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r-rcppensmallen
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'Ensmallen' is a templated C++ mathematical optimization library (by the 'MLPACK' team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library and pairs the appropriate header files from 'armadillo' through the 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2, 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under similar terms. Note that 'Ensmallen' requires a compiler that supports 'C++11' and 'Armadillo' 9.800 or later.
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2025-04-22 |
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r-rcppdl
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This package is based on the C++ code from Yusuke Sugomori, which implements basic machine learning methods with many layers (deep learning), including dA (Denoising Autoencoder), SdA (Stacked Denoising Autoencoder), RBM (Restricted Boltzmann machine) and DBN (Deep Belief Nets).
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2025-04-22 |
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r-rcppdist
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The 'Rcpp' package provides a C++ library to make it easier to use C++ with R. R and 'Rcpp' provide functions for a variety of statistical distributions. Several R packages make functions available to R for additional statistical distributions. However, to access these functions from C++ code, a costly call to the R functions must be made. 'RcppDist' provides a header-only C++ library with functions for additional statistical distributions that can be called from C++ when writing code using 'Rcpp' or 'RcppArmadillo'. Functions are available that return a 'NumericVector' as well as doubles, and for multivariate or matrix distributions, 'Armadillo' vectors and matrices. 'RcppDist' provides functions for the following distributions: the four parameter beta distribution; the location- scale t distribution; the truncated normal distribution; the truncated t distribution; a truncated location-scale t distribution; the triangle distribution; the multivariate normal distribution*; the multivariate t distribution*; the Wishart distribution*; and the inverse Wishart distribution*. Distributions marked with an asterisk rely on 'RcppArmadillo'.
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2025-04-22 |
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r-rcppde
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An efficient C++ based implementation of the 'DEoptim' function which performs global optimization by differential evolution. Its creation was motivated by trying to see if the old approximation "easier, shorter, faster: pick any two" could in fact be extended to achieving all three goals while moving the code from plain old C to modern C++. The initial version did in fact do so, but a good part of the gain was due to an implicit code review which eliminated a few inefficiencies which have since been eliminated in 'DEoptim'.
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2025-04-22 |
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r-rcppcwb
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'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query engine to efficiently analyze large corpora (<https://cwb.sourceforge.io>). 'RcppCWB' is licensed under the GNU GPL-3, in line with the GPL-3 license of the 'CWB' (<https://www.r-project.org/Licenses/GPL-3>). The 'CWB' relies on 'pcre2' (BSD license, see <http://www.pcre.org/licence.txt>) and 'GLib' (LGPL license, see <https://www.gnu.org/licenses/lgpl-3.0.en.html>). See the file LICENSE.note for further information. The package includes modified code of the 'rcqp' package (GPL-2, see <https://cran.r-project.org/package=rcqp>). The original work of the authors of the 'rcqp' package is acknowledged with great respect, and they are listed as authors of this package. To achieve cross-platform portability (including Windows), using 'Rcpp' for wrapper code is the approach used by 'RcppCWB'.
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2025-04-22 |
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r-rcppcnpy
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The 'cnpy' library written by Carl Rogers provides read and write facilities for files created with (or for) the 'NumPy' extension for 'Python'. Vectors and matrices of numeric types can be read or written to and from files as well as compressed files. Support for integer files is available if the package has been built with -std=c++11 which should be the default on all platforms since the release of R 3.3.0.
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2025-04-22 |
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r-rcppclassic
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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.
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2025-04-22 |
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r-rcppcctz
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'Rcpp' Access to the 'CCTZ' timezone library is provided. 'CCTZ' is a C++ library for translating between absolute and civil times using the rules of a time zone. The 'CCTZ' source code, released under the Apache 2.0 License, is included in this package. See <https://github.com/google/cctz> for more details.
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2025-04-22 |
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r-rcppbdt
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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 R's 64) to present a 'ptime' object (but this needs recompilation with a #define set).
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2025-04-22 |
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r-rcppapt
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The 'APT Package Management System' provides Debian and Debian-derived Linux systems with a powerful system to resolve package dependencies. This package offers access directly from R. This can only work on a system with a suitable 'libapt-pkg-dev' installation so functionality is curtailed if such a library is not found.
