Estimation of multinomial probit models based on panel data. It uses the full likelihood as well as the MaCML approach. The multinomial Gaussian CDF is approximated using the Solow-Joe method.

Estimation of multinomial probit models based on panel data. It uses the full likelihood as well as the MaCML approach. The multinomial Gaussian CDF is approximated using the Solow-Joe method.

LaGrange, Georgia

Ordinal semiparametric regression models in the cumulative probability model (CPM) family, of which proportional odds, proportional hazards, and probit regression are examples, contain virtually all r...

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Fits spatial item factor analysis models for binary responses, following Chacón-Montalván, E. A., Parry, L., Giorgi, E., Torres, P., Orellana, J. D. Y., Moraga, P., and Taylor, B. M. (2025) &lt;<a href="https://doi.org/10.1214%2F25-AOAS2072" target="_top">doi:10.1214/25-AOAS2072</a>&gt;, using full Bayesian inference via adaptive Metropolis-Hastings within Gibbs sampling and auxiliary variables with a probit link function. The latent factors are modelled as the sum of a predictor effect, a multivariate Gaussian process capturing spatial dependence, and a multivariate non-spatial term, allowing users to fit and predict spatially-referenced constructs such as food insecurity or socioeconomic indices. Standard exploratory and confirmatory item factor analysis are supported as particular cases, simply by dropping the spatial structure.

Fits spatial item factor analysis models for binary responses, following Chacón-Montalván, E. A., Parry, L., Giorgi, E., Torres, P., Orellana, J. D. Y., Moraga, P., and Taylor, B. M. (2025) &lt;<a href="https://doi.org/10.1214%2F25-AOAS2072" target="_top">doi:10.1214/25-AOAS2072</a>&gt;, using full Bayesian inference via adaptive Metropolis-Hastings within Gibbs sampling and auxiliary variables with a probit link function. The latent factors are modelled as the sum of a predictor effect, a multivariate Gaussian process capturing spatial dependence, and a multivariate non-spatial term, allowing users to fit and predict spatially-referenced constructs such as food insecurity or socioeconomic indices. Standard exploratory and confirmatory item factor analysis are supported as particular cases, simply by dropping the spatial structure.

to RePEc:eee:econom:v:156:y:2010:i:2:p:367-376

Utilities for checking and diagnosing separation phenomena (complete separation, quasi-complete separation, overlap) for latent polyhedral categorical response models including binary, nominal and ordinal data models with link construction as baseline-category, adjacent-category, cumulative, sequential (continuation-ratio), ordered stereotype and arbitrary link error distribution (including logit, probit, cauchit, cloglog, loglog, log amongst others) based on structure vectors as described in Sablica, Hornik, Rusch (2026) &lt;<a href="https://doi.org/10.48550%2FarXiv.2610.08100" target="_top">doi:10.48550/arXiv.2610.08100</a>&gt; and Rusch, Sablica, Hornik (2026) &lt;<a href="https://doi.org/10.48550%2FarXiv.2610.09951" target="_top">doi:10.48550/arXiv.2610.09951</a>&gt;. The package functionality includes a check for separation and a check for overlap with a linear program, a quick check for overlap, a sequential and a parallel check for overlap based on subsamples, detailed separation diagnostics such as identification of columns in the design matrix/structure vector matrix that lead to separation, identification of design matrix/structure vector matrix rows that lead to separation, the dimension of the recession cone and the structure vectors that are linearities. Calculations can be done with package 'rcdd' to employ arbitrary precision and various other linear program solvers can also be used via package 'ROI'. Implemented checks can be done before model fitting based on outcome and design matrix or structure vector matrix or via formula (pre-fit), or after model fitting for objects of class 'glm', 'multinom', 'polr', 'osm', 'clm', 'bracl', 'brmultinom'. Wrappers for other classes can be easily constructed by third parties via pre-fit functions.

Utilities for checking and diagnosing separation phenomena (complete separation, quasi-complete separation, overlap) for latent polyhedral categorical response models including binary, nominal and ordinal data models with link construction as baseline-category, adjacent-category, cumulative, sequential (continuation-ratio), ordered stereotype and arbitrary link error distribution (including logit, probit, cauchit, cloglog, loglog, log amongst others) based on structure vectors as described in Sablica, Hornik, Rusch (2026) &lt;<a href="https://doi.org/10.48550%2FarXiv.2610.08100" target="_top">doi:10.48550/arXiv.2610.08100</a>&gt; and Rusch, Sablica, Hornik (2026) &lt;<a href="https://doi.org/10.48550%2FarXiv.2610.09951" target="_top">doi:10.48550/arXiv.2610.09951</a>&gt;. The package functionality includes a check for separation and a check for overlap with a linear program, a quick check for overlap, a sequential and a parallel check for overlap based on subsamples, detailed separation diagnostics such as identification of columns in the design matrix/structure vector matrix that lead to separation, identification of design matrix/structure vector matrix rows that lead to separation, the dimension of the recession cone and the structure vectors that are linearities. Calculations can be done with package 'rcdd' to employ arbitrary precision and various other linear program solvers can also be used via package 'ROI'. Implemented checks can be done before model fitting based on outcome and design matrix or structure vector matrix or via formula (pre-fit), or after model fitting for objects of class 'glm', 'multinom', 'polr', 'osm', 'clm', 'bracl', 'brmultinom'. Wrappers for other classes can be easily constructed by third parties via pre-fit functions.

Fits two-equation simultaneous models in the style of Lillard (1993) &lt;<a href="https://doi.org/10.1016%2F0304-4076%2893%2990106-F" target="_top">doi:10.1016/0304-4076(93)90106-F</a>&gt;: a pair of processes, each either a binary probit outcome or a continuous-time hazard duration, linked through a single correlation parameter between their underlying error terms and estimated jointly by maximum likelihood via a Gaussian copula. Equation 1 may be a probit, a log-normal accelerated-failure-time hazard, or a piecewise (linear-log-hazard) Gompertz hazard with an arbitrary, user-specified number of segments; equation 2 is always a hazard, log-normal or piecewise Gompertz. Models can also be fit under independence (correlation fixed at zero), which reduces exactly to two separate univariate fits. See the package vignette/manual for the full likelihood derivation and a simulation-based verification.

Fits two-equation simultaneous models in the style of Lillard (1993) &lt;<a href="https://doi.org/10.1016%2F0304-4076%2893%2990106-F" target="_top">doi:10.1016/0304-4076(93)90106-F</a>&gt;: a pair of processes, each either a binary probit outcome or a continuous-time hazard duration, linked through a single correlation parameter between their underlying error terms and estimated jointly by maximum likelihood via a Gaussian copula. Equation 1 may be a probit, a log-normal accelerated-failure-time hazard, or a piecewise (linear-log-hazard) Gompertz hazard with an arbitrary, user-specified number of segments; equation 2 is always a hazard, log-normal or piecewise Gompertz. Models can also be fit under independence (correlation fixed at zero), which reduces exactly to two separate univariate fits. See the package vignette/manual for the full likelihood derivation and a simulation-based verification.