Author: Tarn Duong <tarn.duong@gmail.com> & Matt Wand <wand@uow.edu.au>
Packaged: Tue Nov  4 16:11:26 2008; tduong

Packaged: Fri Dec 12 17:22:58 2008; tduong

Author: Tarn Duong <tduong@maths.unsw.edu.au> & Matt Wand <wand@maths.unsw.edu.au>
Packaged: Fri Aug 18 21:42:40 2006; hornik

Packaged: Sun Aug 20 16:46:25 2006; tduong

Packaged: Mon Oct  2 15:31:45 2006; tduong

Packaged: Thu Nov  2 13:03:32 2006; tduong

Packaged: Sat Jan  6 16:42:43 2007; tduong

Packaged: Mon Feb  5 13:23:10 2007; tduong

Packaged: Wed Mar 28 15:13:20 2007; tduong

Depends: R (>= 1.4.0), KernSmooth, rpanel, ks
Packaged: Fri Jan  9 11:33:16 2009; tduong

Author: Tarn Duong <tarn.duong@gmail.com> & Matt Wand <Matt.Wand@uts.edu.au>

Title: Local Inferential Feature Significance for Multivariate Kernel
Imports: grDevices, graphics, ks (>= 1.8.0), misc3d, rgl, stats, tcltk
Description: Local inferential feature significance for multivariate kernel density estimation.

Imports: grDevices, graphics, ks (>= 1.10.0), misc3d, rgl, stats, tcltk

Suggests: knitr, misc3d, rgl, rmarkdown, MASS

Authors@R: c(person("Tarn", "Duong", role=c("aut","cre"), email="tarn.duong@gmail.com"),

Depends: R (>= 1.4.0), KernSmooth, rpanel, ks (>= 1.6.1)
Packaged: Mon Feb 16 16:49:19 2009; tduong

Depends: R (>= 1.4.0), KernSmooth, rpanel, ks (>= 1.6.3)
Description: Feature significance for multivariate kernel density
Packaged: Mon Apr 6 14:08:25 2009; tduong

Packaged: Thu Sep 3 11:48:16 2009; tduong

Depends: R (>= 1.4.0), KernSmooth, ks (>= 1.6.3), tcltk

Depends: R (>= 1.4.0), KernSmooth, ks (>= 1.8.0), tcltk

Title: Measurement Level Independent Feature Correlation Matrix
Authors@R: c(person("Guido", "Moeser", role = c("aut", "cre"),
Description: Uses three different correlation coefficients to
calculate measurement-level adequate correlations in a feature matrix:
Packaged: 2020-05-18 13:57:00 UTC; Dr. Guido Möser

Authors@R: person("Richard", "Davis", email = "davisconsulting@gmail.com", role = c("aut", "cre"))
Description: Finds modelling features through a detailed analysis of model residuals using 'rpart' classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model prediction differs from the actual target variable. S. Chatterjee, A. S. Hadi (2006) <doi:10.1002/0470055464>.
Suggests: png, knitr, Metrics, mlr, gbm, randomForest
Imports: rpart, rpart.plot, plyr, grDevices