Blimp was originally designed as a multiple imputation program, but the application now offers general-purpose Bayesian estimation for a wide range of single-level and multilevel regression models with two or three levels, with or without missing data. Algorithmic development by Craig Enders, Brian Keller, and Han Du. C++ programming by Brian Keller. Qt graphical user interface development by Brian Keller and Behrouz NematiPour.
R Packages and Functions
Boot.heterogeneity: A Bootstrap-Based Heterogeneity Test for Meta-Analysis
Implements a bootstrap-based heterogeneity test for standardized mean differences (d), Fisher-transformed Pearson’s correlations (r), and natural-logarithm-transformed odds ratio (or) in meta-analysis studies. Depending on the presence of moderators, this Monte Carlo based test can be implemented in the random- or mixed-effects model.
An R function for calculating the reliability estimate for each indicator (such as intraindividual standard deviation and autocorrelation coefficient) with autocorrelated longitudinal data by Du and Wang (2018)
Du, H., & Wang, L. (2018). Investigating reliabilities of intraindividual variability indicators with autocorrelated longitudinal data. Multivariate Behavioral Research, 53(4), 502-520.
An R function for Bayesian fill-in meta-analysis (BALM)
method to adjust publication bias and estimate population effect size that accommodates different assumptions for publication bias by Du, Liu, and Wang (2017)
Du, H., Liu, F., & Wang, L. (2017). A Bayesian” fill-in” method for correcting for publication bias in meta-analysis. Psychological Methods, 22(4), 799-817.
An R function for implementing the proposed Bayesian power analysis procedure with considering uncertainty in the effect size estimates from a meta-analysis by Du and Wang (2016)
Du, H., & Wang, L. (2016). A Bayesian power analysis procedure considering uncertainty in effect size estimates from a meta-analysis. Multivariate Behavioral Research, 51(1), 589-605.