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r-taxa
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Provides classes for storing and manipulating taxonomic data. Most of the classes can be treated like base R vectors (e.g. can be used in tables as columns and can be named). Vectorized classes can store taxon names and authorities, taxon IDs from databases, taxon ranks, and other types of information. More complex classes are provided to store taxonomic trees and user-defined data associated with them.
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2025-03-25 |
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r-taxadb
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Creates a local database of many commonly used taxonomic authorities and provides functions that can quickly query this data.
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2025-03-25 |
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r-targets
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Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, <doi:10.21105/joss.00550>).
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2025-03-25 |
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r-tarchetypes
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Function-oriented Make-like declarative pipelines for Statistics and data science are supported in the 'targets' R package. As an extension to 'targets', the 'tarchetypes' package provides convenient user-side functions to make 'targets' easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the 'drake' R package by Will Landau (2018) <doi:10.21105/joss.00550>.
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2025-03-25 |
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r-taf
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Functions to organize data, methods, and results used in scientific analyses. A TAF analysis consists of four scripts (data.R, model.R, output.R, report.R) that are run sequentially. Each script starts by reading files from a previous step and ends with writing out files for the next step. Convenience functions are provided to version control the required data and software, run analyses, clean residues from previous runs, manage files, manipulate tables, and produce figures. With a focus on stability and reproducible analyses, TAF is designed to have no package dependencies. TAF forms a base layer for the 'icesTAF' package and other scientific applications.
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2025-03-25 |
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r-tables
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Computes and displays complex tables of summary statistics. Output may be in LaTeX, HTML, plain text, or an R matrix for further processing.
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2025-03-25 |
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r-tab
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Contains functions for creating various types of summary tables, e.g. comparing characteristics across levels of a categorical variable and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards models. Functions are available to handle data from simple random samples as well as complex surveys.
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2025-03-25 |
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r-syuzhet
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Extracts sentiment and sentiment-derived plot arcs from text using a variety of sentiment dictionaries conveniently packaged for consumption by R users. Implemented dictionaries include "syuzhet" (default) developed in the Nebraska Literary Lab "afinn" developed by Finn Årup Nielsen, "bing" developed by Minqing Hu and Bing Liu, and "nrc" developed by Mohammad, Saif M. and Turney, Peter D. Applicable references are available in README.md and in the documentation for the "get_sentiment" function. The package also provides a hack for implementing Stanford's coreNLP sentiment parser. The package provides several methods for plot arc normalization.
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2025-03-25 |
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r-tableone
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Creates 'Table 1', i.e., description of baseline patient characteristics, which is essential in every medical research. Supports both continuous and categorical variables, as well as p-values and standardized mean differences. Weighted data are supported via the 'survey' package.
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2025-03-25 |
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r-syslognet
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Send 'syslog' protocol messages to a remote 'syslog' server specified by host name and TCP network port.
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2025-03-25 |
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r-synthpop
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A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data. For a description of the implemented method see Nowok, Raab and Dibben (2016) <doi:10.18637/jss.v074.i11>.
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2025-03-25 |
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r-synth
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Implements the synthetic control group method for comparative case studies as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010, 2011, 2014). The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc.) is exposed to an event or intervention. It provides a data-driven procedure to construct synthetic control units based on a weighted combination of comparison units that approximates the characteristics of the unit that is exposed to the intervention. A combination of comparison units often provides a better comparison for the unit exposed to the intervention than any comparison unit alone.
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2025-03-25 |
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r-symmoments
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Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.
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2025-03-25 |
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r-sylly.en
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public |
Adds support for the English language to the 'sylly' package. Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please consider subscribing to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).
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2025-03-25 |
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r-sweep
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Tidies up the forecasting modeling and prediction work flow, extends the 'broom' package with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions for various forecasting models, and enables converting 'forecast' objects to "tidy" data frames with 'sw_sweep'.
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2025-03-25 |
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r-swimplot
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Used for creating swimmers plots with functions to customize the bars, add points, add lines, add text, and add arrows.
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2025-03-25 |
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r-svdialogs
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public |
Quickly construct standard dialog boxes for your GUI, including message boxes, input boxes, list, file or directory selection, ... In case R cannot display GUI dialog boxes, a simpler command line version of these interactive elements is also provided as fallback solution.
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2025-03-25 |
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r-susier
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Implements methods for variable selection in linear regression based on the "Sum of Single Effects" (SuSiE) model, as described in Wang et al (2020) <DOI:10.1101/501114> and Zou et al (2021) <DOI:10.1101/2021.11.03.467167>. These methods provide simple summaries, called "Credible Sets", for accurately quantifying uncertainty in which variables should be selected. The methods are motivated by genetic fine-mapping applications, and are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse. The fitting algorithm, a Bayesian analogue of stepwise selection methods called "Iterative Bayesian Stepwise Selection" (IBSS), is simple and fast, allowing the SuSiE model be fit to large data sets (thousands of samples and hundreds of thousands of variables).
