R (>= 2.10.0), mixdist, mclust, survival
CGHcall, CGHregions, snow, CNVtools, xtable, MASS

R (>= 2.15.0), mixdist, mclust, survival
This package carries out association analysis of common
copy number variants in population-based studies. This package
includes functions for analysing association under a series of
study designs (case-control, cohort, etc), using several
dependent variables (class status, censored data, counts) as
response, adjusting for covariates and considering various
inheritance models. It also includes functions for inferring
copy number (CNV genotype calling).  Various classes and
methods for generic functions (print, summary, plot, anova, ...
) have been created to facilitate the analysis.

R (>= 2.15.0), CNVassocData, mixdist, mclust, survival
CGHcall, CGHregions, CNVtools, xtable, MASS

Association Analysis of CNV Data and Imputed SNPs
CGHcall, CGHregions, CNVtools, xtable, MASS, Biobase, CGHbase
The CNVassoc package carries out analysis of common
Copy Number Variants (CNVs) and imputed Single Nucleotide
Polymorphisms (SNPs) in population-based studies.
It includes tools for estimating association under a series
of study designs (case-control, cohort, etc), using several
dependent variables (class status, censored data, counts)
as response, adjusting for covariates and considering
various inheritance models. Moreover, it is possible to
perform epistasis studies with pairs of CNVs or imputed SNPs.
It has been optimized in order to make feasible the analyses
of Genome Wide Association studies (GWAs) with hundreds of
thousands of genetic variants (CNVs / imputed SNPs). Also,
it incorporates functions for inferring copy number (CNV
genotype calling). Various classes and methods for generic
functions (print, summary, plot, anova, ...) have been
created to facilitate the analysis.

Carries out analysis of common
Copy Number Variants (CNVs) and imputed Single Nucleotide
Polymorphisms (SNPs) in population-based studies.
It includes tools for estimating association under a series
of study designs (case-control, cohort, etc), using several
dependent variables (class status, censored data, counts)
as response, adjusting for covariates and considering
various inheritance models. Moreover, it is possible to
perform epistasis studies with pairs of CNVs or imputed SNPs.
It has been optimized in order to make feasible the analyses
of Genome Wide Association studies (GWAs) with hundreds of
thousands of genetic variants (CNVs / imputed SNPs). Also,
it incorporates functions for inferring copy number (CNV
genotype calling). Various classes and methods for generic
functions (print, summary, plot, anova, ...) have been
created to facilitate the analysis.