Authors@R: c(person("Kristian Hovde", "Liland", role = c("aut","cre"), email="kristian.liland@nofima.no"),
person("Tormod", "N�s", role=c("ctb")), person("Ulf Geir", "Indahl", role="ctb"))
measures like r1, r2, r3, r4, Yanai's GCD, RV, RV2 and adjusted RV are included
Imports: Rcpp, plotrix, pracma, progress, rARPACK
Packaged: 2016-03-16 14:04:18 UTC; kristian.liland

person("Tormod", "Næs", role=c("ctb")), person("Ulf Geir", "Indahl", role="ctb"))
Packaged: 2016-04-25 08:17:49 UTC; kristian.liland

Packaged: 2017-05-05 20:00:23 UTC; kristian.liland

Authors@R: c(person("Kristian Hovde", "Liland", role = c("aut","cre"), email="kristian.liland@nmbu.no"),
Packaged: 2017-09-24 17:59:38 UTC; kristian.liland

Packaged: 2016-10-12 20:14:38 UTC; kristian.liland

Title: Matrix eQTL: Ultra fast eQTL analysis via large matrix
Description: Matrix eQTL is designed for fast eQTL analysis on large
datasets. Matrix eQTL can test for association between genotype
and gene expression using both linear regression and ANOVA
models. The models can include covariates to account for such
factors as population structure, gender, and clinical
variables. It also supports models with heteroscedastic and/or

datasets.  Matrix eQTL can test for association between
genotype and gene expression using both linear regression and
ANOVA models.  The models can include covariates to account for
such factors as population structure, gender, and clinical
variables.  It also supports models with heteroscedastic and/or
correlated errors, false discovery rate estimation and separate
treatment of local (cis) and distant (trans) eQTLs.

genotype and gene expression using linear regression with
either additive or ANOVA genotype effects.  The models can
include covariates to account for factors as population
stratification, gender, and clinical variables.  It also
supports models with heteroscedastic and/or correlated errors,
false discovery rate estimation and separate treatment of local

Description: Matrix eQTL is designed for fast eQTL analysis on large datasets.
Matrix eQTL can test for association between genotype and gene expression using linear regression
with either additive or ANOVA genotype effects.
The models can include covariates to account for factors
as population stratification, gender, and clinical variables.
It also supports models with heteroscedastic and/or correlated errors,
false discovery rate estimation and separate treatment of local (cis) and distant (trans) eQTLs.