb 11 2025
R-4.3-macOKFeb 11 2025

Exports:COUNT_REGRESSIONLOGISTIC_REGRESSIONMODERATED_REGRESSIONOLS_REGRESSIONPARTIAL_COEFSPLOT_MODELREGIONS_OF_SIGNIFICANCESIMPLE.REGRESSION

Dependencies:abindbackportsbase64encBayesFactorbayesplotBHbootbslibcachemcallrcheckmateclicodacolorspacecolourpickercommonmarkcontfraccpp11crayoncrosstalkdescdeSolvedigestdistributionaldplyrDTdygraphsellipticevaluatefansifarverfastmapfontawesomefsgenericsggplot2ggridgesgluegridExtragtablegtoolshighrhtmltoolshtmlwidgetshttpuvhypergeoigraphinlineisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelme4loomagrittrmarkdownMASSMatrixMatrixModelsmatrixStatsmemoisemgcvmimeminiUIminqamunsellmvtnormnlmenloptrnumDerivpbapplypillarpkgbuildpkgconfigplyrposteriorprocessxpromisespspsclQuickJSRR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRcppParallelRdpackreformulasreshape2rlangrmarkdownrstanrstanarmrstantoolssassscalesshinyshinyjsshinystanshinythemessourcetoolsStanHeadersstringistringrsurvivaltensorAthreejstibbletidyselecttinytexutf8vctrsviridisLitewithrxfunxtablextsyamlzoo

Readme and manuals

Help Manual

Help pageTopics
SIMPLE.REGRESSIONSIMPLE.REGRESSION-package
Count data regressionCOUNT_REGRESSION
data_Bauer_Curran_2005data_Bauer_Curran_2005
data_Bodner_2016data_Bodner_2016
data_Chapman_Little_2016data_Chapman_Little_2016
data_Cohen_Aiken_West_2003_7data_Cohen_Aiken_West_2003_7
data_Cohen_Aiken_West_2003_9data_Cohen_Aiken_West_2003_9
data_Green_Salkind_2014data_Green_Salkind_2014
data_Halvorson_2022_logdata_Halvorson_2022_log
data_Halvorson_2022_poisdata_Halvorson_2022_pois
data_Huitema_2011data_Huitema_2011
data_Kremelburg_2011data_Kremelburg_2011
data_Lorah_Wong_2018data_Lorah_Wong_2018
data_Meyers_2013data_Meyers_2013
data_OConnor_Dvorak_2001data_OConnor_Dvorak_2001
data_Orme_2009_2data_Orme_2009_2
data_Orme_2009_5data_Orme_2009_5
data_Pedhazur_1997data_Pedhazur_1997
data_Pituch_Stevens_2016data_Pituch_Stevens_2016
Logistic regressionLOGISTIC_REGRESSION
Moderated multiple regressionMODERATED.REGRESSION MODERATED_REGRESSION
Ordinary least squares regressionOLS_REGRESSION SIMPLE.REGRESSION
Standardized coefficients and partial correlations for multiple regressionPARTIAL_COEFS
Plots predicted values for a regression modelPLOT_MODEL
Plots of Johnson-Neyman regions of significance for interactionsREGIONS_OF_SIGNIFICANCE