Packaged: 2021-01-20 14:59:09 UTC; Benjamin (PEI)

Packaged: 2022-05-05 14:59:30 UTC; Benjamin (PEI)

Title: Estimating multistate models using gamboost()
Description: Provides features to use function gamboost() from  package mboost for estimation of multistate models
Packaged: 2014-02-11 07:24:20 UTC; Holger Reulen

Packaged: 2014-10-31 15:32:42 UTC; Holger Reulen

Description: Contains infrastructure for using mboost::gamboost() in order to estimate multistate models.
Packaged: 2022-05-06 05:27:25 UTC; hsennhenn

Title: Functions and data for a course on modern regression and
Depends: R (>= 2.10.0), mgcv, DAAG, MASS, randomForest, lattice,
latticeExtra, Ecdat, mlbench, rpart, KernSmooth
Suggests: leaps, quantreg, sp, diagram, ape, oz, forecast, DAAGbio,
Description: Modern regression extends to include classification and

Suggests: leaps, quantreg, sp, diagram, oz, forecast, SMIR, kernlab,
Imports: car, mgcv, DAAG, MASS, rpart, randomForest, lattice,
Description: Modern regression extends to include classification and multivariate exploration

Title: Functions and Data for a Course on Modern Regression and
Description: Functions and data are provided that support a course that emphasizes statistical
issues of inference and generalizability.  Attention is restricted to a relatively small
number of methods, often (misleadingly in my view) referred to as algorithms.

Suggests: leaps, quantreg, sp, diagram, oz, forecast, kernlab, Ecdat,
number of methods, often (misleadingly, in my view) referred to as algorithms.

mlbench, DAAGbio, car, mgcv, DAAG, MASS, ape,
Imports: rpart, randomForest, lattice, latticeExtra, methods
Description: Functions and data are provided that support a course
that emphasizes statistical issues of inference and generalizability.
The functions are designed to make it straightforward to illustrate
the use of cross-validation, the training/test approach, simulation,
and model-based estimates of accuracy.  Methods considered are
Generalized Additive Modeling, Linear and Quadratic Discriminant
Analysis, Tree-based methods, and Random Forests.