Collate: opi.r compassClient.r daydreamClient.r dbTocd.r fourTwo.r

Title: Optimization via Subsampling (OPTS)
Authors@R: c(person("Mihai", "Giurcanu", role = c("aut", "cre"), email = "giurcanu@uchicago.edu"),
person("Marinela", "Capanu", role = c("aut", "ctb")),
person("Mithat", "Gonen", role = "aut"))
Description: Subsampling based variable selection for low dimensional generalized linear models. The methods repeatedly subsample the data minimizing an information criterion (AIC/BIC) over a sequence of nested models for each subsample. Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models.
Packaged: 2022-05-24 14:16:53 UTC; mgiurcanu

Authors@R: c(person("Lijia", "Wang", role = c("aut", "cre","cph"),
person("Xu", "Han", role = "aut"), person("Xin", "Tong", role = "aut") )
Description: Optimal testing under general dependence. The R package implements procedures proposed in Wang, Han, and Tong (2022). The package includes parameter estimation procedures, the computation for the posterior probabilities, and the testing procedure.
Imports: rootSolve, quantreg, RSpectra, stats

Title: Several confidence intervals for the odds ratio
Description: Computes various confidence intervals for the odds ratio of two independent binomial proportions.

Title: Order Restricted Clustering for Microarray Experiments
Author: Adetayo Kasim, Martin Otava, Tobias Verbeke
Description: This package provides clustering of genes with similar
dose response (or time course) profiles. It implements the method

dose response (or time course) profiles. It implements the

Description: Provides clustering of genes with similar

Title: Estimate parent distributions with data of several order
Description: This package uses B-spline based nonparametric smooth
estimators to estimate parent distributions given observations

Title: Order-restricted Information Criterion-based Clustering
Author: Tianqing Liu, Nan Lin, Ningzhong Shi and Baoxue Zhang
Description: ORIClust is a user-friendly R-based software package for
gene clustering. Clusters are given by genes matched to
prespecified profiles across various ordered treatment groups.
It is particularly useful for analyzing data obtained from
short time-course or dose-response microarray experiments.