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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: | 1.0 |
Depends: | R (≥ 3.6.3) |
Imports: | coda (≥ 0.19.4), label.switching (≥ 1.8), rjags (≥ 4.14) |
Published: | 2024-03-23 |
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 |
Reference manual: | BEND.pdf |
Package source: | BEND_1.0.tar.gz |
Windows binaries: | r-devel: BEND_1.0.zip, r-release: BEND_1.0.zip, r-oldrel: BEND_1.0.zip |
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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.