About: Novel R toolbox for collaborative filtering recommender systems.

Initial Announcement on mloss.org.

About: Package for Deep Architectures and Restricted Boltzmann Machines

Fetched by r-cran-robot on 2018-01-01 00:00:07.467914

About: Learning Discrete Bayesian Network Classifiers from Data

Fetched by r-cran-robot on 2016-05-01 00:00:04.546512

About: TBEEF, a doubly ensemble framework for recommendation and prediction problems.

Included the final technical report.

About: Bayesian Additive Regression Trees

Fetched by r-cran-robot on 2018-09-01 00:00:04.269138

About: Efficient and Flexible Distributed/Mobile Deep Learning Framework, for python, R, Julia and more

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 cross-validation and a plethora of score functions.

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

Fetched by r-cran-robot on 2018-01-01 00:00:07.925283

About: R package implementing statistical test and post hoc tests to compare multiple algorithms in multiple problems.

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

• Distributed version of xgboost that runs on YARN, scales to billions of examples

• Direct save/load data and model from/to S3 and HDFS

• Feature importance visualization in R module, by Michael Benesty

• Predict leaf index

• Poisson regression for counts data

• Early stopping option in training

• Native save load support in R and python

• xgboost models now can be saved using save/load in R

• xgboost python model is now pickable

• sklearn wrapper is supported in python module

• Experimental External memory version

About: Generalized Ridge Regression (with special advantage for p >> n cases)

Fetched by r-cran-robot on 2018-05-01 00:00:05.929954

About: Big Random Forests

Fetched by r-cran-robot on 2015-11-01 00:00:04.072762

About: deep learning toolkit in R

Fetched by r-cran-robot on 2018-01-01 00:00:07.583485

About: Estimates statistical significance of association between variables and their principal components (PCs).

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.

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.

o citation update

o plot function improved

About: Evolutionary Learning of Globally Optimal Trees

Fetched by r-cran-robot on 2014-05-01 00:00:05.459097

About: Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priorss include coefficient priors (fixed, flexible and hyper-g priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches.

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 IN VERSION 2.8.0

NEW FEATURES

o rescaling of lapla
o extractPlot does not plot sorted matrices

CHANGES IN VERSION 2.4.0

o spfabia bugfixes

CHANGES IN VERSION 2.3.1

NEW FEATURES

o Getters and setters for class Factorization

2.0.0:

• spfabia: fabia for a sparse data matrix (in sparse matrix format) and sparse vector/matrix computations in the code to speed up computations. spfabia applications: (a) detecting >identity by descent< in next generation sequencing data with rare variants, (b) detecting >shared haplotypes< in disease studies based on next generation sequencing data with rare variants;
• fabia for non-negative factorization (parameter: non_negative);
• changed to C and removed dependencies to Rcpp;
• improved update for lambda (alpha should be smaller, e.g. 0.03);
• introduced maximal number of row elements (lL);
• introduced cycle bL when upper bounds nL or lL are effective;
• reduced computational complexity;
• bug fixes: (a) update formula for lambda: tighter approximation, (b) corrected inverse of the conditional covariance matrix of z;

1.4.0:

• New option nL: maximal number of biclusters per row element;
• Sort biclusters according to information content;
• Improved and extended preprocessing;
• Update to R2.13