This package implements the stochastic meta algorithm called Optimal Feature Weighting for two multiclass classifiers, CART and SVM

Estimation of Ordered Generalized Linear Models
Ordered models such as ordered probit and ordered logit presume that the error variance is constant across observations. In the case that this assumption does not hold estimates of marginal effects are typically biased. This package allows for generalization of ordered probit and ordered logit models by allowing the user to specify a model for the variance. Furthermore, the package includes functions to calculate the marginal effects.

Ordered models such as ordered probit and ordered logit presume that the error variance is constant across observations. In the case that this assumption does not hold estimates of marginal effects are typically biased. This package allows for generalization of ordered probit and ordered logit models by allowing the user to specify a model for the variance. Furthermore, the package includes functions to calculate the marginal effects. Wrapper functions to estimate the standard limited dependent variable models are also included.

Ordered models such as ordered probit and ordered logit presume that the error variance is constant across observations. In the case that this assumption does not hold estimates of marginal effects are typically biased (Weiss (1997)). This package allows for generalization of ordered probit and ordered logit models by allowing the user to specify a model for the variance. Furthermore, the package includes functions to calculate the marginal effects. Wrapper functions to estimate the standard limited dependent variable models are also included.

Modeling of Ordinal Random Variables via Softmax Regression
Supports the modeling of ordinal random variables,
like the outcomes of races, via Softmax regression,
under the Harville <doi:10.1080/01621459.1973.10482425> and
Henery <doi:10.1111/j.2517-6161.1981.tb01153.x> models.
Rcpp (>= 0.12.3), maxLik, magrittr, methods, dplyr
rlang, tidyr, forcats, microbenchmark, testthat, numDeriv,
ggplot2, scales, knitr
'data.r' 'harsm.r' 'hensm.r' 'linodds.r' 'ohenery.r'
'RcppExports.R' 'rsm.r' 'utils.r'