Estimating multistate models using gamboost()
Provides features to use function gamboost() from  package mboost for estimation of multistate models

Contains infrastructure for using mboost::gamboost() in order to estimate multistate models.

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

leaps, quantreg, sp, diagram, oz, forecast, SMIR, kernlab,
Ecdat, mlbench, DAAGbio, knitr
car, mgcv, DAAG, MASS, rpart, randomForest, lattice,
latticeExtra, ape, KernSmooth
Modern regression extends to include classification and multivariate exploration

Functions and Data for a Course on Modern Regression and
Classification
car, mgcv, DAAG, MASS, rpart, randomForest, lattice,
latticeExtra, ape, KernSmooth, methods
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.

leaps, quantreg, sp, diagram, oz, forecast, kernlab, Ecdat,
mlbench, DAAGbio, knitr
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.

leaps, quantreg, sp, diagram, oz, forecast, kernlab, Ecdat,
mlbench, DAAGbio, car, mgcv, DAAG, MASS, ape,
KernSmooth,knitr,prettydoc,rmarkdown,bookdown
rpart, randomForest, lattice, latticeExtra, methods
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.