Bayesian Estimation of Nonlinear Data (BEND) R package BEND version 2.0.1 Provides 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) <https://hdl.handle.net/11299/252533> and Lock et al., (2018) <doi:10.1007/s11336-017-9594-5>), 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) <doi:10.1111/bmsp.12334>), and 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) <doi:10.1037/met0000358>). Software Depends: R (>= 3.6.3) Imports: coda (>= 0.19.4.1), label.switching (>= 1.8), rjags (>= 4.17) Corissa T. Rohloff <corissa.wurth@gmail.com> Comprehensive R Archive Network (CRAN) Corissa T. Rohloff [aut, cre] (ORCID: <https://orcid.org/0000-0003-3228-4653>), Rik Lamm [aut] (ORCID: <https://orcid.org/0000-0002-3317-6243>), Yadira Peralta [aut] (ORCID: <https://orcid.org/0000-0003-4823-6939>), Nidhi Kohli [aut] (ORCID: <https://orcid.org/0000-0003-4690-2854>), Eric F. Lock [aut] (ORCID: <https://orcid.org/0000-0003-4663-2356>) MIT + file LICENSE (https://CRAN.R-project.org/package=BEND/LICENSE) 2026-04-19 application/tgz https://CRAN.R-project.org/package=BEND doi:10.32614/CRAN.package.BEND