gamclass: Functions and Data for a Course on Modern Regression and Classification

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.

Version: 0.56
Depends: R (≥ 3.0.0)
Imports: car, mgcv, DAAG, MASS, rpart, randomForest, lattice, latticeExtra, ape, KernSmooth, methods
Suggests: leaps, quantreg, sp, diagram, oz, forecast, SMIR, kernlab, Ecdat, mlbench, DAAGbio, knitr
Published: 2015-08-20
Author: John Maindonald
Maintainer: John Maindonald <john.maindonald at anu.edu.au>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: gamclass results

Downloads:

Reference manual: gamclass.pdf
Vignettes: Key Ideas and Issues (Set 1 Figures)
Model Output Can Deceive (Set 10)
Ordination (Set 11)
Limits of Statistical Learning (Set 2)
Data-Based Generalization (Set 3)
Linear Models (Set 4)
Generalized Linear Models (Set 5)
Generalized Additive Models (Set 6)
Time Series (Set 7)
Tree-based regression (Set 8)
Discrimination and Classification (Set 9)
Package source: gamclass_0.56.tar.gz
Windows binaries: r-devel: gamclass_0.56.zip, r-release: gamclass_0.56.zip, r-oldrel: gamclass_0.56.zip
OS X Mavericks binaries: r-release: gamclass_0.56.tgz, r-oldrel: gamclass_0.56.tgz
Old sources: gamclass archive