The goal of statcodelists is to promote the reuse and exchange of statistical information and related metadata with making the internationally standardized SDMX code lists available for the R user. SDMX has been published as an ISO International Standard (ISO 17369). The metadata definitions, including the codelists are updated regularly according to the standard. The authoritative version of the code lists made available in this package is <https://sdmx.org/?page_id=3215/>.

This package implements the sparse clustering methods of Witten and Tibshirani (2010): "A framework for feature selection in clustering"; published in Journal of the American Statistical Association 105(490): 713-726.

Tidy snapshot of every episode of the public-television travel series Rick Steves Europe (2000-2025), enriched with IMDB ratings, geocoded destinations, ISO country codes, episode thumbnails, and descriptive summaries. Designed as a companion dataset for introductory data analysis and visualization in the spirit of the moderndive textbook: every row is an episode, every column is a candidate for a plot or a join. Compiled from public sources for teaching purposes; not an official or verified Rick Steves Europe dataset, and shared with the permission of the Rick Steves Europe team.

New tools for post-selection inference, for use with forward stepwise regression, least angle regression, the lasso, and the many means problem. The lasso function implements Gaussian, logistic and Cox survival models.

Estimates sparse regression models (i.e., with few non-zero coefficients) in high-dimensional multi-task learning and transfer learning settings, as proposed by Rauschenberger et al. (2025) <https://orbilu.uni.lu/handle/10993/63425>.

Quickly and flexibly calculates weights for survey data, in order to correct for survey non-response or other sampling issues. Uses rake weighting, a common technique also know as rim weighting or iterative proportional fitting. This technique allows for weighting on multiple variables, even when the interlocked distribution of the two variables is not known. Interacts with Thomas Lumley's survey package, as described in Lumley, Thomas (2011, ISBN:978-1-118-21093-2). Adds additional functionality, more adaptable syntax, and error-checking to the base weighting functionality in survey.'.

Determining potential output and the output gap - two inherently unobservable variables - is a major challenge for macroeconomists. sectorgap features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying economic output fluctuations consistent with subsectors of the economy. The proposed model is able to capture various correlations between output and a set of aggregate as well as subsector indicators. Estimation of the latent states and parameters is achieved using a simple Gibbs sampling procedure and various plotting options facilitate the assessment of the results. For details on the methodology and an illustrative example, see Streicher (2024) <https://www.research-collection.ethz.ch/handle/20.500.11850/653682>.

This package provides a dplyr'-like interface for grouped summaries on large arrow datasets. Complete summaries are attempted in arrow first. When a result can be reconstructed from arrow'-computable sufficient statistics, an explicit MapReduce-style reduction and R finalization strategy is available. For arbitrary R functions that require raw group observations, complete groups are materialized in bounded chunks, with optional parallel execution and shared-memory processing. The MapReduce strategy follows the programming model described by Dean and Ghemawat (2008) <doi:10.1145/1327452.1327492>.

This package provides a collection of functions that enable easy access and updating of a database of data over time. More specifically, the package facilitates type-2 history for data-warehouses and provides a number of Quality of life improvements for working on SQL databases with R. For reference see Ralph Kimball and Margy Ross (2013, ISBN 9781118530801).

Interact with the Smartsheet platform through the Smartsheet API 2.0. <https://smartsheet.redoc.ly/>. API is an acronym for application programming interface; the Smartsheet API allows users to interact with Smartsheet sheets directly within R.

Implementation of the boosting procedure with the simulation and extrapolation approach to address variable selection and estimation for high-dimensional data subject to measurement error in predictors. It can be used to address generalized linear models (GLM) in Chen (2023) <doi: 10.1007/s11222-023-10209-3> and the accelerated failure time (AFT) model in Chen and Qiu (2023) <doi: 10.1111/biom.13898>. Some relevant references include Chen and Yi (2021) <doi:10.1111/biom.13331> and Hastie, Tibshirani, and Friedman (2008, ISBN:978-0387848570).

The function generates and plots random snowflakes. Each snowflake is defined by a given diameter, width of the crystal, color, and random seed. Snowflakes are plotted in such way that they always remain round, no matter what the aspect ratio of the plot is. Snowflakes can be created using transparent colors, which creates a more interesting, somewhat realistic, image. Images of the snowflakes can be separately saved as svg files and used in websites as static or animated images.

This package provides plotting utilities supporting packages in the easystats ecosystem (<https://github.com/easystats/easystats>) and some extra themes, geoms, and scales for ggplot2'. Color scales are based on <https://materialui.co/>. References: Lüdecke et al. (2021) <doi:10.21105/joss.03393>.

