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Imports: cli, stringr, readr, dplyr (>= 1.0.0), tinytiger, sf, withr,
Packaged: 2022-08-25 15:46:00 UTC; cmccartan

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Title: Portmanteau Local Feature Discrimination for Matrix-Variate Data
person("Zengchao", "Xu", role = c("aut", "cre"), email = "zengc.xu@aliyun.com"),
The portmanteau local feature discriminant approach first identifies
the local discriminant features and their differential structures,
then constructs the discriminant rule by pooling the identified local
features together. This method is applicable to high-dimensional
LinkingTo: Rcpp (>= 1.0.2), RcppArmadillo (>= 0.9.800)

Title: Multiplicity control using Pooled LIS statistics
Description: PLIS is a multiple testing procedure for testing several
groups of hypotheses. Linear dependency is expected from the
hypotheses within the same group and is modeled by hidden
Markov Models. It is noted that, for PLIS, a smaller p value
does not necessarily imply more significance because of
dependency among the hypotheses. A typical application of PLIS
is to analyze genome wide association studies datasets, where
SNPs from the same chromosome are treated as a group and
exhibit strong linear genomic dependency.

Package: PLIS
Type: Package
Title: Multiplicity control using Pooled LIS statistic
Version: 1.1
Date: 2012-08-08
Author: Zhi Wei & Wenguang Sun
Maintainer: Zhi Wei <zhiwei04@gmail.com>
Description: PLIS is a multiple testing procedure for testing several
groups of hypotheses. Linear dependency is expected from the
hypotheses within the same group and is modeled by hidden
Markov Models. It is noted that, for PLIS, a smaller p value
does not necessarily imply more significance because of
dependency among the hypotheses. A typical application of PLIS
is to analyze genome wide association studies datasets, where
SNPs from the same chromosome are treated as a group and
exhibit strong linear genomic dependency.
License: GPL-3
Packaged: 2012-08-11 03:34:15 UTC; Zhi
Repository: CRAN
Date/Publication: 2012-08-11 04:40:46