Patrick Meyer and Sebastien Bigaret, with contributions from Krzysztof Ciomek.

Functions to Parse and Create XMCDA Files
Functions which allow to read many XMCDA tags and transform them into R variables which are then usable in MCDA algorithms written in R. It also allows to write certain R variables into XML files respecting the XMCDA standard.

Maximum Likelihood Shrinkage via Ridge or Least Angle Regression
Identify and display TRACEs for a specified shrinkage path and
determine the extent of shrinkage most likely, under normal distribution
theory, to produce an optimal reduction in MSE Risk in estimates of
regression (beta) coefficients.

Identify and display TRACEs for a specified shrinkage path and determine the extent of shrinkage most likely, under normal distribution theory, to produce an optimal reduction in MSE Risk in estimates of regression (beta) coefficients.

Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression
Identify and display TRACEs for a specified shrinkage path and determine
the extent of shrinkage most likely, under normal distribution theory, to produce an
optimal reduction in MSE Risk in estimates of regression (beta) coefficients.

Maximum Likelihood Shrinkage via Generalized Ridge or Least
Angle Regression
Identify and display TRACEs for a specified shrinkage path
and determine the extent of shrinkage most likely, under normal
distribution theory, to produce an optimal reduction in MSE
Risk in estimates of regression (beta) coefficients.

Identify and display TRACEs for a specified shrinkage path and determine
the extent of shrinkage most likely, under normal distribution theory, to produce an
optimal reduction in MSE Risk in estimates of regression (beta) coefficients. Alternative
estimates are also provided when ill-conditioned (nearly multicollinear) models yield
OLS estimates with "wrong" numerical signs.

Maximum Likelihood Shrinkage using Generalized Ridge or Least
Angle Regression Methods
Functions are provided to calculate and display ridge TRACEs for various
shrinkage PATHS. They determine the m-Extent of shrinkage most likely, under
Normal-distribution theory, to produce biased estimates of regression (beta) coefficients
with minimum MSE Risk. An alternative function, correct.signs(), provides beta coefficient
estimates when an ill-conditioned (nearly multicollinear) model yields OLS estimates with
("wrong") numerical signs that disagree with the signs of the observed correlations between
the Y-outcome and the selected X-predictor variables.