Datasets for "Statistics: UnLocking the Power of Data"
Datasets for "Statistics:Unlocking the Power of Data" by
Lock, Lock, Lock, Lock and Lock

Datasets for "Statistics: Unlocking the Power of Data" by Lock^5
Datasets for the first and second editions of the book.  Older editions of revised data often have an extra 1e in the name.

person(given = "Robin",
family = "Lock",
role = c("aut", "cre"),
email = "rlock@stlawu.edu")
Datasets for the third edition of "Statistics: Unlocking the Power of Data" by Lock^5
Includes version of datasets from earlier editions.

Datasets for 'Statistics: Unlocking the Power of Data'
Data sets and other
utilities for 'Statistics: Unlocking the Power of Data'
by Lock, Lock, Lock, Lock and Lock.

Data sets and other utilities for
'Statistics: Unlocking the Power of Data'
by Lock, Lock, Lock, Lock and Lock
(ISBN : 978-0-470-60187-7, http://lock5stat.com/).

Maximum likelihood estimation of a log-concave density
Madeleine Cule, Robert Gramacy, Richard Samworth
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.

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

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

Madeleine Cule, Robert Gramacy, Richard Samworth, Yining Chen

Log-Concave Density Estimation in Arbitrary Dimensions
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>).

Tools for assessing equivalence of similar Logistic Regression models.

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.