infotheo, parallel, irlba, testthat, doSNOW, foreach

Description: Data on the Spy vs. Spy comic strip of Mad magazine, created and

Title: Split a Dataset for Training and Testing
c(person("Akhil", "Vakayil", role = c("aut", "cre"), email = "akhilv@gatech.edu"),
person("Roshan", "Joseph", role = c("aut", "ths")),
Description: Procedure to optimally split a dataset for training and testing.
'SPlit' is based on the method of support points, which is independent of modeling methods.
Please see Joseph and Vakayil (2020) <arXiv:2012.10945> for details.
This work is supported by U.S. National Science Foundation grant CBET-1921873.

This work is supported by U.S. National Science Foundation grant DMREF-1921873.

Title: Semiparametric Maximum likelihood Method for interactions
gene-environment in case-mother control-mother designs
Author: Moliere Nguile Makao <moliere.nguile@gmail.com> and Alexandre Bureau <alexandre.bureau@msp.ulaval.ca>
Maintainer: Moliere Nguile Makao <moliere.nguile@gmail.com>
Description: Implements the method of general semiparametric maximum likelihood estimation for logistic models in case-mother control-mother designs.

Title: Semiparametric Maximum Likelihood for Mother-Child Designs.
Author: Moliere Nguile-Makao <moliere.nguile@gmail.com> and Alexandre Bureau <alexandre.bureau@msp.ulaval.ca>
Packaged: 2014-12-08 14:54:40 UTC; molierenguilemakao

Title: Semiparametric Maximum Likelihood Method for Interactions
Gene-Environment in Case-Mother Control-Mother Designs
Authors@R: c(person("Moliere", "Nguile-Makao", role = c("aut", "cre"), email = "moliere.nguile@gmail.com"),
person("Alexandre", "Bureau", role = "aut",email = "alexandre.bureau@msp.ulaval.ca"))
Author: Moliere Nguile-Makao [aut, cre], Alexandre Bureau [aut]
Packaged: 2015-12-11 10:46:12 UTC; molierenguilemakao

Title: Selecting a Predictive Function for a Given Gene Expression Data
Author: Victor L Jong, Kit CB Roes & Marinus JC Eijkemans
Maintainer: Victor L Jong <v.l.jong@umcutrecht.nl>
Description: The recent advancement of high-throughput technologies has led to frequent utilization of gene expression and other "omics" data for toxicological, diagnostic or prognostic studies in and clinical applications. Unlike in classical predictions where the number of samples is greater than the number of variables (n>p), the challenge faced with prediction using "omics" data is that the number of parameters greatly exceeds the number of samples (p>>n). To solve this curse of dimensionality problem, several predictive functions have been proposed for direct and probabilistic classification and survival predictions. Nevertheless, these predictive functions have been shown to perform differently across datasets. Comparing predictive functions and choosing the best is computationally intensive and leads to selection bias. Thus, the question which function should one choose for a given dataset is to be ascertained. This package implements the approach proposed by Jong et al., (2016) to address this question.
Imports: stats, methods, utils, Biobase, CMA, lme4, lattice, limma,
Suggests: e1071, MASS, class, nnet, glmnet, randomForest