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2025-03-25 |
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r-survminer
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public |
Contains the function 'ggsurvplot()' for drawing easily beautiful and 'ready-to-publish' survival curves with the 'number at risk' table and 'censoring count plot'. Other functions are also available to plot adjusted curves for `Cox` model and to visually examine 'Cox' model assumptions.
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2025-03-25 |
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r-survmisc
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A collection of functions to help in the analysis of right-censored survival data. These extend the methods available in package:survival.
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2025-03-25 |
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r-sur
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Access to the datasets and many of the functions used in "Statistics Using R: An Integrative Approach". These datasets include a subset of the National Education Longitudinal Study, the Framingham Heart Study, as well as several simulated datasets used in the examples throughout the textbook. The functions included in the package reproduce some of the functionality of 'Stata' that is not directly available in 'R'. The package also contains a tutorial on basic data frame management, including how to handle missing data.
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2025-03-25 |
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r-support.ces
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Provides basic functions that support an implementation of (discrete) choice experiments (CEs). CEs is a question-based survey method measuring people's preferences for goods/services and their characteristics. Refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831> for details on CEs, and Aizaki (2012) <doi:10.18637/jss.v050.c02> for the package.
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2025-03-25 |
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r-superheat
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A system for generating extendable and customizable heatmaps for exploring complex datasets, including big data and data with multiple data types.
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2025-03-25 |
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r-superlearner
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Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.
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2025-03-25 |
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r-sungeo
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public |
Tools for integrating spatially-misaligned GIS datasets. Part of the Sub-National Geospatial Data Archive System.
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2025-03-25 |
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r-suntools
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public |
Provides a set of convenient functions for calculating sun-related information, including the sun's position (elevation and azimuth), and the times of sunrise, sunset, solar noon, and twilight for any given geographical location on Earth. These calculations are based on equations provided by the National Oceanic & Atmospheric Administration (NOAA) <https://gml.noaa.gov/grad/solcalc/calcdetails.html> as described in "Astronomical Algorithms" by Jean Meeus (1991, ISBN: 978-0-943396-35-4). A resource for researchers and professionals working in fields such as climatology, biology, and renewable energy.
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2025-03-25 |
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r-summclust
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Module to compute cluster specific information for regression models with clustered errors, including leverage and influence statistics. Models of type 'lm' and 'fixest'(from the 'stats' and 'fixest' packages) are supported. 'summclust' implements similar features as the user-written 'summclust.ado' Stata module (MacKinnon, Nielsen & Webb, 2022; <arXiv:2205.03288v1>).
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2025-03-25 |
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r-sunburstr
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Make interactive 'd3.js' sequence sunburst diagrams in R with the convenience and infrastructure of an 'htmlwidget'.
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2025-03-25 |
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r-stylo
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public |
Supervised and unsupervised multivariate methods, supplemented by GUI and some visualizations, to perform various analyses in the field of computational stylistics, authorship attribution, etc. For further reference, see Eder et al. (2016), <https://journal.r-project.org/archive/2016/RJ-2016-007/index.html>. You are also encouraged to visit the Computational Stylistics Group's website <https://computationalstylistics.github.io/>, where a reasonable amount of information about the package and related projects are provided.
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2025-03-25 |
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r-styler
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public |
Pretty-prints R code without changing the user's formatting intent.
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2025-03-25 |
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r-streamr
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public |
Functions to access Twitter's filter, sample, and user streams, and to parse the output into data frames.
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2025-03-25 |
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r-str
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public |
Methods for decomposing seasonal data: STR (a Seasonal-Trend decomposition procedure based on Regression) and Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. They allow for multiple seasonal components, multiple linear covariates with constant, flexible and seasonal influence. Seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. The methods provide confidence intervals for the estimated components. The methods can be used for forecasting.
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2025-03-25 |
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r-str2str
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Offers a suite of functions for converting to and from (atomic) vectors, matrices, data.frames, and (3D+) arrays as well as lists of these objects. It is an alternative to the base R as.<str>.<method>() functions (e.g., as.data.frame.array()) that provides more useful and/or flexible restructuring of R objects. To do so, it only works with common structuring of R objects (e.g., data.frames with only atomic vector columns).
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2025-03-25 |
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r-stplanr
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Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. Create geographic "desire lines" from origin-destination (OD) data (building on the 'od' package); calculate routes on the transport network locally and via interfaces to routing services such as <https://cyclestreets.net/>; calculate route segment attributes such as bearing. The package implements the 'travel flow aggregration' method described in Morgan and Lovelace (2020) <doi:10.1177/2399808320942779>. Further information on the package's aim and scope can be found in the vignettes and in a paper in the R Journal (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053>.
