Quick Serialization of R Objects

Provides functions for quickly writing and reading any R object to and from disk.

'Rcpp' Bindings for the Boost Date_Time Library

Access to Boost Date_Time functionality for dates, durations (both for days and date time objects), time zones, and posix time ('ptime') is provided by using 'Rcpp modules'. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of 64 with R) to present a 'ptime' object (but this needs recompilation with a #define set).

Examples using 'Rcpp' to Interface R and C++

Examples for Seamless R and C++ integration The 'Rcpp' package contains a C++ library that facilitates the integration of R and C++ in various ways. This package provides some usage examples. Note that the documentation in this package currently does not cover all the features in the package. The site < https://gallery.rcpp.org> regroups a large number of examples for 'Rcpp'.

Simulating Longitudinal Data with Causal Inference Applications

A flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples.

C++ Classes to Embed R in C++ (and C) Applications

C++ classes to embed R in C++ (and C) applications A C++ class providing the R interpreter is offered by this package making it easier to have "R inside" your C++ application. As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. Numerous examples are provided in the nine subdirectories of the examples/ directory of the installed package: standard, 'mpi' (for parallel computing), 'qt' (showing how to embed 'RInside' inside a Qt GUI application), 'wt' (showing how to build a "web-application" using the Wt toolkit), 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C interface and 'Ruby' illustration. The examples use 'GNUmakefile(s)' with GNU extensions, so a GNU make is required (and will use the 'GNUmakefile' automatically). 'Doxygen'-generated documentation of the C++ classes is available at the 'RInside' website as well.

Keyword Assisted Topic Models

Fits keyword assisted topic models (keyATM) using collapsed Gibbs samplers. The keyATM combines the latent dirichlet allocation (LDA) models with a small number of keywords selected by researchers in order to improve the interpretability and topic classification of the LDA. The keyATM can also incorporate covariates and directly model time trends. The keyATM is proposed in Eshima, Imai, and Sasaki (2024) .

A Toolbox for Writing Pretty Papers and Reports

A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based reports and to prettify the output of various estimated models.

Examples using 'RcppClassic' to Interface R and C++

The 'Rcpp' package contains a C++ library that facilitates the integration of R and C++ in various ways via a rich API. This API was preceded by an earlier version which has been deprecated since 2010 (but is still supported to provide backwards compatibility in the package 'RcppClassic'). This package 'RcppClassicExamples' provides usage examples for the older, deprecated API. There is also a corresponding package 'RcppExamples' with examples for the newer, current API which we strongly recommend as the basis for all new development.

Fast and Stable Fitting of Generalized Linear Models using 'RcppEigen'

Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner (2011) to help safeguard against convergence issues.

Deprecated 'classic' 'Rcpp' 'API'

The 'RcppClassic' package provides a deprecated C++ library which facilitates the integration of R and C++. New projects should use the new 'Rcpp' 'API' in the 'Rcpp' package.