About: Package for Deep Architectures and Restricted Boltzmann Machines Changes:Fetched by rcranrobot on 20180101 00:00:07.467914

About: Bayesian Additive Regression Trees Changes:Initial Announcement on mloss.org by rcranrobot

About: Learning Discrete Bayesian Network Classifiers from Data Changes:Fetched by rcranrobot on 20160501 00:00:04.546512

About: TBEEF, a doubly ensemble framework for recommendation and prediction problems. Changes:Included the final technical report.

About: Bayesian Additive Regression Trees Changes:Fetched by rcranrobot on 20180201 00:00:05.401450

About: Efficient and Flexible Distributed/Mobile Deep Learning Framework, for python, R, Julia and more Changes:This version comes with Distributed and Mobile Examples

About: Optunity is a library containing various optimizers for hyperparameter tuning. Hyperparameter tuning is a recurrent problem in many machine learning tasks, both supervised and unsupervised.This package provides several distinct approaches to solve such problems including some helpful facilities such as crossvalidation and a plethora of score functions. Changes:This minor release has the same feature set as Optunity 1.1.0, but incorporates several bug fixes, mostly related to the specification of structured search spaces.

About: Data Sets, Functions and Examples from the Book Changes:Fetched by rcranrobot on 20180101 00:00:07.925283

About: R package implementing statistical test and post hoc tests to compare multiple algorithms in multiple problems. Changes:Initial Announcement on mloss.org.

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily. The newest version of xgboost now supports distributed learning on various platforms such as hadoop, mpi and scales to even larger problems Changes:

About: Generalized Ridge Regression (with special advantage for p >> n cases) Changes:Fetched by rcranrobot on 20180201 00:00:05.701156

About: Big Random Forests Changes:Fetched by rcranrobot on 20151101 00:00:04.072762

About: The package "fastclime" provides a method of recover the precision matrix efficiently by applying parametric simplex method. The computation is based on a linear optimization solver. It also contains a generic LP solver and a parameterized LP solver using parametric simplex method. Changes:Initial Announcement on mloss.org.

About: Estimates statistical significance of association between variables and their principal components (PCs). Changes:Initial Announcement on mloss.org.

About: hapFabia is an R package for identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data. It detects 100 times smaller segments than previous methods. Changes:o citation update o plot function improved

About: hapFabia is an R package for identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data. Changes:o citation update o plot function improved

About: Evolutionary Learning of Globally Optimal Trees Changes:Fetched by rcranrobot on 20140501 00:00:05.459097

About: Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Builtin priorss include coefficient priors (fixed, flexible and hyperg priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Changes:Initial Announcement on mloss.org.

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:
