1. Sparsity by Quadratic Penalties- This package fits classical sparse regression models with efficient active set algorithms by solving quadratic problems. Also provides a few methods for model selection purpose (cross-validation, stability selection).
Topic : Machine Learning : Regularized and Shrinkage Methods [Filter]
Topic : Multivariate Statistics : Linear Models (Now Filtering)
Registered: 2007-07-07 15:13
4. heplots: Visualizing Multivariate Tests- Visualizing Tests in Multivariate Linear Models: The heplots package provides functions for representing sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions).
Development Status : 5 - Production/Stable [Filter]
5. desiRe- desiRe allows the computation and interactive specification of desirability functions of Harrington- and Derringer/Suich-type. Related density and distribution functions, optimization procedures and several different types of charts are provided.
8. Hierarchical Generalized Linear Models- The aim of the project is to develop a series of packages, including the hglm package, which implement the estimation algorithm of different random effects models based on the hierarchical likelihood theory.
10. Generalized ridge trace plots- The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias and precision.