Blimp

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.

http://www.appliedmissingdata.com/multilevel-imputation.html

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.

https://cran.r-project.org/web/packages/boot.heterogeneity/index.html

n.intensive

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)

https://ucla.box.com/s/j41idz4awhcn2n5k82p4vkb1027mq21q

Du, H., & Wang, L. (2018). Investigating reliabilities of intraindividual variability indicators with autocorrelated longitudinal data. Multivariate Behavioral Research, 53(4), 502-520.

BALM

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)

https://ucla.box.com/s/8507h6a1utake2fohh0bb51chgg4uqqo

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.

pas

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)

https://ucla.box.com/s/glmci2mpybnqmvaiqjb4e1q55g878g71

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.