Imports: microbenchmark, Rcpp (>= 1.0.0), RcppParallel (>= 5.0.1),
Packaged: 2022-08-17 19:14:46 UTC; connorpuritz

Packaged: 2022-09-29 13:05:20 UTC; connorpuritz

Packaged: 2022-11-11 20:18:07 UTC; connorpuritz

Title: Fair Adjusted Selective Inference
Description: The Fair Adjusted Selective Inference (FASI) procedure as described in the paper "A Fairness-Adjusted Selective Inference Framework For Classification" By: Bradley Rava, Wen Sun, Gareth James and Xin Tong (2021). The package includes a function that preforms the FASI procedure and returns both the r-scores and classification for new observations.
Depends: R (>= 3.5.0), naivebayes, fastAdaboost, gam, stats
Packaged: 2021-07-29 19:36:42 UTC; bradleyrava

Title: A Fast and Scalable Joint Estimator for Learning Multiple
Related Sparse Gaussian Graphical Models
Author: Beilun Wang [aut, cre], Yanjun Qi [aut]
Description: The FASJEM (A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models) is a joint estimator which is fast and scalable for learning multiple related sparse Gaussian graphical models. For more details, please see <https://www.cs.virginia.edu/yanjun/paperA14/2017_JEM_combined.pdf>.
Packaged: 2017-04-21 13:02:25 UTC; beilunwang

Packaged: 2017-05-25 18:53:21 UTC; beilunwang

Description: This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). The FASJEM algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogonous data sets, this tool  can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(fasjem) to learn the basic functions provided by this package. For more details, please see <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>.
Packaged: 2017-07-12 14:58:20 UTC; beilunwang