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Statistical software and Monte Carlo simulations in R

To simplify the application of new statistical methods in practice, their user-friendly implementation is central. Monte Carlo methods are also often used to investigate how well certain statistical techniques and methods work in practice (especially when not all assumptions for their application are met). To simplify both the application and Monte Carlo simulations, we are working on several R packages that are freely available as open-source software. An overview as well as download options can be found here: Software

Relevant publications:

• Heck, D. W., Arnold, N. R., & Arnold, D. (2018). TreeBUGS: An R package for hierarchical multinomial-processing-tree modeling. Behavior Research Methods, 50, 264-284. https://doi.org/10.3758/s13428-017-0869-7
• Heck, D. W., & Gronau, Q. F. (2017). metaBMA: Bayesian model averaging for random- and fixed-effects meta-analysis. https://cran.r-project.org/package=metaBMA
• Heck, D. W., Overstall, A., Gronau, Q. F., & Wagenmakers, E. (2019). Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models. Statistics & Computing, 29, 631-643. https://doi.org/10.1007/s1
• Heck, D. W., & Davis-Stober, C. P. (2019). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference. Journal of Mathematical Psychology, 91, 70-87.
  https://doi.org/10.1016/j.jmp.2019.03.0041222-018-9828-0
• Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and regression analyses of randomized response data. Journal of Statistical Software, 85(2), 1-29. https://doi.org/10.18637/jss.v085.i02