results_pcrem

<p>Simulated data for a piecewise crossed random effects model (PCREM) using <code>SimData_CREM</code>. Included to demonstrate the use of <code>summary.CREM()</code>.</p>

datasets

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>).

Corissa T. Rohloff

BEND

Bayesian Estimation of Nonlinear Data (BEND)

Rik Lamm

Yadira Peralta

Nidhi Kohli

Eric F. Lock

results_pcrem function

<p>A list (an object of class <code>CREM</code>) with fitted model results.</p>

Format

Fitted results for a PCREM — results_pcrem

Fitted results for a PCREM

results_pcrem: Fitted results for a PCREM

Description

Usage

Arguments

Format