Author: Nicolas De Jay, Simon Papillon-Cavanagh, Catharina Olsen,
Depends: R (>= 2.10), survival, igraph, methods

Author: Nicolas De Jay, Simon Papillon-Cavanagh, Catharina Olsen, Gianluca Bontempi, Benjamin Haibe-Kains
Description: This package contains a set of function to compute mutual information matrices from continuous, categorical and survival variables. It also contains function to perform feature selection with mRMR and a new ensemble mRMR technique.

Title: Parallelized Minimum Redundancy, Maximum Relevance (mRMR)
Description: Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique.

Title: "Parallelized Minimum Redundancy, Maximum Relevance (mRMR)
Description: "Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique with DOI: N De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>."

Description: Computes mutual information matrices from continuous, categorical
and survival variables, as well as feature selection with minimum redundancy,
maximum relevance (mRMR) and a new ensemble mRMR technique. Published in
De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>.
Depends: R (>= 3.5), survival, igraph, methods

Title: Numerical Tools for Filling Out an NCAA Basketball Tournament
person("Eli", "Shayer", email = "eshayer@stanford.edu",
person("Scott", "Powers", email = "sspowers@stanford.edu", role = "aut"))
Imports: dplyr, glmnet, Matrix, rvest, shiny, xml2
Description: Scrape season results, estimate win probabilities, and find a
competitive bracket for your office pool. Additional utilities include:
scraping population picks; simulating tournament results; and testing your
Packaged: 2017-04-09 05:13:38 UTC; elishayer

Title: mRm: An R package for conditional maximum likelihood estimation
Description: Conditional maximum likelihood estimation via the EM
algorithm and information-criterion-based model selection in