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.
reshape2, ggplot2, nlme, boot, nloptr, rootSolve, psych,
magic, glmnet

person("Gary", "Chung", email="gchung05@gmail.com",
role=c("aut", "cre"))
A set of core functions for handling medical device event data in
the context of post-market surveillance, pharmacovigilance, signal detection
and trending, and regulatory reporting. Primary inputs are data on events by
device and data on exposures by device. Outputs include: standardized
device-event and exposure datasets, defined analyses, and time series.

Searching for Optimal MDS Procedure for Metric Data
Marek Walesiak <marek.walesiak@ue.wroc.pl>  Andrzej Dudek <andrzej.dudek@ue.wroc.pl>
Searching for Optimal MDS procedure for metric data.

Selecting the optimal multidimensional scaling (MDS) procedure for metric data via metric MDS (ratio, interval, mspline) and nonmetric MDS (ordinal). Selecting the optimal MDS procedure for statistical data referring to the evaluation of tourist attractiveness of Lower Silesian counties (BORG I., GROENEN P.J.F., MAIR P., (2013) <doi:10.1007/978-3-642-31848-1>, DE LEEUW J., MAIR P., (2015) <doi:10.1002/9781118445112.stat06268.pub2>, WALESIAK M., (2016) <doi:10.15611/ekt.2016.2.01>).