Package: mdpeer
Title: Graph-Constrained Regression with Enhanced Regularization
Parameters Selection
Version: 1.0.1
Authors@R: c(person("Marta", "Karas", email = "marta.karass@gmail.com", role = c("aut", "cre")),
person("Damian", "Brzyski", email = "dbrzyski@iu.edu", role = "ctb"),
person("Jaroslaw", "Harezlak", email = "harezlak@iu.edu", role = "ctb"))
Author: Marta Karas [aut, cre], Damian Brzyski [ctb], Jaroslaw Harezlak [ctb]
Maintainer: Marta Karas <marta.karass@gmail.com>
Description: 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.
Depends: R (>= 3.3.3)
Imports: reshape2, ggplot2, nlme, boot, nloptr, rootSolve, psych,
magic, glmnet
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2017-05-30 02:48:05 UTC; mkaras
Repository: CRAN
Date/Publication: 2017-05-30 04:44:40 UTC