unified computational interfaces for pop PK
GPL2; incorporates by permission code of W. Bachman
(wrtab 5.for, infnx5u.for), A. Gelman, (bugs.R) and
J.Pinheiro (getPsi).

unified computational interfaces for population PK

GPL-2; incorporates by permission code of W. Bachman
(wrtab 5.for, infnx5u.for), A. Gelman, (bugs.R) and
J.Pinheiro (getPsi).

computations for WinBUGS, NONMEM V, NLME

Estimates closed population size using a partial
likelihood.

Estimates closed population size when using a partial
likelihood from capture-recapture data.

Tabulate P.L. 94-171 Redistricting Data Summary Files
c(
person("Cory", "McCartan", email = "cmccartan@g.harvard.edu", role = c("aut", "cre")),
person("Christopher T.", "Kenny", email = "christopherkenny@fas.harvard.edu", role = "aut"))
Tools to process legacy format summary redistricting data files
produced by the United States Census Bureau pursuant to P.L. 94-171. These
files are generally available earlier but are difficult to work with as-is.
stringr, readr, dplyr (>= 1.0.0), tigris, sf, withr, httr
testthat (>= 3.0.0), lifecycle, knitr, rmarkdown, ggplot2
Cory McCartan [aut, cre],
Christopher T. Kenny [aut]

cli, stringr, readr, dplyr (>= 1.0.0), tinytiger, sf, withr,
httr

Portmanteau Local Feature Discrimination for Matrix-Variate Data
c(
person("Zengchao", "Xu", role = c("aut", "cre"), email = "zengc.xu@aliyun.com"),
person("Shan", "Luo",    role="aut"),
person("Zehua", "Chen",  role="aut"))
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
matrix-variate data.
Zengchao Xu [aut, cre],
Shan Luo [aut],
Zehua Chen [aut]

Multiplicity control using Pooled LIS statistics
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.