Designing group acceptance sampling inspection plans under time-truncated life tests. The package calculates the required minimum number of groups subject to a consumer's risk constraint on the probability of acceptance. Users can supply failure probabilities obtained from any lifetime distribution, allowing the methodology to be applied without restricting the analysis to a particular probability model. The package also provides a function for plotting the required minimum number of groups against the termination ratio. Saha et al. (2025) <doi:10.1007/s41872-025-00305-w>; Tripathi et al. (2020) <doi:10.1080/02664763.2020.1759031>; Tripathi and Aslam (2024) <doi:10.1285/i20705948v17n3p636>.

This package provides fababean-specific functions, processes and parameter sets used by fababean model in the Agricultural Production Systems sIMulator ('APSIM') Next Generation. Includes tools for crop process calculations, parameter management and customisation, enabling users to explore, modify and apply fababean model components.

Set of tools to manipulate the JDemetra+ workspaces. Based on the RJDemetra package (which interfaces with version 2 of the JDemetra+ (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially recommended to the members of the European Statistical System (ESS) and the European System of Central Banks). This package provides access to additional workspace manipulation functions such as metadata manipulation, raw paths and wrangling of several workspaces simultaneously. These additional functionalities are useful as part of a CVS data production chain.

This package contains functions to retrieve, organize, and visualize weather data from the NCEP/NCAR Reanalysis (<https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html>) and NCEP/DOE Reanalysis II (<https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html>) datasets. Data are queried via the Internet and may be obtained for a specified spatial and temporal extent or interpolated to a point in space and time. We also provide functions to visualize these weather data on a map. There are also functions to simulate flight trajectories according to specified behavior using either NCEP wind data or data specified by the user.

This package provides a novel sufficient-dimension reduction method is robust against outliers using alpha-distance covariance and manifold-learning in dimensionality reduction problems. Please refer Hsin-Hsiung Huang, Feng Yu & Teng Zhang (2024) <doi:10.1080/10485252.2024.2313137> for the details.

This package provides a S4 class has been created such that complex operations can be executed on each cell of a raster map. The raster of objects contains a raster map with the addition of a list of generic objects: one object for each raster cells. It allows to write few lines of R code for complex map algebra. Two environmental applications about frequency analysis of raster map of precipitation and creation of a raster map of soil water retention curves have been presented.

This package implements the Zig-Zag algorithm (Bierkens, Fearnhead, Roberts, 2016) <arXiv:1607.03188> applied and Bouncy Particle Sampler <arXiv:1510.02451> for a Gaussian target and Student distribution.

Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH.

Univariate and multivariate versions of risk-based control charts. Univariate versions of control charts, such as the risk-based version of X-bar, Moving Average (MA), Exponentially Weighted Moving Average Control Charts (EWMA), and Cumulative Sum Control Charts (CUSUM) charts. The risk-based version of the multivariate T2 control chart. Plot and summary functions. Kosztyan et. al. (2016) <doi:10.1016/j.eswa.2016.06.019>.

This package provides functions to calculate several ecological indices of individual and population niche width (Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis W, and Roughgarden's WIC/TNW) to assess individual specialization based on data of resource use. Resource use can be quantified by counts of categories, measures of mass or length, or proportions. Monte Carlo resampling procedures are available for hypothesis testing against multinomial null models. Details are provided in Zaccarelli et al. (2013) <doi:10.1111/2041-210X.12079> and associated references.

This package provides a programmatic interface to the Species+ <https://speciesplus.net/> database via the Species+/CITES Checklist API <https://api.speciesplus.net/>.

Mathematical and statistical tools for computational biology in drug discovery. Functions are designed for high performance. Implements the hierarchical fuzzy multi-linkage partitioning method proposed by Huang et al. (2007) <doi:10.1186/gb-2007-8-9-r183>.

Creation, estimation, and prediction of random weight neural networks (RWNN), Schmidt et al. (1992) <doi:10.1109/ICPR.1992.201708>, including popular variants like extreme learning machines, Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, sparse RWNN, Zhang et al. (2019) <doi:10.1016/j.neunet.2019.01.007>, and deep RWNN, Henrà quez et al. (2018) <doi:10.1109/IJCNN.2018.8489703>. It further allows for the creation of ensemble RWNNs like bagging RWNN, Sui et al. (2021) <doi:10.1109/ECCE47101.2021.9595113>, boosting RWNN, stacking RWNN, and ensemble deep RWNN, Shi et al. (2021) <doi:10.1016/j.patcog.2021.107978>.

