Exact Arbitrary-Precision Decimal Vectors

Arbitrary-precision vectors with an exact decimal representation, avoiding the rounding surprises of binary floating point. Built on the 'mpdecimal' C library, arithmetic is governed by an explicit decimal context controlling precision, rounding, and signaling, and vectors integrate with 'vctrs' for use in data frames, 'tibble' objects, summaries, and common numeric workflows. Missing values, signed zeros, infinities, and not-a-number values are supported throughout. The arithmetic model follows Cowlishaw (2009) "General Decimal Arithmetic" < https://speleotrove.com/decimal/decarith.html>.

Download Indicators from UNICEF Data Warehouse

An R client to fetch SDMX (Statistical Data and Metadata eXchange) CSV series from the UNICEF Data Warehouse < https://data.unicef.org/>. Part of a trilingual suite also available for 'Python' and 'Stata'. Features include automatic pagination, caching with memoisation, country name lookups, metadata versioning (vintages), and comprehensive indicator support for SDG (Sustainable Development Goals) monitoring.

Interval Estimation of Linear Models with Heteroskedasticity

Calculates the interval estimates for the parameters of linear models with heteroscedastic regression using bootstrap - (Wild Bootstrap) and double bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using the percentile bootstrap and percentile bootstrap double. The package can calculate consistent estimates of the covariance matrix of the parameters of linear regression models with heteroscedasticity of unknown form. The package also provides a function to consistently calculate the covariance matrix of the parameters of linear models with heteroscedasticity of unknown form. The bootstrap methods exported by the package are based on the master's thesis of the first author, available at < https://raw.githubusercontent.com/prdm0/hcci/master/references/dissertacao_mestrado.pdf>. The hcci package in previous versions was cited in the book VINOD, Hrishikesh D. Hands-on Intermediate Econometrics Using R: Templates for Learning Quantitative Methods and R Software. 2022, p. 441, ISBN 978-981-125-617-2 (hardcover). The simple bootstrap schemes are based on the works of Cribari-Neto F and Lima M. G. (2009) , while the double bootstrap schemes for the parameters that index the linear models with heteroscedasticity of unknown form are based on the works of Beran (1987) . The use of bootstrap for the calculation of interval estimates in regression models with heteroscedasticity of unknown form from a weighting of the residuals was proposed by Wu (1986) . This bootstrap scheme is known as weighted or wild bootstrap.

File Encryption with the 'age' Format

An idiomatic R interface to the 'age' file encryption format (< https://age-encryption.org/v1>), backed by a vendored copy of the 'agec' C implementation (< https://git.sr.ht/~min/agec>). Encrypt and decrypt raw vectors, files, and strings for one or more X25519 recipients or with a passphrase, with optional ASCII armor. Cryptography is vendored and randomness is drawn from the operating system, so the package has no external library dependencies.

Open Welfare Data Brazil

Tools for collecting municipal-level data < http://www.transparencia.gov.br/swagger-ui.html> from several Brazilian governmental social programs.

Core Inflation

Provides access to core inflation functions. Four different core inflation functions are provided. The well known trimmed means, exclusion and double weighing methods, alongside the new Triple Filter method introduced in Ferreira et al. (2016) < https://goo.gl/UYLhcj>.

Semantic UI Support for Shiny

Creating a great user interface for your Shiny apps can be a hassle, especially if you want to work purely in R and don't want to use, for instance HTML templates. This package adds support for a powerful UI library Fomantic UI - < https://fomantic-ui.com/> (before Semantic). It also supports universal UI input binding that works with various DOM elements.

Download, Compile and Link 'OpenBLAS' Library with R

The 'ropenblas' package (< https://prdm0.github.io/ropenblas/>) is useful for users of any 'GNU/Linux' distribution. It will be possible to download, compile and link the 'OpenBLAS' library (< https://www.openblas.net/>) with the R language, always by the same procedure, regardless of the 'GNU/Linux' distribution used. With the 'ropenblas' package it is possible to download, compile and link the latest version of the 'OpenBLAS' library even the repositories of the 'GNU/Linux' distribution used do not include the latest versions of 'OpenBLAS'. If of interest, older versions of the 'OpenBLAS' library may be considered. Linking R with an optimized version of 'BLAS' (< https://netlib.org/blas/>) may improve the computational performance of R code. The 'OpenBLAS' library is an optimized implementation of 'BLAS' that can be easily linked to R with the 'ropenblas' package.

Parse 'HTML' with a Bundled 'Gumbo' Parser

Parses real-world 'HTML' with a bundled copy of the 'Gumbo' parser (< https://codeberg.org/gumbo-parser/gumbo-parser>), which follows the 'WHATWG' parsing algorithm, so that no system library is required. Documents become immutable trees navigated with a documented subset of 'CSS' selectors. Attributes, text, lists, tables, links, forms and page metadata ('JSON-LD', microdata) are extracted into ordinary character vectors, lists and data frames, and nodes convert to 'Markdown'. Input is a string, raw bytes, a file, a URL or a connection, and raw input is decoded as browsers decode it, from a byte-order mark or a '' declaration. Parsing is bounded by limits on input size, native memory and nesting depth.

Decision Models with Multi Attribute Utility Theory

Provides functions for the creation, evaluation and test of decision models based in Multi Attribute Utility Theory (MAUT). Can process and evaluate local risk aversion utilities for a set of indexes, compute utilities and weights for the whole decision tree defining the decision model and simulate weights employing Dirichlet distributions under addition constraints in weights. Also includes other rating analysis methods as for example the Colley, Offensive - Defensive ratings and the ranking aggregation with Borda count.