This package provides functions to retrieve data and metadata from providers that disseminate data by means of SDMX web services. SDMX (Statistical Data and Metadata eXchange) is a standard that has been developed with the aim of simplifying the exchange of statistical information. More about the SDMX standard and the SDMX Web Services can be found at: <https://sdmx.org>.

Deterministic, name-blind detection of variable roles (group, outcome, survival time and event, paired and agreement measurements, repeated measures, scale items, subject identifier, covariate) in tabular data. Roles are assigned from each column's information-theoretic signature -- Shannon entropy, normalized mutual information, and distributional shape -- rather than from column names, so renaming columns to col_1', col_2', ... does not change the result ("Data inspice, non nomen"). An optional, capped name-based hint and automatic header-row detection are also provided. No large language models and no external data transmission. Extracted from the MDStatR biostatistics engine; see Boynukara (2026) <doi:10.5281/zenodo.20707791>.

This package provides a Minimal Example Package which demonstrates mlpack use via C++ Code from R.

Understanding heterogeneous causal effects based on pretreatment covariates is a crucial step in modern empirical work in data science. Building on the recent developments in Calonico et al (2025) <doi:10.48550/arXiv.2503.13696>, this package provides tools for estimation and inference of heterogeneous treatment effects in Regression Discontinuity (RD) Designs. The package includes two main commands: rdhte to conduct estimation and robust bias-corrected inference for conditional RD treatment effects (given choice of bandwidth parameter); rdbwhte', which implements automatic bandwidth selection methods; and rdhte_lincom to test linear combinations of parameters.

Ray Shooting Depth functions are provided for bivariate analysis. This mainly includes functions for computing the bivariate depth as well as RS median. Drawing functions for depth bags are also provided.

This package provides a memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can (1) effectively process large data, (2) rapidly evaluate population structure, (3) efficiently estimate variance components several algorithms, (4) implement parallel-accelerated association tests of markers three methods, (5) globally efficient design on GWAS process computing, (6) enhance visualization of related information. rMVP contains three models GLM (Alkes Price (2006) <DOI:10.1038/ng1847>), MLM (Jianming Yu (2006) <DOI:10.1038/ng1702>) and FarmCPU (Xiaolei Liu (2016) <doi:10.1371/journal.pgen.1005767>); variance components estimation methods EMMAX (Hyunmin Kang (2008) <DOI:10.1534/genetics.107.080101>;), FaSTLMM (method: Christoph Lippert (2011) <DOI:10.1038/nmeth.1681>, R implementation from GAPIT2': You Tang and Xiaolei Liu (2016) <DOI:10.1371/journal.pone.0107684> and SUPER': Qishan Wang and Feng Tian (2014) <DOI:10.1371/journal.pone.0107684>), and HE regression (Xiang Zhou (2017) <DOI:10.1214/17-AOAS1052>).

This package provides functions to download and parse robots.txt files. Ultimately the package makes it easy to check if bots (spiders, crawler, scrapers, ...) are allowed to access specific resources on a domain.

This package provides utility functions that extend the capabilities of the reference-based multiple imputation package rbmi'. It supports clinical trial analysis workflows with functions for managing imputed datasets, applying analysis methods across imputations, and tidying results for reporting.

This package provides functions to generate plots and tables for comparing independently- sampled populations. Companion package to "A Primer on Visualizations for Comparing Populations, Including the Issue of Overlapping Confidence Intervals" by Wright, Klein, and Wieczorek (2019) <DOI:10.1080/00031305.2017.1392359> and "A Joint Confidence Region for an Overall Ranking of Populations" by Klein, Wright, and Wieczorek (2020) <DOI:10.1111/rssc.12402>.

Rho is used to test the generalization of inter rater reliability (IRR) statistics. Calculating rho starts by generating a large number of simulated, fully-coded data sets: a sizable collection of hypothetical populations, all of which have a kappa value below a given threshold -- which indicates unacceptable agreement. Then kappa is calculated on a sample from each of those sets in the collection to see if it is equal to or higher than the kappa in then real sample. If less than five percent of the distribution of samples from the simulated data sets is greater than actual observed kappa, the null hypothesis is rejected and one can conclude that if the two raters had coded the rest of the data, we would have acceptable agreement (kappa above the threshold).

