arXiv subjects

Nidhi Kohli

Publications and source records attributed to Nidhi Kohli.

2 recordsLinked to original sources

arXiv2026

BEND: An R Package for the Bayesian Estimation of Nonlinear Longitudinal Data

Longitudinal data are useful for capturing and analyzing patterns of change over time. Often, these patterns follow a nonlinear form. One useful and commonly applied nonlinear function is the piecewise function, which assumes growth occurs in distinct phases, each with its own functional form. Past literature has established that Bayesian inference is preferred over likelihood-based methods for estimating piecewise models. To address this, we developed the R package BEND - Bayesian Estimation of Nonlinear Data (available on CRAN). The purpose of BEND is to provide a user friendly software for estimating nonlinear longitudinal models using a Bayesian inference approach. Given the flexibility and practicality of the piecewise models, BEND includes several extensions of it to accommodate various types of complex longitudinal datasets and applications. Bayes_PREM() can empirically identify the number and location of random changepoints in a piecewise random effects model. This function can also model multiple latent classes with different longitudinal growth patterns and incorporate covariates to predict the outcome and latent class membership. Bayes_BPREM() can jointly model the longitudinal piecewise trajectories of two interrelated outcomes. Lastly, Bayes_CREM() can estimate the impact of group membership on longitudinal growth. This paper provides an overview of the functions included in BEND and empirical examples of how to apply these models in practice.

stat.ME↗

arXiv2017

Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models

Piecewise growth mixture models (PGMM) are a flexible and useful class of methods for analyzing segmented trends in individual growth trajectory over time, where the individuals come from a mixture of two or more latent classes. These models allow each segment of the overall developmental process within each class to have a different functional form; examples include two linear phases of growth, or a quadratic phase followed by a linear phase. The changepoint (knot) is the time of transition from one developmental phase (segment) to another. Inferring the location of the changepoint(s) is often of practical interest, along with inference for other model parameters. A random changepoint allows for individual differences in the transition time within each class. The primary objectives of our study are: (1) to develop a PGMM using a Bayesian inference approach that allows the estimation of multiple random changepoints within each class; (2) to develop a procedure to empirically detect the number of random changepoints within each class; and (3) to empirically investigate the bias and precision of the estimation of the model parameters, including the random changepoints, via a simulation study. We have developed the user-friendly package BayesianPGMM for R to facilitate the adoption of this methodology in practice, which is available at https://github.com/lockEF/BayesianPGMM . We describe an application to mouse-tracking data for a visual recognition task.

stat.ME↗