Title: Bias Reduction in the Skew-Probit Model for a Binary Response
Description: Provides a function for the estimation of parameters
in a binary regression with the skew-probit link function.
Naive MLE, Jeffrey type of prior and Cauchy prior type of penalization are implemented,
as described in DongHyuk Lee and Samiran Sinha (2019+) <doi:10.1080/00949655.2019.1590579>.

Description: Methodology: Remove one observation. Training the rest of data that are sampled without replacement and given this observation's input, predict the response back. Replicate this N times and for each response, take a sample from these replicates with replacement. Average each responses of sample and again replicate this step N time for each observation. Approximate these N new responses and generate another N responses y'. Training these y' and predict to have N responses of each testing observation. The average of N is the final prediction. Each observation will do the same.
Depends: R (>= 3.2.5), rpart , parallel , epandist , triangle, caret

Description: Methodology: Remove one observation. Training the rest of data that are sampled without replacement and given this observation's input, predict the response back. Replicate this N times and for each response, take a sample from these replicates with replacement. Average each responses of sample and again replicate this step N time for each observation. Approximate these N new responses by using bootstrap method and generate another N responses y'. Training these y' and predict to have N responses of each testing observation. The average of N is the final prediction. Each observation will do the same.
Depends: R (>= 3.2.5), rpart , parallel, caret, nnet, pls

Title: Sparse Quadratic Discriminant Analysis
Description: Sparse Quadratic Discriminant Analysis (SQDA) can be performed. In SQDA, the covariance matrix are assumed to be block-diagonal.And, for each block, sparsity assumption is imposed on the covariance matrix. It is useful in high-dimensional setting.
Packaged: 2014-09-30 18:53:29 UTC; jiehuansun