Author: Jens Hainmueller (Stanford) Chad Hazlett (UCLA)
Packaged: 2014-05-21 17:22:17 UTC; jhainmueller

Title: Kernel-Based Regularized Least Squares

Authors@R: person("Laval", "Jacquin", email = "jacquin.julien@gmail.com", role = c("aut", "cre"))
Description: Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. <doi:10.3389/fgene.2016.00145>.
Packaged: 2017-06-03 16:29:07 UTC; laval_jacquin

Title: Retained Component Criterion for Principal Component Analysis
Maintainer: Dr. Fayed Alshammri <fayed333@hotmail.com>
Description: The Retained Component Criterion for Principal Component Analysis (RCC_PCA) is a tool to determine the optimal number of components to retain in PCA.

Authors@R: c(person("Qiang", "Liu",role="aut",email="qliu@cs.dartmouth.edu"),person("Min Hyung", "Kang", role = c("aut","cre"), email = "Minhyung.Daniel.Kang@gmail.com"))
Description: An adaptation of Kernelized Stein Discrepancy, a discrepancy statistic for measuring differences between two probability distributions based on combination of Stein's method and reproducing kernel Hilbert space theory. It provides a method for a user to evaluate how similar two distributions are. It is based on "A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation" by Liu, Lee, and Jordan, available at <http://arxiv.org/abs/1602.03253>.
Suggests: datasets, ggplot2, gridExtra, mclust, mvtnorm
Maintainer: Min Hyung Kang <Minhyung.Daniel.Kang@gmail.com>