| Name : R-mdpeer
| |
| Version : 1.0.1
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp156.21.1
| Date : 2024-09-10 03:20:19
|
| Group : Development/Libraries/Other
| Source RPM : R-mdpeer-1.0.1-lp156.21.1.src.rpm
|
| Size : 0.46 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Graph-Constrained Regression with Enhanced Regularization Parameters Selection
|
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.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.6/x86_64 |
