**MsgPack’ C++ Header Files** (**RcppMsgPack**)

MessagePack’ is an efficient binary serialization format. It lets you exchange data among multiple languages like ‘JSON’. But it is faster and smaller. Small integers are encoded into a single byte, and typical short strings require only one extra byte in addition to the strings themselves. This package provides headers from the ‘msgpack-c’ implementation for C and C++(11) for use by R, particularly ‘Rcpp’. The included ‘msgpack-c’ headers are licensed under the Boost Software License (Version 1.0); the code added by this package as well the R integration are licensed under the GPL (>= 2). See the files ‘COPYRIGHTS’ and ‘AUTHORS’ for a full list of copyright holders and contributors to ‘msgpack-c’.

**The Matthews Correlation Coefficient** (**mccr**)

The Matthews correlation coefficient (MCC) score is calculated (Matthews BW (1975) <DOI:10.1016/0005-2795(75)90109-9>).

**Uncertainty Quantified Matrix Completion using Bayesian Hierarchical Matrix Factorization** (**BHPMF**)

Fills the gaps of a matrix incorporating a hierarchical side information while providing uncertainty quantification.

**Benchmarks for High-Performance Computing Environments** (**RHPCBenchmark**)

Microbenchmarks for determining the run time performance of aspects of the R programming environment and packages relevant to high-performance computation. The benchmarks are divided into three categories: dense matrix linear algebra kernels, sparse matrix linear algebra kernels, and machine learning functionality.

**Survival Support Vector Analysis** (**survivalsvm**)

Performs support vectors analysis for data sets with survival outcome. Three approaches are available in the package: The regression approach takes censoring into account when formulating the inequality constraints of the support vector problem. In the ranking approach, the inequality constraints set the objective to maximize the concordance index for comparable pairs of observations. The hybrid approach combines the regression and ranking constraints in the same model.

### Like this:

Like Loading...

*Related*