Hierarchical Multinomial Processing Tree Modeling
Bayes Factors for Simple (Nonhierarchical) MPT Models
Bayes Factor for Slope Parameters in Latent-Trait MPT
Fit a Hierarchical Beta-MPT Model
C++ Sampler for Hierarchical Beta-MPT Model
Between-Subject Comparison of Parameters
Posterior Distribution for Correlations
Extend MCMC Sampling for MPT Model
Generate Data for Beta MPT Models
Generate MPT Frequencies
Generate Data for Latent-Trait MPT Models
Get Mean Parameters per Group
Get Parameter Posterior Statistics
Get Posterior Samples from Fitted MPT Model
Marginal Likelihood for Simple MPT
Plot Convergence for Hierarchical MPT Models
Plot Distribution of Individual Estimates
Plot Posterior Predictive Mean Frequencies
Plot Raw Frequencies
Plot Parameter Estimates
Plot Prior Distributions
Plot Prior vs. Posterior Distribution
Get Posterior Predictive Samples
Compute Posterior Predictive P-Values
Prior Predictive Samples
Probit-Inverse of Group-Level Normal Distribution
Read multiTree files
C++ Sampler for Standard (Nonhierarchical) MPT Models
MCMC Summary
Summarize JAGS Output for Hierarchical MPT Models
Chi-Square Test of Heterogeneity
Permutation Test of Heterogeneity
Fit a Hierarchical Latent-Trait MPT Model
Get Transformed Parameters
TreeBUGS: Hierarchical Multinomial Processing Tree Modeling
WAIC: Widely Applicable Information Criterion
Generate EQN Files for Within-Subject Designs
User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions. A detailed documentation is available in Heck, Arnold, & Arnold (2018) <DOI:10.3758/s13428-017-0869-7> and a tutorial on MPT modeling can be found in Schmidt, Erdfelder, & Heck (2022) <DOI:10.31234/osf.io/gh8md>.