Description: This is the package for selecting variables for index
Packaged: Thu Sep 17 09:25:57 2009; wenxuan

Title: Tools for processing COPAS large-particle flow cytometer data
Author: Tyler Shimko <TShimko126@gmail.com>, Erik Andersen
Description: A logical workflow for the reading, processing, and visualization
of data obtained from the Union Biometrica Complex Object Parametric
Analyzer and Sorter (COPAS) platform large-particle flow cytometers and a
powerful suite of functions for the rapid processing and analysis of large
high-throughput screening data sets. It combines the speed of dplyr with
the elegance of ggplot2 to make analysis of COPAS data fast and painless.
Imports: dplyr (>= 0.2), kernlab (>= 0.9), stringr (>= 0.6), knitr (>=
Packaged: 2014-10-04 21:00:52 UTC; tylershimko

Packaged: 2014-12-10 03:33:58 UTC; tylershimko

Packaged: 2015-01-15 02:13:07 UTC; tylershimko

Packaged: 2014-07-04 15:01:10 UTC; tylershimko

Title: The COR for Optimal Subset Selection in Distributed Estimation
c(person("Guangbao", "Guo",role = c("aut", "cre"),
Description: An algorithm of optimal subset selection, related to Covariance matrices,
Observation matrices and Response vectors (COR) to select the optimal subsets
in distributed estimation. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.
Author: Guangbao Guo [aut, cre] (<https://orcid.org/0000-0002-4115-6218>),

Description: given a collection of intervals with integer start and end
positions, find recurrently targeted regions and estimate the
significance of finding. Randomization is implemented by
parallel methods, either using local host machines, or

Description: given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs.

Description: Given a collection of intervals with integer start and end
positions, find recurrently targeted regions and estimate the significance
of finding. Randomization is implemented by parallel methods, either
using local host machines, or submitting grid engine jobs.