BEND: Bayesian Estimation of Nonlinear Data (BEND)

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>).

Version:	1.1
Depends:	R (≥ 3.6.3)
Imports:	coda (≥ 0.19.4), label.switching (≥ 1.8), rjags (≥ 4.14)
Published:	2025-09-24
DOI:	10.32614/CRAN.package.BEND
Author:	Corissa T. Rohloff [aut, cre], Rik Lamm [aut], Yadira Peralta [aut], Nidhi Kohli [aut], Eric F. Lock [aut]
Maintainer:	Corissa T. Rohloff <corissa.wurth at gmail.com>
BugReports:	https://github.com/crohlo/BEND/issues
License:	MIT + file LICENSE
URL:	https://github.com/crohlo/BEND
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	BEND results

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