Last data update: 2014.03.03

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Haplin : Analyzing Case-Parent Triad and/or Case-Control Data with SNP Haplotypes

Package: Haplin
Title: Analyzing Case-Parent Triad and/or Case-Control Data with SNP
Haplotypes
Version: 6.0.1
Date: 2016-05-25
Type: Package
Author: Hakon K. Gjessing
Maintainer: Hakon K. Gjessing <hakon.gjessing@fhi.no>
Depends: GenABEL, DatABEL
Imports: utils, stats, graphics, grDevices, parallel, tools, mgcv,
MASS, SuppDists, snow
Description: Performs genetic association analyses of case-parent triad (trio) data with multiple markers. It can also incorporate complete or incomplete control triads, for instance independent control children. Estimation is based on haplotypes, for instance SNP haplotypes, even though phase is not known from the genetic data. Haplin estimates relative risk (RR + conf.int.) and p-value associated with each haplotype. It uses maximum likelihood estimation to make optimal use of data from triads with missing genotypic data, for instance if some SNPs has not been typed for some individuals. Haplin also allows estimation of effects of maternal haplotypes and parent-of-origin effects, particularly appropriate in perinatal epidemiology. Haplin allows special models, like X-inactivation, to be fitted on the X-chromosome. A GxE analysis allows testing interactions between environment and all estimated genetic effects.
License: GPL (>= 2)
URL: http://folk.uib.no/gjessing/genetics/software/haplin/
NeedsCompilation: no
Packaged: 2016-05-25 17:09:49 UTC; hkgjess
Repository: CRAN
Date/Publication: 2016-05-26 17:44:32

● Data Source: CranContrib
● 0 images, 30 functions, 0 datasets
● Reverse Depends: 0

bigRR : Generalized Ridge Regression (with special advantage for p >> n cases)

Package: bigRR
Type: Package
Title: Generalized Ridge Regression (with special advantage for p >> n
cases)
Version: 1.3-10
Date: 2014-08-23
Author: Xia Shen, Moudud Alam and Lars Ronnegard
Maintainer: Xia Shen <xia.shen@ki.se>
Description: The package fits large-scale (generalized) ridge regression for various distributions of response. The shrinkage parameters (lambdas) can be pre-specified or estimated using an internal update routine (fitting a heteroscedastic effects model, or HEM). It gives possibility to shrink any subset of parameters in the model. It has special computational advantage for the cases when the number of shrinkage parameters exceeds the number of observations. For example, the package is very useful for fitting large-scale omics data, such as high-throughput genotype data (genomics), gene expression data (transcriptomics), metabolomics data, etc.
Depends: R (>= 2.10), utils, hglm, DatABEL
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2014-08-23 06:09:24 UTC; xia
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-08-23 09:38:31

● Data Source: CranContrib
● Cran Task View: MachineLearning
● 0 images, 7 functions, 6 datasets
● Reverse Depends: 0