Description: Datasets for the third edition of "Statistics: Unlocking the Power of Data" by Lock^5
Includes version of datasets from earlier editions.

Title: Datasets for 'Statistics: Unlocking the Power of Data'
utilities for 'Statistics: Unlocking the Power of Data'

Description: Data sets and other utilities for
'Statistics: Unlocking the Power of Data'

Title: Maximum likelihood estimation of a log-concave density
Author: Madeleine Cule, Robert Gramacy, Richard Samworth
Description: Computes the maximum likelihood estimator from an i.i.d. sample of data from a log-concave density in any number of dimensions.  Plots are available for 1- and 2-d data.
License: LGPL (Lesser GNU Public Licence)
Packaged: Wed May 30 18:09:15 2007; bobby

Packaged: Tue Dec  4 14:08:52 2007; mlc40

Packaged: Mon Aug  6 11:10:54 2007; mlc40

Packaged: Tue Apr  8 00:24:39 2008; mlc40

Depends: R (>= 2.3), akima, MASS, geometry
Packaged: Sun Jul  6 09:47:26 2008; mlc40

Title: Log-concave Density Estimation in Arbitrary Dimensions
Description: Computes a log-concave (maximum likelihood) estimator for i.i.d. data in any number of dimensions.

Description: Computes a log-concave (maximum likelihood) estimator for
i.i.d. data in any number of dimensions.

Author: Madeleine Cule, Robert Gramacy, Richard Samworth, Yining Chen

Suggests: logcondens, rgl, mclust, tkrplot, tcltk

Suggests: logcondens, rgl, mclust, tkrplot

Title: Log-Concave Density Estimation in Arbitrary Dimensions
Imports: MASS, mclust, mvtnorm, rgl, tkrplot
Description: Software for computing a log-concave (maximum likelihood) estimator for i.i.d. data in any number of dimensions. For a detailed description of the method see Cule, Samworth and Stewart (2010, Journal of Royal Statistical Society Series B, <doi:10.1111/j.1467-9868.2010.00753.x>).

Description: Tools for assessing equivalence of similar Logistic Regression models.

Depends: LogicReg, CircStats, gtools, plotrix
Description: Classification ensemble based on logic regression trees.
Used for classification of binary responses based on binary
predictors and for identification of important variables and
variable interactions predictive of a binary outcome.