Packaged: 2020-10-01 14:46:21 UTC; johnmuschelli

Packaged: 2020-10-02 17:06:21 UTC; johnmuschelli

Packaged: 2020-10-28 15:12:01 UTC; johnmuschelli

Packaged: 2022-06-10 12:34:34 UTC; johnmuschelli

Title: R Client for the Microsoft Cognitive Services Web Language Model
Description: R Client for the Microsoft Cognitive Services Web Language Model
REST API, including Break Into Words, Calculate Conditional
Probability, Calculate Joint Probability, Generate Next Words, and List
Available Models. A valid account MUST be registered at the Microsoft
Imports: methods, httr, jsonlite, pander

Suggests: knitr, rmarkdown, testthat, mscstexta4r

Title: Method of Successive Dichotomizations
Description: Implements the method of successive dichotomizations by
Bradley and Massof (2018) <doi:10.1371/journal.pone.0206106>,
which estimates item measures, person measures and ordered rating
category thresholds given ordinal rating scale data.
Packaged: 2020-03-06 03:22:34 UTC; chrisbradley

Packaged: 2020-05-02 13:25:18 UTC; chrisbradley

Packaged: 2020-11-05 19:49:00 UTC; chrisbradley

Packaged: 2021-03-03 15:52:50 UTC; chrisbradley

Title: Multi-class Sparse Discriminant Analysis
Author: Qing Mai <mai@stat.fsu.edu>, Yi Yang <yiyang@umn.edu>,  Hui Zou <hzou@stat.umn.edu>
Description: Efficient procedures for computing a new multi-class sparse discriminant analysis method that estimates all discriminant directions simultaneously.

Title: Multi-Class Sparse Discriminant Analysis
Description: Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously.

Author: Qing Mai <mai@stat.fsu.edu>, Yi Yang <yi.yang6@mcgill.ca>,  Hui Zou <hzou@stat.umn.edu>
Description: Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously. It is an implementation of the work proposed by Mai, Q., Yang, Y., and Zou, H. (2019) <doi:10.5705/ss.202016.0117>.

Title: Bayesian Inference for Multivariate Stochastic Differential
person("JunYong", "Tong", role = "aut"),
person("Nigel", "Delaney", role = "ctb"))
Description: Implements an MCMC sampler for the posterior distribution of arbitrary time-homogeneous multivariate stochastic differential equation (SDE) models with possibly latent components.  The package provides a simple entry point to integrate user-defined models directly with the sampler's C++ code, and parallelizes large portions of the calculations when compiled with 'OpenMP'.
Imports: Rcpp (>= 0.12.7), methods, stats, tools
Suggests: knitr, rmarkdown, testthat, RcppProgress