Package: BEND 1.0

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>).

Authors:Corissa T. Rohloff [aut, cre], Rik Lamm [aut], Yadira Peralta [aut], Nidhi Kohli [aut], Eric F. Lock [aut]

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BEND.pdf |BEND.html✨
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Bug tracker:https://github.com/crohlo/bend/issues

On CRAN:

4 exports 0.93 score 6 dependencies 234 downloads

Last updated 6 months agofrom:9cdd67a08e. Checks:OK: 7. Indexed: yes.

Exports:Bayes_BPREMBayes_CREMBayes_PREMplot_BEND

Dependencies:codacombinatlabel.switchinglatticelpSolverjags