Distributed ‘glm’ for Big Data using ‘ddR’ API (glm.ddR)
Distributed training and prediction of generalized linear models using ‘ddR’ (Distributed Data Structures) API in the ‘ddR’ package.
Deep Learning for R (mxnet)
The MXNet R packages brings flexible and efficient GPU computing and state-of-art deep learning to R.
• It enables you to write seamless tensor/matrix computation with multiple GPUs in R.
• It also enables you to construct and customize the state-of-art deep learning models in R, and apply them to tasks such as image classification and data science challenges.
Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution (MvBinary)
Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.