Restricted Cubic Splines were performed to explore the shape of association form of "U, inverted U, L" shape and test linearity or non-linearity base on "Cox,Logistic,linear,quasipoisson" regression, and auto output Restricted Cubic Splines figures. rcssci package could automatically draw RCS graphics with Y-axis "OR,HR,RR,beta". The Restricted Cubic Splines method were based on Suli Huang (2022) <doi:10.1016/j.ecoenv.2022.113183>,Amit Kaura (2019) <doi:10.1136/bmj.l6055>, and Harrell Jr (2015, ISBN:978-3-319-19424-0 (Print) 978-3-319-19425-7 (Online)).

This package provides a straightforward, easy-to-use and robust parsing package which aims to digest history files from the popular messenger service WhatsApp in all locales and from all devices.

This package implements a general framework for globally optimizing user-specified objective functionals over interpretable binary weight functions represented as sparse decision trees, called ROOT (Rashomon Set of Optimal Trees). It searches over candidate trees to construct a Rashomon set of near-optimal solutions and derives a summary tree highlighting stable patterns in the optimized weights. ROOT includes a built-in generalizability mode for identifying subgroups in trial settings for transportability analyses (Parikh et al. (2025) <doi:10.1080/01621459.2025.2495319>).

This package provides tools for working with Type S (Sign) and Type M (Magnitude) errors, as proposed in Gelman and Tuerlinckx (2000) <doi:10.1007/s001800000040> and Gelman & Carlin (2014) <doi:10.1177/1745691614551642>. In addition to simply calculating the probability of Type S/M error, the package includes functions for calculating these errors across a variety of effect sizes for comparison, and recommended sample size given "tolerances" for Type S/M errors. To improve the speed of these calculations, closed forms solutions for the probability of a Type S/M error from Lu, Qiu, and Deng (2018) <doi:10.1111/bmsp.12132> are implemented. As of 1.0.0, this includes support only for simple research designs. See the package vignette for a fuller exposition on how Type S/M errors arise in research, and how to analyze them using the type of design analysis proposed in the above papers.

Processing and analysis of targeted sequencing data. The package provides a user-friendly interface for core VSEARCH (Rognes et al. (2016), <doi:10.7717/peerj.2584>) functions, in addition to tools for visualization and parameter tuning.

Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.

This package provides functions to conduct hypothesis tests and derive confidence intervals for quantiles, linear combinations of quantiles, ratios of dependent linear combinations and differences and ratios of all of the above for comparisons between independent samples. Additionally, quantile-based measures of inequality are also considered.

An R Commander plug-in for the WorldFlora package. It was mainly developed to show work flows and scripts for first-time users.

An integrated package for constructing random forest prediction intervals using a fast implementation package ranger'. This package can apply the following three methods described in Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman (2019) <doi:10.1080/00031305.2019.1585288>: the out-of-bag prediction interval, the split conformal method, and the quantile regression forest.

This package contains functions to create regulatory-style statistical reports. Originally designed to create tables, listings, and figures for the pharmaceutical, biotechnology, and medical device industries, these reports are generalized enough that they could be used in any industry. Generates text, rich-text, PDF, HTML, and Microsoft Word file formats. The package specializes in printing wide and long tables with automatic page wrapping and splitting. Reports can be produced with a minimum of function calls, and without relying on other table packages. The package supports titles, footnotes, page header, page footers, spanning headers, page by variables, and automatic page numbering.

Download and read the public, non-identified microdata of the RAIS (Relação Anual de Informações Sociais), the annual census of formal employment relationships and establishments published by the Brazilian Ministry of Labour and Employment through the PDET FTP server <ftp://ftp.mtps.gov.br/pdet/microdados/RAIS/>. Lists the years and archives available on the server, resolves which regional or state archive holds a given state, downloads it with an idempotent local cache, and reads the 7z archives as a stream, filtering by state and selecting columns before anything is kept in memory, so that a single state can be extracted from a regional file of tens of millions of records. Handles the two header generations of the files (up to the RAIS 2022 and from the RAIS 2023 onwards) with the same normalized column names, provides the official record layout and a helper to consolidate the employment stock, admissions, separations and December payroll.