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betadelta

betaDelta: Confidence Intervals for Standardized Regression Coefficients

betamc

betaMC: Generates Monte Carlo confidence intervals for standardized regression coefficients for models fitted by lm().

betanb

betaNB: Generates nonparametric bootstrap confidence intervals for standardized regression coefficients and other effect sizes for models fitted by lm().

betasandwich

betaSandwich: Robust Confidence Intervals for Standardized Regression Coefficients

ctmed

Calculates standard errors and confidence intervals for effects in continuous time mediation models.

docker-dynr-arma-rocker

A Docker container for R projects based on the Rocker Project with the dynr (arma branch) package.

docker-dynr-rocker

A Docker container for R projects based on the Rocker Project with the dynr package.

docker-dynr-rocker-sandbox

docker-manbetaciwald-rocker

A rocker/rstudio container for the manBetaCIWald project.

docker-manmcmedmiss

Docker and Apptainer/Singularity containers for the manMCMedMiss project based on Arch Linux.

docker-manmcmedmiss-rocker

A Docker container for the manMCMedMiss project based on the Rocker Project.

dotfiles

dynr

Dynamic Modeling in R

dynr-wiki

dynr Wiki

dynrautovar

dynutils

Utility functions for data preparation when fitting dynamic models.

jeksterslab.r-universe.dev

manbetaciwald

Research compendium for the manuscript Pesigan, I. J. A., Sun, R. W., & Cheung, S. F. (2023). betaDelta and betaSandwich: Confidence intervals for standardized regression coefficients in R. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2023.2201277

manctmed

manmcmedemadyn

manmcmedmiss

Research compendium for the manuscript Pesigan, I. J. A., & Cheung, S. F. (2023). Monte Carlo confidence intervals for the indirect effect with missing data. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02114-4

metavar

semmcci

semmcci: Monte Carlo Confidence Intervals in Structural Equation Modeling

simstatespace

Provides a streamlined and user-friendly framework for simulating data in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. For an introduction to state space models in social and behavioral sciences, refer to Chow, Ho, Hamaker, and Dolan (2010).