Abstract

Change point models are a diverse set of non-regular models that all depend on change points or thresholds. Many software implementations exist for change point models that are aimed at detecting structural changes in a time series. Motivated by non-time series biometrical applications, the R package \textit{chngpt} provides estimation and hypothesis testing functionalities for four variants of change point regression models that allow covariates not subject to thresholding. We illustrate its usage with a real example from the study of immune responses associated with reduction in mother-to-child-transmission of HIV-1 viruses.

Disciplines

Biostatistics

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Biostatistics Commons

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