Implements the gene-based segregation test(GESE) and the weighted GESE test for identifying genes with causal variants of large effects for family-based sequencing data. The methods are described in Qiao, D. Lange, C., Laird, N.M., Won, S., Hersh, C.P., et al. (2017). <DOI:10.1002/gepi.22037>. Gene-based segregation method for identifying rare variants for family-based sequencing studies. Genet Epidemiol 41(4):309-319. More details can be found at <http://scholar.harvard.edu/dqiao/gese>.

The Gene Expression State Transformation (GESTr) models
the states of expression of genes across a compendium of
samples in order to provide a universal scale of gene
expression for all genes. TranSAM is a modification of the SAM
approach designed to utilise GESTr-transformed gene expression
data.

c(
person(given = 'Mari', family = 'Myllymäki', role = c('aut', 'cre'),
email = 'mari.myllymaki@luke.fi'),
person(given = 'Tomáš', family = 'Mrkvička', role = 'aut',
email = 'mrkvicka.toma@gmail.com'),
person(given = 'Pavel', family = 'Grabarnik', role = 'ctb',
email = 'gpya@rambler.ru'),
person(given = 'Ute', family = 'Hahn', role = 'ctb',
email = 'ute@math.au.dk'),
person(given = 'Mikko', family = 'Kuronen', role = 'ctb',
email = 'mikko.kuronen@luke.fi'),
person(given = 'Michael', family = 'Rost', role = 'ctb',
email = 'rost@ef.jcu.cz'),
person(given = 'Henri', family = 'Seijo', role = 'ctb',
email = 'henri.seijo@iki.fi')
)
caTools, fda.usc, ggplot2, graphics, grDevices, gridExtra,
gstat, methods, parallel, plyr, sp, spatstat, stats, utils,
Implementation of global envelopes with intrinsic graphical interpretation which can be used for graphical Monte Carlo and permutation tests where the test statistic is a multivariate vector or function (e.g. goodness-of-fit testing for point patterns and random sets, functional analysis of variance, functional general linear model, n-sample test of correspondence of distribution functions), for central regions of functional or multivariate data (e.g. outlier detection, functional boxplot) and for global confidence and prediction bands (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See Myllymäki et al. (2017) <doi: 10.1111/rssb.12172>, Mrkvička et al. (2017) <doi: 10.1007/s11222-016-9683-9>, Mrkvička et al. (2016) <doi: 10.1016/j.spasta.2016.04.005>, Mrkvička et al. (2018) <arXiv:1612.03608>, Mrkvička et al. (2019) <arXiv:1906.09004>, Mrkvička et al. (2019) <arXiv:1902.04926>.
Mari Myllymäki [aut, cre],
Tomáš Mrkvička [aut],
Pavel Grabarnik [ctb],
Ute Hahn [ctb],
Mikko Kuronen [ctb],
Michael Rost [ctb],
Henri Seijo [ctb]