The Stochastic Dominance (SD) is the classical way of comparing two random prospects, using their distribution functions. Almost Stochastic Dominance (ASD) has also been developed to cover the SD failures due to the extreme utility functions. This package focuses on classical and heuristic methods for testing the first and second SD and ASD methods given the probability mass function (PMF) of the random prospects. The goal is to apply these methods easily, efficiently, and effectively on real-world datasets. For more details see Hanoch and Levy (1969) <doi:10.2307/2296431>, Leshno and Levy (2002) <doi:10.1287/mnsc.48.8.1074.169>, and Tzeng et al. (2012) <doi:10.1287/mnsc.1120.1616>.

PaleoClim <http://www.paleoclim.org> (Brown et al. 2019, <doi:10.1038/sdata.2018.254>) is a set of free, high resolution paleoclimate surfaces covering the whole globe. It includes data on surface temperature, precipitation and the standard bioclimatic variables commonly used in ecological modelling, derived from the HadCM3 general circulation model and downscaled to a spatial resolution of up to 2.5 minutes. Simulations are available for key time periods from the Late Holocene to mid-Pliocene. Data on current and Last Glacial Maximum climate is derived from CHELSA (Karger et al. 2017, <doi:10.1038/sdata.2017.122>) and reprocessed by PaleoClim to match their format; it is available at up to 30 seconds resolution. This package provides a simple interface for downloading PaleoClim data in R, with support for caching and filtering retrieved data by period, resolution, and geographic extent.

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.

Extend Rasch and Item Response Theory (IRT) analyses by providing tools for post-processing the output from five major IRT packages (i.e., eRm', psychotools', ltm', mirt', and TAM'). The current version provides the plotPIccc() function, which extracts from the return object of the originating package all information required to draw an extended Person-Item-Map (PIccc), showing any combination of * category characteristic curves (CCCs), * threshold characteristic curves (TCCs), * item characteristic curves (ICCs), * category information functions (CIFs), * item information functions (IIFs), * test information function (TIF), and the * standard error curve (S.E.). for uni- and multidimensional models (as far as supported by each package). It allows for selecting dimensions, items, and categories to plot and offers numerous options to adapt the output. The return object contains all calculated values for further processing.

The APT Package Management System provides Debian and Debian-derived Linux systems with a powerful system to resolve package dependencies. This package offers access directly from R. This can only work on a system with a suitable libapt-pkg-dev installation so functionality is curtailed if such a library is not found.

Rapidly estimates tree-topology from large allele frequency data using Root Distances Method, under a Brownian Motion Model. See Peng et al. (2021) <doi:10.1016/j.ympev.2021.107142>.

The Coinbase Advanced Trade API <https://docs.cdp.coinbase.com/api-reference/advanced-trade-api/rest-api/introduction> lets you manage orders, portfolios, products, and fees with the new v3 endpoints.

Get data from Linkedin Advertising API <https://learn.microsoft.com/en-us/linkedin/marketing/overview?view=li-lms-2023-10>. You can load ad account hierarchy (accounts, users, campaign groups, campaigns and creatives) and also you can load ad analytics data from your Linkedin Ad account.

This package provides functions and methods for manipulating SNOMED CT concepts. The package contains functions for loading the SNOMED CT release into a convenient R environment, selecting SNOMED CT concepts using regular expressions, and navigating the SNOMED CT ontology. It provides the SNOMEDconcept S3 class for a vector of SNOMED CT concepts (stored as 64-bit integers) and the SNOMEDcodelist S3 class for a table of concepts IDs with descriptions. The package can be used to construct sets of SNOMED CT concepts for research (<doi:10.1093/jamia/ocac158>). For more information about SNOMED CT visit <https://www.snomed.org/>.

Interface to the yacas computer algebra system (<http://www.yacas.org/>).

The rank distance correlation <doi:10.1080/01621459.2020.1782223> is computed. Included also is a function to perform permutation based testing.

