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) <> 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: 1.0
Depends: R (≥ 3.6.3)
Imports: coda (≥ 0.19.4), label.switching (≥ 1.8), rjags (≥ 4.14)
Published: 2024-03-23
Author: Corissa T. Rohloff ORCID iD [aut, cre], Rik Lamm ORCID iD [aut], Yadira Peralta ORCID iD [aut], Nidhi Kohli ORCID iD [aut], Eric F. Lock ORCID iD [aut]
Maintainer: Corissa T. Rohloff <corissa.wurth at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: BEND results


Reference manual: BEND.pdf


Package source: BEND_1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BEND_1.0.tgz, r-oldrel (arm64): BEND_1.0.tgz, r-release (x86_64): BEND_1.0.tgz, r-oldrel (x86_64): BEND_1.0.tgz


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