Advanced response surface analysis. The main function RSA computes and compares several nested polynomial regression models (full second- or third-order polynomial, shifted and rotated squared difference model, rising ridge surfaces, basic squared difference model, asymmetric or level-dependent congruence effect models). The package provides plotting functions for 3d wireframe surfaces, interactive 3d plots, and contour plots. Calculates many surface parameters (a1 to a5, principal axes, stationary point, eigenvalues) and provides standard, robust, or bootstrapped standard errors and confidence intervals for them.

Non-linear inversion for hypocenter estimation and analysis of seismic data collected continuously, or in trigger mode. The functions organize other functions from RSEIS and GEOmap to help researchers pick, locate, and store hypocenters for detailed seismic investigation. Error ellipsoids and station influence are estimated via jackknife analysis. References include Iversen, E. S., and J. M. Lees (1996)<doi:10.1785/BSSA0860061853>.

The method generate() is extended for spatial multi-site stochastic generation of daily precipitation. It generates precipitation occurrence in several sites using logit regression (Generalized Linear Models) and the approach by D.S. Wilks (1998) <doi:10.1016/S0022-1694(98)00186-3> .

ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models. Shaffer, D.W., Collier, W., & Ruis, A.R. (2016).

This package provides a programmatic interface to openfisheries.org'. This package is part of the rOpenSci suite (http://ropensci.org).

This package provides utilities for processing and analyzing dyadic data collected using a round-robin design, in which each person in a group rates or interacts with every other person on at least one variable. Data manipulation functions prepare datasets for dyadic data analysis by creating the actor and partner dummy variables required by the social relations model (SRM). Analysis functions implement the SRM using multilevel modeling via a custom nlme covariance class ('pdSRM'), following the approach of Snijders and Kenny (1999) <doi:10.1111/j.1475-6811.1999.tb00204.x> and Knight and Humphrey (2019) <doi:10.1037/0000115-019>. The package estimates group, actor, partner, and relationship variance components along with generalized and dyadic reciprocity correlations, and supports both null and fixed-effects models.

Finds the k nearest neighbours for every point in a given dataset using Jose Luis nanoflann library. There is support for exact searches, fixed radius searches with kd trees and two distances, the Euclidean and Manhattan'. For more information see <https://github.com/jlblancoc/nanoflann>. Also, the nanoflann library is exported and ready to be used via the linking to mechanism.

Relational Class Analysis (RCA) is a method for detecting heterogeneity in attitudinal data (as described in Goldberg A., 2011, Am. J. Soc, 116(5)).

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.

This package provides a platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.

Inference for the treatment effect with possibly invalid instrumental variables via TSHT ('Guo et al. (2018) <doi:10.1111/rssb.12275>) and SearchingSampling ('Guo (2023) <doi:10.1093/jrsssb/qkad049>), which are effective for both low- and high-dimensional covariates and instrumental variables; test of endogeneity in high dimensions ('Guo et al. (2018) <doi:10.1016/j.jeconom.2018.07.002>).

Perform a Relative Weights Analysis (RWA) (a.k.a. Key Drivers Analysis) as per the method described in Tonidandel & LeBreton (2015) <DOI:10.1007/s10869-014-9351-z>, with its original roots in Johnson (2000) <DOI:10.1207/S15327906MBR3501_1>. In essence, RWA decomposes the total variance predicted in a regression model into weights that accurately reflect the proportional contribution of the predictor variables, which addresses the issue of multi-collinearity. In typical scenarios, RWA returns similar results to Shapley regression, but with a significant advantage on computational performance.

Estimates the rearranged dependence measure ('RDM') of two continuous random variables for different underlying measures. Furthermore, it provides a method to estimate the (SI)-rearrangement copula using empirical checkerboard copulas. It is based on the theoretical results presented in Strothmann et al. (2022) <arXiv:2201.03329> and Strothmann (2021) <doi:10.17877/DE290R-22733>.

This provides a robust estimator for stochastic frontier models, employing the Minimum Density Power Divergence Estimator (MDPDE) for enhanced robustness against outliers. Additionally, it includes a function to recommend the optimal tuning parameter, alpha, which controls the robustness of the MDPDE. The methods implemented in this package are based on Song et al. (2017) <doi:10.1016/j.csda.2016.08.005>.