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2025-03-25 |
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r-stmomo
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Implementation of the family of generalised age-period-cohort stochastic mortality models. This family of models encompasses many models proposed in the actuarial and demographic literature including the Lee-Carter (1992) <doi:10.2307/2290201> and the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models. It includes functions for fitting mortality models, analysing their goodness-of-fit and performing mortality projections and simulations.
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2025-03-25 |
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r-statsr
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Data and functions to support Bayesian and frequentist inference and decision making for the Coursera Specialization "Statistics with R". See <https://github.com/StatsWithR/statsr> for more information.
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2025-03-25 |
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r-statsexpressions
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Utilities for producing dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for 'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.
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2025-03-25 |
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r-stats19
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Tools to help download, process and analyse the UK road collision data collected using the 'STATS19' form. The data are provided as 'CSV' files with detailed road safety data about the circumstances of car crashes and other incidents on the roads resulting in casualties in Great Britain from 1979, the types (including make and model) of vehicles involved and the consequential casualties. The statistics relate only to personal casualties on public roads that are reported to the police, and subsequently recorded, using the 'STATS19' accident reporting form. See the Department for Transport website <https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data> for more information on these data.
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2025-03-25 |
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r-statmatch
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Integration of two data sources referred to the same target population which share a number of variables. Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing with data from complex sample surveys are available too.
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2025-03-25 |
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r-statnet
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Statnet is a collection of packages for statistical network analysis that are designed to work together because they share common data representations and 'API' design. They provide an integrated set of tools for the representation, visualization, analysis, and simulation of many different forms of network data. This package is designed to make it easy to install and load the key 'statnet' packages in a single step. Learn more about 'statnet' at <http://www.statnet.org>. Tutorials for many packages can be found at <https://github.com/statnet/Workshops/wiki>. For an introduction to functions in this package, type help(package='statnet').
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2025-03-25 |
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r-states
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Create panel data consisting of independent states from 1816 to the present. The package includes the Gleditsch & Ward (G&W) and Correlates of War (COW) lists of independent states, as well as helper functions for working with state panel data and standardizing other data sources to create country-year/month/etc. data.
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2025-03-25 |
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r-statebins
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The 'cartogram' heatmaps generated by the included methods are an alternative to choropleth maps for the United States and are based on work by the Washington Post graphics department in their report on "The states most threatened by trade" (<http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>). "State bins" preserve as much of the geographic placement of the states as possible but have the look and feel of a traditional heatmap. Functions are provided that allow for use of a binned, discrete scale, a continuous scale or manually specified colors depending on what is needed for the underlying data.
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2025-03-25 |
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r-stat
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An interactive document on the topic of basic statistical analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at <https://jarvisatharva.shinyapps.io/StatisticsPrimer/>.
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2025-03-25 |
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r-statar
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A set of tools inspired by 'Stata' to explore data.frames ('summarize', 'tabulate', 'xtile', 'pctile', 'binscatter', elapsed quarters/month, lead/lag).
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2025-03-25 |
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r-stampp
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Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects allowing integration with other packages such adegenet. Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. <doi:10.1111/1755-0998.12129> for the appropriate citation and user manual. Thank you in advance.
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2025-03-25 |
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r-stacks
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Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. 'stacks' implements a grammar for 'tidymodels'-aligned model stacking.
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2025-03-25 |
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r-stablelearner
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Graphical and computational methods that can be used to assess the stability of results from supervised statistical learning.
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2025-03-25 |
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r-srvyr
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Use piping, verbs like 'group_by' and 'summarize', and other 'dplyr' inspired syntactic style when calculating summary statistics on survey data using functions from the 'survey' package.
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2025-03-25 |
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r-st
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Implements the "shrinkage t" statistic introduced in Opgen-Rhein and Strimmer (2007) <DOI:10.2202/1544-6115.1252> and a shrinkage estimate of the "correlation-adjusted t-score" (CAT score) described in Zuber and Strimmer (2009) <DOI:10.1093/bioinformatics/btp460>. It also offers a convenient interface to a number of other regularized t-statistics commonly employed in high-dimensional case-control studies.
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2025-03-25 |
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r-sqldf
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The sqldf() function is typically passed a single argument which is an SQL select statement where the table names are ordinary R data frame names. sqldf() transparently sets up a database, imports the data frames into that database, performs the SQL select or other statement and returns the result using a heuristic to determine which class to assign to each column of the returned data frame. The sqldf() or read.csv.sql() functions can also be used to read filtered files into R even if the original files are larger than R itself can handle. 'RSQLite', 'RH2', 'RMySQL' and 'RPostgreSQL' backends are supported.
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2025-03-25 |
