This package provides a native R implementation for encoding and decoding sixel graphics (<https://vt100.net/docs/vt3xx-gp/chapter14.html>), and a dedicated sixel graphics device that allows plots to be rendered directly within compatible terminal emulators.

Screens all .R', .Rmd', and .qmd files to extract the name of packages used in a project. This package detects packages called with library(foo)', require(foo)', foo::bar() and use("foo", "bar") and adds these dependencies in the DESCRIPTION file in the sections Depends, Imports, and Suggests.

Create and manipulate hypergraph objects. This early version of rhype allows for the output of matrices associated with the hypergraphs themselves. It also uses these matrices to calculate hypergraph spectra and perform spectral comparison. Functionality coming soon includes calculation of hyperpaths and hypergraph centrality measures.

Pattern matching, extraction, replacement and other string processing operations using Google's RE2 <https://github.com/google/re2> regular-expression engine. Consistent interface (similar to stringr'). RE2 uses finite-automata based techniques, and offers a fast and safe alternative to backtracking regular-expression engines like those used in stringr', stringi and other PCRE implementations.

This package provides a lightweight toolkit to validate new observations when computing their predictions with a predictive model. The validation process consists of two steps: (1) record relevant statistics and meta data of the variables in the original training data for the predictive model and (2) use these data to run a set of basic validation tests on the new set of observations.

Combine multiple data files from a common directory. The data files will be read into R and bound together, creating a single large data.frame. A general function is provided along with a specific function for data that was collected using the open-source experiment builder OpenSesame <https://osdoc.cogsci.nl/>.

The RQuantLib package makes parts of QuantLib accessible from R The QuantLib project aims to provide a comprehensive software framework for quantitative finance. The goal is to provide a standard open source library for quantitative analysis, modeling, trading, and risk management of financial assets.

We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. It returns either maximum likelihood estimates of unknown model parameters (hyper-parameters) via an EM algorithm or posterior samples of those parameters via MCMC. Also see Tak et al. (2019) <doi:10.1080/10618600.2019.1704295>.

Utility functions to retrieve data from the UK National River Flow Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions: <https://nrfa.ceh.ac.uk/help/costs-terms-and-conditions>). The package contains R wrappers to the UK NRFA data temporary-API. There are functions to retrieve stations falling in a bounding box, to generate a map and extracting time series and general information. The package is fully described in Vitolo et al (2016) "rnrfa: An R package to Retrieve, Filter and Visualize Data from the UK National River Flow Archive" <https://journal.r-project.org/archive/2016/RJ-2016-036/RJ-2016-036.pdf>.

Estimates of standard errors of popular risk and performance measures for asset or portfolio returns using methods as described in Chen and Martin (2021) <doi:10.21314/JOR.2020.446>.

Seamless extraction of river networks from digital elevation models data. The package allows analysis of digital elevation models that can be either externally provided or downloaded from open source repositories (thus interfacing with the elevatr package). Extraction is performed via the D8 flow direction algorithm of TauDEM (Terrain Analysis Using Digital Elevation Models), thus interfacing with the traudem package. Resulting river networks are compatible with functions from the OCNet package. See Carraro (2023) <doi:10.5194/hess-27-3733-2023> for a presentation of the package.

Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.

This package provides an R interface for using AmCharts Library. Based on htmlwidgets', it provides a global architecture to generate JavaScript source code for charts. Most of classes in the library have their equivalent in R with S4 classes; for those classes, not all properties have been referenced but can easily be added in the constructors. Complex properties (e.g. JavaScript object) can be passed as named list. See examples at <https://datastorm-open.github.io/introduction_ramcharts/> and <https://www.amcharts.com/> for more information about the library. The package includes the free version of AmCharts Library. Its only limitation is a small link to the web site displayed on your charts. If you enjoy this library, do not hesitate to refer to this page <https://www.amcharts.com/online-store/> to purchase a licence, and thus support its creators and get a period of Priority Support. See also <https://www.amcharts.com/about/> for more information about AmCharts company.

