About: Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bidirectional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license. Changes:New version November 2013

About: Cox models by likelihood based boosting for a single survival endpoint or competing risks Changes:Fetched by rcranrobot on 20140401 00:00:04.738601

About: A wrapper algorithm for allrelevant feature selection Changes:Fetched by rcranrobot on 20140401 00:00:04.400248

About: FABIA is a biclustering algorithm that clusters rows and columns of a matrix simultaneously. Consequently, members of a row cluster are similar to each other on a subset of columns and, analogously, members of a column cluster are similar to each other on a subset of rows. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. Applications include detection of transcriptional modules in gene expression data and identification of haplotypes/>identity by descent< consisting of rare variants obtained by next generation sequencing. Changes:CHANGES IN VERSION 2.8.0NEW FEATURES
CHANGES IN VERSION 2.4.0
CHANGES IN VERSION 2.3.1NEW FEATURES
2.0.0:
1.4.0:

About: Gradient Boosting Changes:Fetched by rcranrobot on 20140401 00:00:04.470817

About: Generalized Ridge Regression (with special advantage for p >> n cases) Changes:Fetched by rcranrobot on 20140401 00:00:04.268631

About: Generalized linear and additive models by likelihood based boosting Changes:Fetched by rcranrobot on 20130401 00:00:04.893311

About: Feedforward Neural Networks and Multinomial LogLinear Models Changes:Fetched by rcranrobot on 20130401 00:00:06.544403

About: Graphical user interface for data mining in R Changes:Fetched by rcranrobot on 20130401 00:00:07.700426

About: Heteroscedastic Discriminant Analysis Changes:Fetched by rcranrobot on 20130401 00:00:05.551691

About: L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model Changes:Fetched by rcranrobot on 20130401 00:00:05.305206

About: Generalized Boosted Regression Models Changes:Fetched by rcranrobot on 20130401 00:00:05.019963

About: Classification rule based on Bayesian naive Bayes models with feature selection bias corrected Changes:Fetched by rcranrobot on 20121201 00:00:07.510624

About: Bayesian Prediction with Highorder Interactions Changes:Fetched by rcranrobot on 20121201 00:00:03.777292

About: L1 constrained estimation aka `lasso' Changes:Fetched by rcranrobot on 20130401 00:00:05.967868

About: Improved Predictors Changes:Fetched by rcranrobot on 20130401 00:00:05.613011

About: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model Changes:Fetched by rcranrobot on 20130401 00:00:06.939105

About: The UniverSVM is a SVM implementation written in C/C++. Its functionality comprises large scale transduction via CCCP optimization, sparse solutions via CCCP optimization and datadependent [...] Changes:Minor changes: fix bug on set_alphas_b0 function (thanks to Ferdinand Kaiser  ferdinand.kaiser@tut.fi)

About: Breiman and Cutler's random forests for classification and regression Changes:Fetched by rcranrobot on 20130401 00:00:07.638240

About: Simpler use of data mining methods (e.g. NN and SVM) in classification and regression. Changes:Fetched by rcranrobot on 20130401 00:00:08.226306
