Description: Implements Bayesian linear regression, normal means estimation, and variable selection using the inverse gamma-gamma prior, as introduced by Bai and Ghosh (2018) <arXiv:1710.04369>.
Depends: R (>= 3.1.0), Matrix, MASS, glmnet, pscl, GIGrvg

Author: Quanli Wang, Sahar Gelfman, Diana Hall, Ryan Dhindsa
Description: Software tools for the characterization of neuronal networks as recorded on multi-electrode arrays.

Description: Software tools developed by the Institute for Genomic Medicine, Columbia University, for the characterization of neuronal networks as recorded on Multi-Electrode Arrays (MEAs).

Title: Interchangeable Gaussian Process Models
person("Collin", "Erickson", email = "collinberickson@gmail.com", role = c("aut", "cre"))
Description: Creates a Gaussian process model using the specified package.
Makes it easy to try different packages in same code, only the
It is essentially a wrapper for the other Gaussian process
Suggests: GPfit, laGP, mlegp, tgp, DiceKriging, GauPro, testthat, lhs,

Suggests: GPfit, laGP, mlegp, tgp, DiceKriging, CGP, GauPro, hetGP,
testthat, lhs, ggplot2, reshape, ContourFunctions, numDeriv
Packaged: 2021-04-26 02:57:45 UTC; Collin Erickson

Author: Nitesh Kumar Sharma, Dwijesh Chandra Mishra, Neeraj Budhlakoti and Md. Samir Farooqi
Maintainer: Nitesh Kumar Sharma <sharmanitesh.iasri@gmail.com>
Description: Mining informative genes with certain biological meanings are important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package selects the informative genes related to a specific trait using gene expression dataset. These trait specific genes are considered as informative genes. This package returns the informative gene set from the high dimensional gene expression data using a combination of methods SVM and MRMR (for feature selection) with bootstrapping procedure.