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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Modeling psychological time series with multilevel hidden Markov models: A tutorial.

Emmeke Aarts1, Jonas Haslbeck2

  • 1Department of Methodology and Statistics, Utrecht University.

Psychological Methods
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

Hidden Markov models (HMMs) offer advanced analysis for psychological time series data, capturing complex dynamics beyond linear models. This tutorial introduces HMMs and their application using R, enhancing researchers' methodological toolkit.

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Area of Science:

  • Psychological science
  • Behavioral dynamics
  • Quantitative psychology

Background:

  • Time series data are increasingly vital in psychological science for granular analysis of human functioning.
  • Traditional linear models struggle with complex behavioral dynamics, such as multiple equilibrium states or non-normal distributions.
  • Hidden Markov models (HMMs) provide a powerful framework for modeling these complex dynamics in time series data.

Purpose of the Study:

  • To introduce researchers to the application of hidden Markov models (HMMs) for analyzing psychological time series data.
  • To address the limited familiarity, software availability, and complexity challenges hindering HMM adoption in psychology.
  • To provide a practical, reproducible tutorial for HMM analysis using the R-package mHMMbayes.

Main Methods:

  • Utilizing hidden Markov models (HMMs) to analyze intensive longitudinal data.
  • Employing the mHMMbayes R package for multilevel HMM analysis.
  • Demonstrating a step-by-step reproducible workflow for HMM estimation, analysis, and reporting.

Main Results:

  • HMMs effectively model complex within-person dynamics, including multiple latent states and transitions.
  • The tutorial successfully guides users through applying HMMs to psychological time series.
  • The mHMMbayes package facilitates the estimation and analysis of multilevel HMMs.

Conclusions:

  • HMMs are a valuable, underutilized tool for psychological time series research.
  • Increased familiarity and accessible tutorials can promote the adoption of HMMs.
  • This work empowers researchers to incorporate HMMs into their analytical methods for richer insights into human functioning.