Enhances the R Optimization Infrastructure ('ROI') package with the DEoptim and DEoptimR package. DEoptim is used for unconstrained optimization and DEoptimR for constrained optimization.

Calculates the Iberian Actuarial Climate Index and its componentsâ including temperature, precipitation, wind power, and sea level dataâ to support climate change analysis and risk assessment. See "Zhou et al." (2023) <doi:10.26360/2023_3> for further details.

Range Modeling Metadata Standards (RMMS) address three challenges: they (i) are designed for convenience to encourage use, (ii) accommodate a wide variety of applications, and (iii) are extensible to allow the community of range modelers to steer it as needed. RMMS are based on a data dictionary that specifies a hierarchical structure to catalog different aspects of the range modeling process. The dictionary balances a constrained, minimalist vocabulary to improve standardization with flexibility for users to provide their own values. Merow et al. (2019) <DOI:10.1111/geb.12993> describe the standards in more detail. Note that users who prefer to use the R package ecospat can obtain it from <https://github.com/ecospat/ecospat>.

Regularized calibrated estimation for causal inference and missing-data problems with high-dimensional data, based on Tan (2020a) <doi:10.1093/biomet/asz059>, Tan (2020b) <doi:10.1214/19-AOS1824> and Sun and Tan (2020) <arXiv:2009.09286>.

REDUCE is a portable general-purpose computer algebra system supporting scalar, vector, matrix and tensor algebra, symbolic differential and integral calculus, arbitrary precision numerical calculations and output in LaTeX format. REDUCE is based on Lisp and is available on the two dialects Portable Standard Lisp ('PSL') and Codemist Standard Lisp ('CSL'). The redcas package provides an interface for executing arbitrary REDUCE code interactively from R', returning output as character vectors. R code and REDUCE code can be interspersed. It also provides a specialized function for calling the REDUCE feature for solving systems of equations, returning the output as an R object designed for the purpose. A further specialized function uses REDUCE features to generate LaTeX output and post-processes this for direct use in LaTeX documents, e.g. using Sweave'.

Connection to the Redis (or Valkey') key/value store using the C-language client library hiredis (included as a fallback) with MsgPack encoding provided via RcppMsgPack headers. It now also includes the pub/sub functions from the rredis package.

Loading calls data from Ringostat API'. See <https://help.ringostat.com/knowledge-base/article/integration-with-ringostat-via-api>.

MsgPack header files are provided for use by R packages, along with the ability to access, create and alter MsgPack objects directly from R. MsgPack 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'.

This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm.

Measure single-storage water supply system performance using resilience, reliability, and vulnerability metrics; assess storage-yield-reliability relationships; determine no-fail storage with sequent peak analysis; optimize release decisions for water supply, hydropower, and multi-objective reservoirs using deterministic and stochastic dynamic programming; generate inflow replicates using parametric and non-parametric models; evaluate inflow persistence using the Hurst coefficient.

Generates graphs, CSV files, and coordinates related to river valleys when calling the riverbuilder() function.

This package provides tools to read various file types into one list of data structures, usually, but not limited to, data frames. Excel files are read sheet-wise, i.e., all or a selection of sheets can be read. Field delimiters and decimal separators are determined automatically.

We provide functions to perform an empirical small telescopes analysis. This package contains 2 functions, SmallTelescopes() and EstimatePower(). Users only need to call SmallTelescopes() to conduct the analysis. For more information on small telescopes analysis see Uri Simonsohn (2015) <doi:10.1177/0956797614567341>.

The goal of rlowdb is to provide a lightweight, file-based JSON database. Inspired by LowDB in JavaScript', it generates an intuitive interface for storing, retrieving, updating, and querying structured data without requiring a full-fledged database system. Ideal for prototyping, small-scale applications, and lightweight data management needs.

This package provides a tool for undergraduate and graduate courses in open-channel hydraulics. Provides functions for computing normal and critical depths, steady-state water surface profiles (e.g. backwater curves) and unsteady flow computations (e.g. flood wave routing) as described in Koohafkan MC, Younis BA (2015). "Open-channel computation with R." The R Journal, 7(2), 249â 262. <doi: 10.32614/RJ-2015-034>.

This package provides a client for the API of OpenDota. OpenDota is a web service which is provide DOTA2 real time data. Data is collected through the Steam WebAPI. With ROpenDota you can easily grab the latest DOTA2 statistics in R programming such as latest match on official international competition, analyzing your or enemy performance to learn their strategies,etc. Please see <https://github.com/rosdyana/ROpenDota> for more information.

R functions for the computation of the truncated maximum likelihood and the robust accelerated failure time regression for gaussian and log-Weibull case.

This holds r markdown and quarto templates for academic papers and slide decks. It also has templates to create research projects which contain academic papers as vignettes.

This package provides an R interface to the NiftyReg image registration tools <https://github.com/KCL-BMEIS/niftyreg>. Linear and nonlinear registration are supported, in two and three dimensions.

Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We not only provide implementations for the basic concepts of RST and FRST but also popular algorithms that derive from those theories. The methods included in the package can be divided into several categories based on their functionality: discretization, feature selection, instance selection, rule induction and classification based on nearest neighbors. RST was introduced by ZdzisÅ aw Pawlak in 1982 as a sophisticated mathematical tool to model and process imprecise or incomplete information. By using the indiscernibility relation for objects/instances, RST does not require additional parameters to analyze the data. FRST is an extension of RST. The FRST combines concepts of vagueness and indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in 1965) and RST.

R package based on Rcpp for MeCab': Yet Another Part-of-Speech and Morphological Analyzer. The purpose of this package is providing a seamless developing and analyzing environment for CJK texts. This package utilizes parallel programming for providing highly efficient text preprocessing posParallel() function. For installation, please refer to README.md file.

Predicts morphological parameters of rorquals (e.g. body mass, flipper length, maximum engulfment capacity) from body length using allometric equations from Kahane-Rapport and Goldbogen (2018) <doi:10.1002/jmor.20846>.

Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface. For details, see Gramacy & Polson (2012 <doi:10.1214/12-BA719>).

R implementation of Maximum Likelihood Principal Component Analysis The main idea of this package is to have an alternative way of PCA for subspace modeling that considers measurement errors. More details can be found in Peter D. Wentzell (2009) <doi:10.1016/B978-0-444-64165-6.03029-9>.