Description: POM-aSPU test evaluates an association between an ordinal response and multiple phenotypes, for details see Kim and Pan (2017) <DOI:10.1002/gepi.22033>.
Depends: R (>= 2.10), MASS, matrixStats, stats

Title: Plot Population Demographic History
Description: Plot demographic graphs for single/multiple populations from coalescent simulation program input. Currently, this package can support the 'ms', 'msHot', 'MaCS', 'msprime', 'SCRM', and 'Cosi2' simulation programs. It does not check the simulation program input for correctness, but assumes the simulation program input has been validated by the simulation program. More features will be added to this package in the future, please check the 'GitHub' page for the latest updates: <https://github.com/YingZhou001/POPdemog>.

Title: Power Simulation for Sequential Analysis and Multiple Hypotheses
Authors@R: c(person("Gáspár", "Lukács", email = "lkcsgaspar@gmail.com", role =
Description: Calculates, via simulation, power and appropriate "stopping"
alpha boundaries (and/or futility bounds) for sequential analyses (i.e.,
"group sequential design") as well as for multiple hypotheses (multiple tests
included in an analysis), given any specified global error rate. This enables
the sequential use of practically any significance test, as long as the
underlying data can be simulated in advance to a reasonable approximation.
Suggests: testthat (>= 3.0.0), faux, knitr, rmarkdown

Title: Power Simulation for Sequential Analyses and Multiple Hypotheses
Description: Calculates, via simulation, power and appropriate stopping
group sequential design) as well as for multiple hypotheses (multiple tests

Title: Phylogeny-Guided OTU-Specific Association Test for Microbiome
Authors@R: c(person("Caizhi", "Huang", role = "aut"),
person("Jung-Ying", "Tzeng", role = "aut"),
Description: Implements the Phylogeny-Guided Microbiome OTU-Specific Association
Test method, which boosts the testing power by adaptively borrowing
information from phylogenetically close OTUs (operational taxonomic units)
is built on a kernel machine regression framework and allows for flexible
modeling of complex microbiome effects, adjustments for covariates, and
can accommodate both continuous and binary outcomes.
Collate: 'ACAT.R' 'CKAT.R' 'POSTmData.R' 'distanceMatrix.R'