Psychometric mixture models based on flexmix infrastructure. At the moment Rasch mixture models with different parametrizations of the score distribution (saturated vs. mean/variance specification) and Bradley-Terry mixture models are implemented. Both mixture models can be estimated with or without concomitant variables. See vignette("raschmix", package = "psychomix") for details on the Rasch mixture models.
Infrastructure for psychometric modeling such as data classes (e.g., for paired comparisons) and basic model fitting functions (e.g., for Rasch and Bradley-Terry models). Intended especially as a common building block for fitting psychometric mixture models in package "psychomix" and psychometric tree models in package "psychotree".
Computes classification accuracy and consistency under Item Response Theory by the approach proposed by Lee, Hanson & Brennen (2002) and Lee (2010) or the approach proposed by Rudner (2001, 2005). For dichotomous IRT models.
This package fits nonparametric item and option characteristic curves using kernel smoothing. It allows for optimal selection of the smoothing bandwidth using cross-validation and a variety of exploratory plotting tools.
Analysis of dichotomous and polytomous response data using latent trait models under the Item Response Theory paradigm. Exploratory models can be estimated via quadrature or stochastic methods, a generalized confirmatory bi-factor analysis is included, and confirmatory models can be fit with a Metropolis-Hastings Robbins-Monro algorithm which may include polynomial or product constructed latent traits. Multiple group analysis and mixed effects designs may be performed for unidimensional or multidimensional item response models for detecting differential item functioning and modelling item and person covariates.
The EstCRM package estimates item and person parameters for the Samejima's Continuous Response Model (CRM), computes item fit residual statistics, draws empirical 3D item category response curves, draws theoretical 3D item category response curves, and generates data under the CRM for simulation studies.
Conditional maximum likelihood estimation via the EM algorithm and information-criterion-based model selection in binary mixed Rasch models.
The qgraph package can be used to visualize data as networks.
This package provides Partial least squares Regression for (weighted) generalized linear models and kfold crossvalidation of such models using various criteria. It allows for missing data in the eXplanatory variables. Bootstrap confidence intervals constructions are also available.
Fitting and testing multinomial processing tree models, a class of statistical models for categorical data. The parameters are the link probabilities of a tree-like graph and represent the latent cognitive processing steps executed to arrive at observable response categories (Batchelder & Riefer, 1999; Erdfelder et al., 2009; Riefer & Batchelder, 1988).