Rcpp : Seamless R and C++ Integration

2,504

1,690

We introduce the C++ application and R package ranger. The software is a fast implementation of random forests for high dimensional data. Ensembles of classification, regression and survival trees are supported. We describe the implementation, provide examples, validate the package with a reference implementation, and compare runtime and memory usage with other implementations. The new software proves to scale best with the number of features, samples, trees, and features tried for splitting. Finally, we show that ranger is the fastest and most memory efficient implementation of random forests to analyze data on the scale of a genome-wide association study.

Section: Methodsmentioning

confidence: 99%

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

Wright¹,

Ziegler²

2017

2,504

1,690

“…The Rcpp package (Eddelbuettel and François 2011;Eddelbuettel 2013) is used to facilitate this integration of the R and C++ code for these objects. Each method is wrapped individually which allows us to add user-friendly custom error handling, type coercion, and performance improvements at the cost of a more verbose implementation.…”

Section: Under the Hood: S4 Classes And Methodsmentioning

confidence: 99%

RProtoBuf: Efficient Cross-Language Data Serialization inR

Eddelbuettel¹,

Stokely²,

Ooms³

2016

Self Cite

Modern data collection and analysis pipelines often involve a sophisticated mix of applications written in general purpose and specialized programming languages. Many formats commonly used to import and export data between different programs or systems, such as CSV or JSON, are verbose, inefficient, not type-safe, or tied to a specific programming language. Protocol Buffers are a popular method of serializing structured data between applications -while remaining independent of programming languages or operating systems. They offer a unique combination of features, performance, and maturity that seems particularly well suited for data-driven applications and numerical computing. The RProtoBuf package provides a complete interface to Protocol Buffers from the R environment for statistical computing. This paper outlines the general class of data serialization requirements for statistical computing, describes the implementation of the RProtoBuf package, and illustrates its use with example applications in large-scale data collection pipelines and web services.

“…Efficient implementation of functions responsible for log-density evaluation is therefore a rewarding optimization strategy which can be combined with the strategies discussed in Section 4.2. The Rcpp (Eddelbuettel and François 2011) framework offers a convenient way to port R functions to C++. Here we use the RcppArmadillo (Eddelbuettel and Sanderson 2014) package for its convenient matrix algebra operations to transform the log-likelihood component of the log-posterior (as it takes the majority of time for large data, compared to the log-prior): While the result is a decent speedup given the relatively small effort put in, yet the impact is not as significant as the previous two strategies.…”

Section: High-performance Pdf Evaluationmentioning

confidence: 99%

Multivariate-From-Univariate MCMC Sampler: The R Package MfUSampler

Mahani¹,

Sharabiani²

2017

The R package MfUSampler provides Markov chain Monte Carlo machinery for generating samples from multivariate probability distributions using univariate sampling algorithms such as the slice sampler and the adaptive rejection sampler. The multivariate wrapper performs a full cycle of univariate sampling steps, one coordinate at a time. In each step, the latest sample values obtained for other coordinates are used to form the conditional distributions. The concept is an extension of Gibbs sampling where each step involves, not an independent sample from the conditional distribution, but a Markov transition for which the conditional distribution is invariant. The software relies on proportionality of conditional distributions to the joint distribution to implement a thin wrapper for producing conditionals. Examples illustrate basic usage as well as methods for improving performance. By encapsulating the multivariate-from-univariate logic, package MfUSampler provides a reliable package for rapid prototyping of custom Bayesian models while allowing for incremental performance optimizations such as taking advantage of conditional independence, and high-performance implementation of function evaluations. Utility functions for MCMC diagnostics as well as sample-based construction of predictive posterior distributions are provided in MfUSampler.