Description: Performs Modal Clustering (MAC) including Hierarchical
the results are summarised as a hierarchical tree. Associated
Depends: R (>= 2.14.0), mvtnorm, zoo, class

Depends: R (>= 2.14.0), mvtnorm, zoo, class, MASS

Description: Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors.  These model-based non-parametric clustering techniques can extract  clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.

Description: Bootstrap cross-validation for ROC, AUC and Brier score to
assess and compare predictions of binary status responses.
Maintainer: Thomas A. Gerds <tag@biostat.ku.dk>

Title: Validation of risk prediction models

Description: Bootstrap cross-validation for ROC, AUC and Brier score to assess and compare predictions of binary status responses.

Description: Bootstrap cross-validation for ROC, AUC and Brier score to assess
and compare predictions of binary status responses.
Suggests: party, rpart, glmnet, rms, randomForest, randomForestSRC
Collate: 'AucMethods.R' 'BrierMethods.R' 'ConfScore.R' 'MgCheck.R'

Package: ModelMap
Type: Package
Title: Creates Random Forest and Stochastic Gradient Boosting Models,
and applies them to GIS .img files to build detailed prediction
maps.
Version: 1.1.11
Date: 2009-03-27
Depends: R (>= 2.8.1), randomForest, gbm, PresenceAbsence, rgdal
Author: Elizabeth Freeman, Tracey Frescino
Maintainer: Elizabeth Freeman <eafreeman@fs.fed.us>
Description: This package will create sophisticated models of training
data and validate the models with an independant test set,
cross validation, or in the case of Random Forest Models, with
Out OF Bag (OOB) predictions on the training data. It will
creat graphs and tables of the model validation results. It
will apply these models to GIS .img files of predictors to
create detailed prediction surfaces. It will handle large
predictor files for map making, by reading in the .img files in
chuncks, and output to the .txt file the prediction for each
data chunk, before reading the next chenk of data.
License: Unlimited
Packaged: Fri Mar 27 14:07:49 2009; eafreeman
Repository: CRAN
Date/Publication: 2009-03-28 10:20:34