Title: R Package for Ridge Fusion in Statistical Learning
Maintainer: Bradley S. Price <price412@umn.edu>
Description: This package implements ridge fusion methodology for inverse covariance matrix estimation for use in quadratic discriminant analysis.  The package also contains function for model based clustering using ridge fusion for inverse matrix estimation, as well as the implementation of the EM validation likelihood for tuning parameter selection.  We have also implemented QDA using joint inverse covariance estimation.

Maintainer: Bradley S. Price <bprice@bus.miami.edu>
Description: This package implements ridge fusion methodology for inverse covariance matrix estimation for use in quadratic discriminant analysis.  The package also contains function for model based clustering using ridge fusion for inverse matrix estimation, as well as tuning parameter selection functions.  We have also implemented QDA using joint inverse covariance estimation.
Packaged: 2014-09-18 12:27:22 UTC; bradprice

Title: Ridit Analysis (An extension of the Kruskal-Wallis Test.)
Description: An extension of the Kruskal-Wallis Test that allow
selection of arbitrary reference group. Also provide Mean Ridit
for each group. Mean Ridit of a group is an estimate of
probability a random observation from that group will be
greater than or equal to a random observation from reference

Title: Functions and C++ Header Files for Computation on Manifolds
Description: We provide a number of algorithms to estimate fundamental statistics including Fréchet mean and geometric median for manifold-valued data. Also, C++ header files are contained that implement elementary operations on manifolds such as Sphere, Grassmann, and others. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> if you are interested in statistics on manifolds, and Absil et al (2007) <isbn:978-0-691-13298-3> on computational aspects of optimization on matrix manifolds.
Imports: Rcpp, utils, Rdpack, parallel, stats, pracma

Description: We provide a number of algorithms to estimate fundamental statistics including Fréchet mean and geometric median for manifold-valued data. Also, C++ header files are contained that implement elementary operations on manifolds such as Sphere, Grassmann, and others. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> if you are interested in statistics on manifolds, and Absil et al (2007, ISBN:9780691132983) on computational aspects of optimization on matrix manifolds.