Dynamic Panel Models Fit with Maximum Likelihood

Implements the dynamic panel models described by Allison, Williams, and Moral-Benito (2017 ) in R. This class of models uses structural equation modeling to specify dynamic (lagged dependent variable) models with fixed effects for panel data. Additionally, models may have predictors that are only weakly exogenous, i.e., are affected by prior values of the dependent variable. Options also allow for random effects, dropping the lagged dependent variable, and a number of other specification choices.

Omega-Generic: Composite Reliability of Multidimensional Measures

It is a computer tool to estimate the item-sum score's reliability (composite reliability, CR) in multidimensional scales with overlapping items. An item that measures more than one domain construct is called an overlapping item. The estimation is based on factor models allowing unlimited cross-factor loadings such as exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM). The factor models include correlated-factor models and bi-factor models. Specifically for bi-factor models, a type of hierarchical factor model, the package estimates the CR hierarchical subscale/hierarchy and CR subscale/scale total. The CR estimator 'Omega-generic' was proposed by Mai, Srivastava, and Krull (2021) < https://whova.com/embedded/subsession/enars_202103/1450751/1452993/>. The current version can only handle continuous data. Yujiao Mai contributes to the algorithms, R programming, and application example. Deo Kumar Srivastava contributes to the algorithms and the application example. Kevin R. Krull contributes to the application example. The package 'OmegaG' was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of 'OmegaG' do not necessarily represent the policy of the ALSAC.

Including Known Interactions in Species Distribution Models

A collection of tools to fit and work with trophic Species Distribution Models. Trophic Species Distribution Models combine knowledge of trophic interactions with Bayesian structural equation models that model each species as a function of its prey (or predators) and environmental conditions. It exploits the topological ordering of the known trophic interaction network to predict species distribution in space and/or time, where the prey (or predator) distribution is unavailable. The method implemented by the package is described in Poggiato, Andréoletti, Pollock and Thuiller (2022) .

A General Framework for Multivariate Analysis with Optimal Scaling

Contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models.

Bayesian Framework for Computational Modeling

Derived from the work of Kruschke (2015, ), the present package aims to provide a framework for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC) sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer, 2003, < https://mcmc-jags.sourceforge.io>). The initial version includes several modules for conducting Bayesian equivalents of chi-squared tests, analysis of variance (ANOVA), multiple (hierarchical) regression, softmax regression, and for fitting data (e.g., structural equation modeling).

Continuous Time SEM - 'OpenMx' Based Functions

Original 'ctsem' (continuous time structural equation modelling) functionality, based on the 'OpenMx' software, as described in Driver, Oud, Voelkle (2017) , with updated details in vignette. Combines stochastic differential equations representing latent processes with structural equation measurement models. These functions were split off from the main package of 'ctsem', as the main package uses the 'rstan' package as a backend now -- offering estimation options from max likelihood to Bayesian. There are nevertheless use cases for the wide format SEM style approach as offered here, particularly when there are no individual differences in observation timing and the number of individuals is large. For the main 'ctsem' package, see < https://cran.r-project.org/package=ctsem>.

SEM Model Comparison with K-Fold Cross-Validation

The goal of 'cvsem' is to provide functions that allow for comparing Structural Equation Models (SEM) using cross-validation. Users can specify multiple SEMs using 'lavaan' syntax. 'cvsem' computes the Kullback Leibler (KL) Divergence between 1) the model implied covariance matrix estimated from the training data and 2) the sample covariance matrix estimated from the test data described in Cudeck, Robert & Browne (1983) . The KL Divergence is computed for each of the specified SEMs allowing for the models to be compared based on their prediction errors.

Pathmox Approach Segmentation Tree Analysis

It provides an interesting solution for handling a high number of segmentation variables in partial least squares structural equation modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez, and Aluja,(2016)) including the F-coefficient test (Lamberti, Sanchez, and Aluja,(2017)) to detect the path coefficients responsible for the identified differences). The package also allows running the hybrid multi-group approach (Lamberti (2021) ).

Evaluation of Inequality Constrained Hypotheses Using GORICA

Implements the generalized order-restricted information criterion approximation (GORICA), an AIC-like information criterion that can be utilized to evaluate informative hypotheses specifying directional relationships between model parameters in terms of (in)equality constraints (see Altinisik, Van Lissa, Hoijtink, Oldehinkel, & Kuiper, 2021), . The GORICA is applicable not only to normal linear models, but also to generalized linear models (GLMs), generalized linear mixed models (GLMMs), structural equation models (SEMs), and contingency tables. For contingency tables, restrictions on cell probabilities can be non-linear.

'shiny' Applications for Statistical and Psychometric Analysis

Toolbox with 'shiny' applications for widely used psychometric methods. Those methods include following analysis: Item analysis, item response theory calibration, principal component analysis, confirmatory factor analysis - structural equation modeling, generating simulated data. References: Chalmers (2012, ); Revelle (2022, < https://CRAN.R-project.org/package=psych Version = 2.2.9.>); Rosseel (2012, ); Magis & Raiche (2012, ); Magis & Barrada (2017, ).