Package: BEND 1.0

Corissa T. Rohloff

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]

BEND_1.0.tar.gz
BEND_1.0.tar.gz(r-4.5-noble)BEND_1.0.tar.gz(r-4.4-noble)
BEND_1.0.tgz(r-4.4-emscripten)BEND_1.0.tgz(r-4.3-emscripten)
BEND.pdf |BEND.html✨
BEND/json (API)
NEWS

# Install 'BEND' in R:
install.packages('BEND', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/crohlo/bend/issues0 issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

jagscpp

1.70 score 215 downloads 4 exports 6 dependencies

Last updated 1 years agofrom:13a5ec9842. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 31 2025
R-4.5-linuxOKMar 31 2025
R-4.4-linuxOKMar 31 2025

Exports:Bayes_BPREMBayes_CREMBayes_PREMplot_BEND

Dependencies:codacombinatlabel.switchinglatticelpSolverjags

Citation

To cite package ‘BEND’ in publications use:

Rohloff C, Lamm R, Peralta Y, Kohli N, Lock E (2024). BEND: Bayesian Estimation of Nonlinear Data (BEND). R package version 1.0, https://CRAN.R-project.org/package=BEND.

Corresponding BibTeX entry: 

  @Manual{,
    title = {BEND: Bayesian Estimation of Nonlinear Data (BEND)},
    author = {Corissa T. Rohloff and Rik Lamm and Yadira Peralta and
      Nidhi Kohli and Eric F. Lock},
    year = {2024},
    note = {R package version 1.0},
    url = {https://CRAN.R-project.org/package=BEND},
  }

Readme and manuals

BEND

The goal of BEND is to provide 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 for more details).

  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 for more details).

  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 for more details).

This package requires Just Another Gibbs Sampler (JAGS) to be installed on your computer (https://mcmc-jags.sourceforge.io/), and depends on the packages rjags and label.switching.

References

Lamm, R. (2022). Incorporation of covariates in Bayesian piecewise growth mixture models. https://hdl.handle.net/11299/252533

Lock, E. F., Kohli, N., & Bose, M. (2018). Detecting multiple random changepoints in Bayesian piecewise growth mixture models. Psychometrika, 83(3), 733–750. https://doi.org/10.1007/s11336-017-9594-5

Peralta, Y., Kohli, N., Lock, E. F., & Davison, M. L. (2022). Bayesian modeling of associations in bivariate piecewise linear mixed-effects models. Psychological Methods, 27(1), 44–64. https://doi.org/10.1037/met0000358

Rohloff, C. T., Kohli, N., & Lock, E. F. (2024). Identifiability and estimability of Bayesian linear and nonlinear crossed random effects models. British Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/bmsp.12334

Installation

You can install the development version of BEND from GitHub with:

# install.packages("devtools")
library(devtools)
install_github("crohlo/BEND")