Imports: Rcpp (>= 0.11.1), splines, MASS, methods

Title: Estimation of Interpretable eQTL Effect Sizes Using a Log of
Author: Andrey A Shabalin, John Palowitch
Maintainer: Andrey A Shabalin <andrey.shabalin@gmail.com>
Description: We use a non-linear model, termed ACME,
that reflects a parsimonious biological model for
allelic contributions of cis-acting eQTLs.
With non-linear least-squares algorithm we
estimate maximum likelihood parameters. The ACME model
provides interpretable effect size estimates and
p-values with well controlled Type-I error.
Includes both R and (much faster) C implementations.
Suggests: knitr, rmarkdown, BiocStyle, pander

For more details see Palowitch et al. (2017) <doi:10.1111/biom.12810>.
Author: Andrey A Shabalin [aut, cre] (0000-0003-0309-6821),

Author: Andrey A Shabalin [aut, cre] (<https://orcid.org/0000-0003-0309-6821>),

Title: Affymetrix SNP probe-summarization using non-negative matrix
Author: Angel Rubio <arubio@ceit.es>, Maria Ortiz <mortiz@ceit.es>,
matrixStats (>= 0.2.2), aroma.affymetrix (>= 2.1.0), MASS
Description: Package for NMF summarization of SNP probes.
biocViews: Infrastructure, Microarray, DNACopyNumber, Preprocessing,

matrixStats (>= 0.3.0), aroma.affymetrix (>= 2.2.0), MASS

Depends: R (>= 2.15.0), aroma.affymetrix (>= 2.11.0)
person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"),

Depends: R (>= 3.0.0), aroma.affymetrix (>= 2.13.0)
Title: Affymetrix SNP Probe-Summarization using Non-Negative Matrix
Description: A summarization method to estimate allele-specific copy number signals for Affymetrix SNP microarrays using non-negative matrix factorization (NMF).
biocViews: aCGH, CopyNumberVariants, SNP, Microarray, OneChannel,

Depends: R (>= 3.0.0), aroma.affymetrix (>= 2.14.0)

Title: Gene Enrichment Analysis from ACSN Maps or Gmt Files
Authors@R: c(person("Paul", "Deveau", role = c("aut", "cre"),
Description: Compute and represent gene set enrichment or depletion from your data based on pre-saved maps from the Atlas of Cancer Signalling Networks (ACSN)  or user imported maps.
The gene set enrichment can be run with hypergeometric test or Fisher exact test, and can use multiple corrections.
Visualization of data can be done either by barplots or heatmaps.
Depends: R (>= 3.1.0),ggplot2, gridExtra