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2025-04-22 |
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r-rcppannoy
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'Annoy' is a small C++ library for Approximate Nearest Neighbors written for efficient memory usage as well an ability to load from / save to disk. This package provides an R interface by relying on the 'Rcpp' package, exposing the same interface as the original Python wrapper to 'Annoy'. See <https://github.com/spotify/annoy> for more on 'Annoy'. 'Annoy' is released under Version 2.0 of the Apache License. Also included is a small Windows port of 'mmap' which is released under the MIT license.
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2025-04-22 |
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r-rcdd
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R interface to (some of) cddlib (<https://github.com/cddlib/cddlib>). Converts back and forth between two representations of a convex polytope: as solution of a set of linear equalities and inequalities and as convex hull of set of points and rays. Also does linear programming and redundant generator elimination (for example, convex hull in n dimensions). All functions can use exact infinite-precision rational arithmetic.
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2025-04-22 |
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r-rcassandra
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This packages provides a direct interface (without the use of Java) to the most basic functionality of Apache Cassanda such as login, updates and queries.
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2025-04-22 |
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r-rblpapi
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An R Interface to 'Bloomberg' is provided via the 'Blp API'.
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2025-04-22 |
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r-rbf
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A robust backfitting algorithm for additive models based on (robust) local polynomial kernel smoothers. It includes both bounded and re-descending (kernel) M-estimators, and it computes predictions for points outside the training set if desired. See Boente, Martinez and Salibian-Barrera (2017) <doi:10.1080/10485252.2017.1369077> and Martinez and Salibian-Barrera (2021) <doi:10.21105/joss.02992> for details.
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2025-04-22 |
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r-rbeta2009
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The package contains functions to generate random numbers from the beta distribution and random vectors from the Dirichlet distribution.
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2025-04-22 |
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r-rbeast
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Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.
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2025-04-22 |
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r-rbart
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A model of the form Y = f(x) + s(x) Z is fit where functions f and s are modeled with ensembles of trees and Z is standard normal. This model is developed in the paper 'Heteroscedastic BART Via Multiplicative Regression Trees' (Pratola, Chipman, George, and McCulloch, 2019, <arXiv:1709.07542v2>). BART refers to Bayesian Additive Regression Trees. See the R-package 'BART'. The predictor vector x may be high dimensional. A Markov Chain Monte Carlo (MCMC) algorithm provides Bayesian posterior uncertainty for both f and s. The MCMC uses the recent innovations in Efficient Metropolis--Hastings proposal mechanisms for Bayesian regression tree models (Pratola, 2015, Bayesian Analysis, <doi:10.1214/16-BA999>).
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2025-04-22 |
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r-rbacon
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An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for deposits, through combining radiocarbon and other dates with prior information on accumulation rates and their variability. See Blaauw & Christen (2011).
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2025-04-22 |
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r-raverage
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Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) <https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.
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2025-04-22 |
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r-rasterkernelestimates
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Performs kernel based estimates on in-memory raster images from the raster package. These kernel estimates include local means variances, modes, and quantiles. All results are in the form of raster images, preserving original resolution and projection attributes.
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2025-04-22 |
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r-raschsampler
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MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.
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2025-04-22 |
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r-rare
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Implementation of an alternating direction method of multipliers algorithm for fitting a linear model with tree-based lasso regularization, which is proposed in Algorithm 1 of Yan and Bien (2018) <arXiv:1803.06675>. The package allows efficient model fitting on the entire 2-dimensional regularization path for large datasets. The complete set of functions also makes the entire process of tuning regularization parameters and visualizing results hassle-free.
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2025-04-22 |
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r-rapror
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Calculate sketches of a data set reducing the number of observations using random projections. These can be used for Bayesian or frequentist linear regression on large data sets as described in Geppert et. al (2017) <doi:10.1007/s11222-015-9608-z>.
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2025-04-22 |
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r-rapiserialize
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Access to the internal R serialization code is provided for use by other packages at the C function level by using the registration of native function mechanism. Client packages simply include a single header file RApiSerializeAPI.h provided by this package. This packages builds on the Rhpc package by Ei-ji Nakama and Junji Nakano which also includes a (partial) copy of the file src/main/serialize.c from R itself. The R Core group is the original author of the serialization code made available by this package.
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2025-04-22 |
