The goal of BEND is to provide a set of models to estimate nonlinear longitudinal data using Bayesian estimation methods. These models include the:

  1. Bayesian Piecewise Random Effects Model (Bayes_PREM()) which estimates a piecewise random effects (mixture) model for a given number of latent classes and a latent number of possible changepoints in each class, and can incorporate class and outcome predictive covariates (see Lamm, 2022 and Lock et al., 2018 for more details).

  2. Bayesian Crossed Random Effects Model (Bayes_CREM()) which estimates a linear, quadratic, exponential, or piecewise crossed random effects models where individuals are changing groups over time (e.g., students and schools; see Rohloff et al., 2024 for more details).

  3. Bayesian Bivariate Piecewise Random Effects Model (Bayes_BPREM()) which estimates a bivariate piecewise random effects model to jointly model two related outcomes (e.g., reading and math achievement; see Peralta et al., 2022 for more details).

This package requires Just Another Gibbs Sampler (JAGS) to be installed on your computer (, and depends on the packages rjags and label.switching.


Lamm, R. (2022). Incorporation of covariates in Bayesian piecewise growth mixture models.

Lock, E. F., Kohli, N., & Bose, M. (2018). Detecting multiple random changepoints in Bayesian piecewise growth mixture models. Psychometrika, 83(3), 733–750.

Peralta, Y., Kohli, N., Lock, E. F., & Davison, M. L. (2022). Bayesian modeling of associations in bivariate piecewise linear mixed-effects models. Psychological Methods, 27(1), 44–64.

Rohloff, C. T., Kohli, N., & Lock, E. F. (2024). Identifiability and estimability of Bayesian linear and nonlinear crossed random effects models. British Journal of Mathematical and Statistical Psychology.


You can install the development version of BEND from GitHub with:

# install.packages("devtools")