mdpeer: Graph-Constrained Regression with Enhanced Regularization Parameters Selection

Provides graph-constrained regression methods in which regularization parameters are selected automatically via estimation of equivalent Linear Mixed Model formulation. 'riPEER' (ridgified Partially Empirical Eigenvectors for Regression) method employs a penalty term being a linear combination of graph-originated and ridge-originated penalty terms, whose two regularization parameters are ML estimators from corresponding Linear Mixed Model solution; a graph-originated penalty term allows imposing similarity between coefficients based on graph information given whereas additional ridge-originated penalty term facilitates parameters estimation: it reduces computational issues arising from singularity in a graph-originated penalty matrix and yields plausible results in situations when graph information is not informative. 'riPEERc' (ridgified Partially Empirical Eigenvectors for Regression with constant) method utilizes addition of a diagonal matrix multiplied by a predefined (small) scalar to handle the non-invertibility of a graph Laplacian matrix. 'vrPEER' (variable reducted PEER) method performs variable-reduction procedure to handle the non-invertibility of a graph Laplacian matrix.

Version: 1.0.1
Depends: R (≥ 3.3.3)
Imports: reshape2, ggplot2, nlme, boot, nloptr, rootSolve, psych, magic, glmnet
Suggests: knitr, rmarkdown
Published: 2017-05-30
Author: Marta Karas [aut, cre], Damian Brzyski [ctb], Jaroslaw Harezlak [ctb]
Maintainer: Marta Karas <marta.karass at>
License: GPL-2
NeedsCompilation: no
Materials: README
CRAN checks: mdpeer results


Reference manual: mdpeer.pdf
Vignettes: Intro and usage examples
Package source: mdpeer_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: mdpeer_1.0.1.tgz
OS X Mavericks binaries: r-oldrel: mdpeer_0.1.0.tgz
Old sources: mdpeer archive


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