Fitting latent class mixed multinomial probit (LCMMNP) models to simulated
or empirical choice data via Bayesian estimation. The number of latent
classes can be updated within the algorithm on a weight-based strategy.
For a reference on the method see Oelschlaeger and Bauer (2021)
Suggests: knitr, rmarkdown, mlogit, vdiffr, testthat (>= 3.0.0)
Packaged: 2021-11-12 16:26:28 UTC; loelschlaeger

Bayes estimation of probit choice models, both in the cross-sectional and
panel setting. The package can analyze binary, multivariate, ordered, and
ranked choices, and places a special focus on modeling heterogeneity of
choice behavior among deciders. The main functionality includes model
fitting via Markov chain Monte Carlo methods, tools for convergence
diagnostic, choice data simulation, in-sample and out-of-sample choice
prediction, and model selection using information criteria and Bayes
factors. The latent class model extension facilitates preference-based
decider classification, where the number of latent classes can be inferred
via the Dirichlet process or a weight-based updating scheme. This allows for
flexible modeling of choice behavior without the need to impose structural
constraints. For a reference on the method see Oelschlaeger and Bauer (2021)
Imports: Rcpp, mvtnorm, viridis, ggplot2, rlang, mixtools, doSNOW,
foreach, progress, gridExtra, crayon, plotROC, MASS
Suggests: knitr, rmarkdown, mlogit, testthat (>= 3.0.0), covr
Packaged: 2022-07-22 09:20:19 UTC; loelschlaeger

Packaged: 2022-08-11 13:00:30 UTC; loelschlaeger

foreach, progress, gridExtra, crayon, plotROC, MASS, cli

Title: WOE Transformation and Scorecard Builder
person("Thomas", "Brandenburger", email = "thomas.brandenburger@sdstate.edu", role = "cre"),
person("Eric", "Stratman", email = "eric.stratman@sdstate.edu", role = "aut"),
person("Riaz", "Khan", email = "mdriazahmed.khan@jacks.sdstate.edu", role = "aut"),
person("Allison", "Lempola", email = "ally@rprofet.com", role = "aut"))
Description: Performs all steps in the credit scoring process. This package allows the user to follow all the necessary steps for building an effective scorecard. It provides the user functions for coarse binning of variables, Weights of Evidence (WOE) transformation, variable clustering, custom binning, visualization, and scaling of logistic regression coefficients. The results will generate a scorecard that can be used as an effective credit scoring tool to evaluate risk. For complete details on the credit scoring process, see Siddiqi (2005, ISBN:047175451X).
Imports: binr, ClustOfVar, ggplot2, gridExtra, plyr, sqldf