Tools for Descriptive Statistics

A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.

R Interface to the 'Protocol Buffers' 'API' (Version 2 or 3)

Protocol Buffers are a way of encoding structured data in an efficient yet extensible format. Google uses Protocol Buffers for almost all of its internal 'RPC' protocols and file formats. Additional documentation is available in two included vignettes one of which corresponds to our 'JSS' paper (2016, . A sufficiently recent version of 'Protocol Buffers' library is required; currently version 3.3.0 from 2017 is the tested minimum.

'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'.

Simple but Efficient Rowwise Jobs

Creating efficiently new column(s) in a data frame (including tibble) by applying a function one row at a time.

'Rcpp' Integration for 'GNU GSL' Vectors and Matrices

'Rcpp' integration for 'GNU GSL' vectors and matrices The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for scientific computing. It is particularly useful for C and C++ programs as it provides a standard C interface to a wide range of mathematical routines. There are over 1000 functions in total with an extensive test suite. The 'RcppGSL' package provides an easy-to-use interface between 'GSL' data structures and R using concepts from 'Rcpp' which is itself a package that eases the interfaces between R and C++. This package also serves as a prime example of how to build a package that uses 'Rcpp' to connect to another third-party library. The 'autoconf' script, 'inline' plugin and example package can all be used as a stanza to write a similar package against another library.

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.