This package provides a collection of R functions for use with Stock Synthesis, a fisheries stock assessment modeling platform written in ADMB by Dr. Richard D. Methot at the NOAA Northwest Fisheries Science Center. The functions include tools for summarizing and plotting results, manipulating files, visualizing model parameterizations, and various other common stock assessment tasks. This version of r4ss is compatible with Stock Synthesis versions 3.24 through 3.30 (specifically version 3.30.19.01, from April 2022).

This package provides an interface to the Spotify API <https://developer.spotify.com/documentation/web-api/>.

This package implements popular methods for matching in time-varying observational studies. Matching is difficult in this scenario because participants can be treated at different times which may have an influence on the outcomes. The core methods include: "Balanced Risk Set Matching" from Li, Propert, and Rosenbaum (2011) <doi:10.1198/016214501753208573> and "Propensity Score Matching with Time-Dependent Covariates" from Lu (2005) <doi:10.1111/j.1541-0420.2005.00356.x>. Some functions use the Gurobi optimization back-end to improve the optimization problem speed; the gurobi R package and associated software can be downloaded from <https://www.gurobi.com> after obtaining a license.

This package provides a framework that supports creating and extending enterprise Shiny applications using best practices.

An R interface to the typeform <https://www.typeform.com/> application program interface. Also provides functions for downloading your results.

The JSON format is ubiquitous for data interchange, and the simdjson library written by Daniel Lemire (and many contributors) provides a high-performance parser for these files which by relying on parallel SIMD instruction manages to parse these files as faster than disk speed. See the <doi:10.48550/arXiv.1902.08318> paper for more details about simdjson'. This package parses JSON from string, file, or remote URLs under a variety of settings.

It helps you to read (.dim) images with CRS directly into R programming. One can import both Sentinel 1 and 2 images or any processed data with this software.

The regression-based (RB) approach is a method to test the missing data mechanism. This package contains two functions that test the type of missing data (Missing Completely At Random vs Missing At Random) on the basis of the RB approach. The first function applies the RB approach independently on each variable with missing data, using the completely observed variables only. The second function tests the missing data mechanism globally (on all variables with missing data) with the use of all available information. The algorithm is adapted both to continuous and categorical data.

Mass rollup for a Bill of Materials is an example of a class of computations in which elements are arranged in a tree structure and some property of each element is a computed function of the corresponding values of its child elements. Leaf elements, i.e., those with no children, have values assigned. In many cases, the combining function is simple arithmetic sum; in other cases (e.g., mass properties), the combiner may involve other information such as the geometric relationship between parent and child, or statistical relations such as root-sum-of-squares (RSS). This package implements a general function for such problems. It is adapted to specific recursive computations by functional programming techniques; the caller passes a function as the update parameter to rollup() (or, at a lower level, passes functions as the get, set, combine, and override parameters to update_prop()) at runtime to specify the desired operations. The implementation relies on graph-theoretic algorithms from the igraph package of Csárdi, et al. (2006 <doi:10.5281/zenodo.7682609>).

This package provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis. A good reference on these methods is Chapter 10 of Wu, C-F J and Hamada, M (2009) "Experiments: Planning, Analysis, and Parameter Design Optimization" ISBN 978-0-471-69946-0. An early version of the package is documented in Journal of Statistical Software <doi:10.18637/jss.v032.i07>.

This package provides a set of functions to see and interactively adjust a distribution of lessons by day, aiming at homogenizing individual distributions (for each class and teacher).

This package provides a test for the well-specification of the linear instrumental variable model. The test is based on trying to predict the residuals of a two-stage least-squares regression using a random forest. Details can be found in Scheidegger, Londschien and Bühlmann (2025) "A residual prediction test for the well-specification of linear instrumental variable models" <doi:10.48550/arXiv.2506.12771>.

This package provides a platform-independent GUI for design of experiments. The package is implemented as a plugin to the R-Commander, which is a more general graphical user interface for statistics in R based on tcl/tk. DoE functionality can be accessed through the menu Design that is added to the R-Commander menus.