Title: DWD implementation based on A IPM SOCP solver
Author: Hanwen Huang, Perry Haaland, Xiaosun Lu, Yufeng Liu, J. S. Marron
Description: This package provides the implementation of distance weighted discrimination (DWD) using an interior point
method for the solution of second order cone programming problems, originally described by K.C. Toh, M.J. Todd,

Author: Hanwen Huang, Perry Haaland, Xiaosun Lu, Yufeng Liu, J. S.
Description: This package provides the implementation of distance
weighted discrimination (DWD) using an interior point method
for the solution of second order cone programming problems,
originally described by K.C. Toh, M.J. Todd, R.H. Tutuncu

Title: Fast Algorithms for Large Scale Generalized Distance Weighted
Author: Xin-Yee Lam, J.S. Marron, Defeng Sun, and Kim-Chuan Toh
Description: Solving large scale distance weighted discrimination.
The main algorithm is a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method. See Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018) <arXiv:1604.05473> for more details.

Author: Nurgazy Sulaimanov, Sunil Kumar and Heinz Koeppl
Description: Infers networks with hubs using degree weighted Lasso method.

Title: Parametric Regression for Discrete Response
Description: Regression for a discrete response, where the conditional distribution is modelled via a discrete Weibull distribution.

Add a "Did You Mean" feature to the R interactive.
With this package, error messages for misspelled input of variable names or package names
suggest what you really want to do in addition to notification of the mistake.

Author: Kosei Abe [aut, cre], Richard Cotton [ctb]
Description: Add a "Did You Mean" feature to the R interactive. With this
package, error messages for misspelled input of variable names or package names

Authors@R: c(person("Xuechan", "Li", email = "xuechanli0530@gmail.com", role = c("aut", "cre")),
person("Jichun", "Xie", email = "jichuan.xie@duke.edu", role = c("ctb")))
Description: A multiple testing procedure aims to find the rare-variant association regions. When variants are rare, the single variant association test approach suffers from low power. To improve testing power, the procedure dynamically and hierarchically aggregates smaller genome regions to larger ones and performs multiple testing for disease associations with a controlled node-level false discovery rate. This method are members of the family of ancillary information assisted recursive testing introduced in Pura, Li, Chan and Xie (2021) <arXiv:1906.07757v2> and Li, Sung and Xie (2021) <arXiv:2103.11085v2>.
Imports: data.table, tidyverse, Matrix, reshape2, stats, methods,
Packaged: 2022-10-14 01:08:56 UTC; xuechanli