mmpca
Integrative Analysis of Several Related Data Matrices
A generalization of principal component analysis for integrative analysis. The method finds principal components that describe single matrices or that are common to several matrices. The solutions are sparse. Rank of solutions is automatically selected using cross validation. The method is described in Kallus, Johansson, Nelander and Jörnsten (2019) <arXiv:1911.04927>.
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Description file content
- Package
- mmpca
- Title
- Integrative Analysis of Several Related Data Matrices
- Version
- 1.0.2
- Author
- Jonatan Kallus [aut, cre]
- Maintainer
- Jonatan Kallus
- Description
- A generalization of principal component analysis for integrative
analysis. The method finds principal components that describe single matrices
or that are common to several matrices. The solutions are sparse. Rank of
solutions is automatically selected using cross validation. The method is
described in Kallus, Johansson, Nelander and Jörnsten (2019)
- Depends
- R (>= 3.3.0)
- Imports
- CMF (>= 1.0), digest (>= 0.6.0), gsl (>= 1.9)
- LinkingTo
- Rcpp, RcppEigen
- License
- GPL-3
- LazyData
- true
- NeedsCompilation
- yes
- Encoding
- UTF-8
- RoxygenNote
- 6.0.0
- Packaged
- 2019-12-05 09:10:26 UTC; jonatan
- Repository
- CRAN
- Date/Publication
- 2019-12-05 09:50:06 UTC
