2020-04-22 19:29:14 UTC; Sebastian Schneider

Gene region-level testing procedure for SNP data, using the min
P test resampling approach
Package minPtest is designed for estimating a gene
region-level summary for SNP data from case-control studies
using a permutation-based resampling method, called min P test,
allowing execution on a compute cluster or multicore computer.

Package minPtest is designed for estimating a gene region-level summary for SNP data from case-control studies using a permutation-based resampling method, called min P test, allowing execution on a compute cluster or multicore computer.

c(person("Ludwig Kian Soon", "Hoon", email = "ldwgkshoon@gmail.com", role = c("aut", "cre"), comment = c(ORCID ="0000-0002-2310-3403")), person("Peter", "Shaw", email = "peter.shaw.cs@gmail.com", role = c("aut", "ctb"), comment = c(ORCID ="0000-0002-3187-8938")), person("Phil", "Giffard", email = "Phil.Giffard@menzies.edu.au", role = c("aut", "ctb"), comment = c(ORCID ="0000-0002-3030-9127")))
This is a R implementation of "Minimum SNPs" software as described in "Price E.P., Inman-Bamber, J., Thiruvenkataswamy, V., Huygens, F and Giffard, P.M." (2007) <doi:10.1186/1471-2105-8-278> "Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants."
knitr, testthat, pkgdown, seqinr, Biostrings, rmarkdown,
withr
Ludwig Kian Soon Hoon [aut, cre]
(<https://orcid.org/0000-0002-2310-3403>),
Peter Shaw [aut, ctb] (<https://orcid.org/0000-0002-3187-8938>),
Phil Giffard [aut, ctb] (<https://orcid.org/0000-0002-3030-9127>)
Ludwig Kian Soon Hoon <ldwgkshoon@gmail.com>

Multivariate Model Based Inference for Domains
c(person(given = "Michele",
family = "D'Alo'",
role = "aut",
email = "dalo@istat.it"),
person(given = "Stefano",
family = "Falorsi",
role = "aut",
email = "stfalors@istat.it"),
person(given = "Andrea",
family = "Fasulo",
role = c("aut", "cre"),
email = "fasulo@istat.it"))
R (>= 3.5.0), data.table, MASS, lme4, qdap, Matrix
Allows users to produce estimates and MSE for multivariate variables using Linear Mixed Model. The package follows the approach of Datta, Day and Basawa (1999) <doi:10.1016/S0378-3758(98)00147-5>.
Michele D'Alo' [aut],
Stefano Falorsi [aut],
Andrea Fasulo [aut, cre]