Maximum likelihood estimation for stochastic frontier analysis (SFA) of production (profit) and cost functions. The package includes the basic stochastic frontier for cross-sectional or pooled data with several distributions for the one-sided error term (i.e., Rayleigh, gamma, Weibull, lognormal, uniform, generalized exponential and truncated skewed Laplace), the latent class stochastic frontier model (LCM) as described in Dakpo et al. (2021) <doi:10.1111/1477-9552.12422>, for cross-sectional and pooled data, and the sample selection model as described in Greene (2010) <doi:10.1007/s11123-009-0159-1>, and applied in Dakpo et al. (2021) <doi:10.1111/agec.12683>. Several possibilities in terms of optimization algorithms are proposed.

Simplifies the process of generating samples from a variety of probability distributions, allowing users to quickly create data frames for demonstrations, troubleshooting, or teaching purposes. Data is available in multiple sizesâ small, medium, and large. For more information, refer to the package documentation.

We have designed this package to address experimental scenarios involving multiple covariates. It focuses on construction of Optimal Covariate Designs (OCDs), checking space filling property of the developed design. The primary objective of the package is to generate OCDs using four methods viz., M array method, Juxtapose method, Orthogonal Integer Array and Hadamard method. The package also evaluates space filling properties of both the base design and OCDs using the MaxPro criterion, providing a meaningful basis for comparison. In addition, it includes tool to visualize the spread offered by the design points in the form of scatterplot, which help users to assess distribution and coverage of design points.

Identifies single nucleotide variants in next-generation sequencing data by estimating their local false discovery rates. For more details, see Karimnezhad, A. and Perkins, T. J. (2024) <doi:10.1038/s41598-024-51958-z>.

This package implements multiple allocation and selection strategies of sampling to construct core collections primarily from clustered or grouped germplasm collection data. Provides methods for allocating entries to clusters/groups based on group sizes, group-wise distance-based diversity metrics, and group-wise diversity index estimates. Includes procedures for selecting entries within clusters/groups through random sampling, genetic distance-based approaches, and optimized diversity metricâ based selection methods. See the package documentation for more, including full list of references for the methods implemented.

An R API providing access to a relational database with macroeconomic time series data for South Africa, obtained from the South African Reserve Bank (SARB) and Statistics South Africa (STATSSA), and updated on a weekly basis via the EconData <https://www.econdata.co.za/> platform and automated scraping of the SARB and STATSSA websites. The database is maintained at the Department of Economics at Stellenbosch University.

Fitting dimension reduction methods to data lying on two-dimensional sphere. This package provides principal geodesic analysis, principal circle, principal curves proposed by Hauberg, and spherical principal curves. Moreover, it offers the method of locally defined principal geodesics which is underway. The detailed procedures are described in Lee, J., Kim, J.-H. and Oh, H.-S. (2021) <doi:10.1109/TPAMI.2020.3025327>. Also see Kim, J.-H., Lee, J. and Oh, H.-S. (2020) <arXiv:2003.02578>.

An algorithm that trains a meta-learning procedure that combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. This package works on top of the caret package and proceeds in a forward-step manner. More specifically, it builds and tests learners starting from very few attributes until it includes a maximal number of attributes by increasing the number of attributes at each step. Hence, for each fixed number of attributes, the algorithm tests various (randomly selected) learners and picks those with the best performance in terms of training error. Throughout, the algorithm uses the information coming from the best learners at the previous step to build and test learners in the following step. In the end, it outputs a set of strong low-dimensional learners.

This package provides a toolkit for common statistical analyses including descriptive statistics, Student's t-tests (one-sample, independent, and paired), one-way and two-way Analysis of Variance (ANOVA), Multivariate Analysis of Variance (MANOVA), chi-square tests, Fisher's Exact Test, McNemar's Test, correlation analysis, simple and multiple linear regression, logistic regression, Friedman Test, and non-parametric tests (Mann-Whitney U, Wilcoxon Signed Rank, and Kruskal-Wallis). Each function automatically interprets results in plain English, reporting effect sizes, confidence intervals, and p-value interpretations, and prints relevant assumption checks by default. A context argument allows users to describe their study design, echoed back alongside the interpretation as a reminder to read results in that context. Post-hoc tests are automatically applied following significant results. A master function automatically detects the appropriate test based on the structure of the input data. Methods are based on Cohen, J. (1988) <doi:10.4324/9780203771587>, Tukey, J. W. (1949) <doi:10.2307/3001913>, and Shapiro and Wilk (1965) <doi:10.2307/2333709>.

Minimal R client for the Screenshotbase API to render website screenshots and query account status. Provides functions to set the API key, call the status endpoint, and take a screenshot as a raw image response.

This package provides a small POSIX sockets interface to R, enabling direct network communication from R for applications that need low-level socket control or lightweight client and server connections. This package provides IPv4 and IPv6 support with TCP/UDP protocols. It functional API with socket_create and an R6 interface through Socket object.