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betaMC (version 1.3.3)

Monte Carlo for Regression Effect Sizes

Description

Generates Monte Carlo confidence intervals for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm(). 'betaMC' combines ideas from Monte Carlo confidence intervals for the indirect effect (Pesigan and Cheung, 2024 ) and the sampling covariance matrix of regression coefficients (Dudgeon, 2017 ) to generate confidence intervals effect sizes in regression.

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Install

install.packages('betaMC')

Monthly Downloads

430

Version

1.3.3

License

MIT + file LICENSE

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Maintainer

Ivan Jacob Agaloos Pesigan

Last Published

October 19th, 2025

Functions in betaMC (1.3.3)

print.betamc

Print Method for an Object of Class betamc
vcov.betamc

Sampling Variance-Covariance Matrix Method for an Object of Class betamc
nas1982

1982 National Academy of Sciences Doctoral Programs Data
confint.betamc

Confidence Intervals Method for an Object of Class betamc
print.mc

Print Method for an Object of Class mc
summary.mc

Summary Method for an Object of Class mc
summary.betamc

Summary Method for an Object of Class betamc
coef.betamc

Estimated Parameter Method for an Object of Class betamc
BetaMC

Estimate Standardized Regression Coefficients and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method
SCorMC

Estimate Semipartial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method
betaMC-package

betaMC: Monte Carlo for Regression Effect Sizes
DeltaRSqMC

Estimate Improvement in R-Squared and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method
DiffBetaMC

Estimate Differences of Standardized Slopes and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method
MCMI

Generate the Sampling Distribution of Regression Parameters Using the Monte Carlo Method for Data with Missing Values
RSqMC

Estimate Multiple Correlation Coefficients (R-Squared and Adjusted R-Squared) and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method
PCorMC

Estimate Squared Partial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method
MC

Generate the Sampling Distribution of Regression Parameters Using the Monte Carlo Method