Toolkit to interact with the SOAP web services of the SEI (Sistema Eletronico de Informacoes), the electronic system for document and process management widely used by Brazilian public administration bodies. Provides functions to build the SOAP envelopes, perform the requests, handle SOAP faults, and parse the XML responses into data frames. Covers process and document queries, listing services, write operations (creating processes and documents, sending and signing off processes, blocks, deadlines and markers) and the permission services of the companion SIP system. Note that access to the web services is restricted by the server to previously authorized network addresses. For more information about the SEI system and its web services see <https://www.gov.br/gestao/pt-br/assuntos/processo-eletronico-nacional>.

Useful tools for determining whether two samples are from the same distribution. Utilizes a robust method to address the problematic structure of the similarity graph constructed from high-dimensional data. The method is provided in Yichuan Bai and Lynna Chu (2023) <arXiv:2307.12325>.

Downloads and imports agricultural data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) <https://www.agriculture.gov.au/abares> and the Australian Bureau of Statistics (ABS) <https://www.abs.gov.au>. Supports multiple data formats including spreadsheets, commaâ separated value (CSV) files, and geospatial data such as shapefiles and GeoTIFFs. Covers topics such as broadacre crops, livestock, soils, commodities and related agricultural information. The package standardises field names and data formats to improve interoperability and simplify analysis. It also streamlines the import of geospatial data and corrects common issues found in these data sources upon loading.

Interoperability between Rcpp and the C++11 array and tuple types. Linking to this package allows fixed-length std::array objects to be converted to and from equivalent R vectors, and std::tuple objects converted to lists, via the as() and wrap() functions. There is also experimental support for std::span from C++20'.

Population genetic data such as Single Nucleotide Polymorphisms (SNPs) is often used to identify genomic regions that have been under recent natural or artificial selection and might provide clues about the molecular mechanisms of adaptation. One approach, the concept of an Extended Haplotype Homozygosity (EHH), introduced by (Sabeti 2002) <doi:10.1038/nature01140>, has given rise to several statistics designed for whole genome scans. The package provides functions to compute three of these, namely: iHS (Voight 2006) <doi:10.1371/journal.pbio.0040072> for detecting positive or Darwinian selection within a single population as well as Rsb (Tang 2007) <doi:10.1371/journal.pbio.0050171> and XP-EHH (Sabeti 2007) <doi:10.1038/nature06250>, targeted at differential selection between two populations. Various plotting functions are included to facilitate visualization and interpretation of these statistics.

This package provides functions allowing the user to recursively extract frequent patterns and confident rules according to indicators of minimal support and minimal confidence. These functions are described in "Recursive Association Rule Mining" Abdelkader Mokkadem, Mariane Pelletier, Louis Raimbault (2020) <arXiv:2011.14195>.

Perform a supervised data analysis on a database through a shiny graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines.

Designed to support the application of plant trait data providing easy applicable functions for the basic steps of data preprocessing, e.g. data import, data exploration, selection of columns and rows, excluding trait data according to different attributes, geocoding, long- to wide-table transformation, and data export. rtry was initially developed as part of the TRY R project to preprocess trait data received via the TRY database.

Computes linear Bayesian spectral estimates from multirate data for second-order stationary time series. Provides credible intervals and methods for plotting various spectral estimates. Please see the paper `Should we sample a time series more frequently? (doi below) for a full description of and motivation for the methodology.

Create, Plot and Compare Replication Timing Profiles. The method is described in Muller et al., (2014) <doi: 10.1093/nar/gkt878>.

Enhances the R Optimization Infrastructure (ROI) package by registering the CPLEX commercial solver. It allows for solving mixed integer quadratically constrained programming (MIQPQC) problems as well as all variants/combinations of LP, QP, QCP, IP.