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r-tcpl
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A set of tools for processing and modeling high-throughput and high-content chemical screening data. The package was developed for the the chemical screening data generated by the US EPA ToxCast program, but can be used for diverse chemical screening efforts.
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2025-04-22 |
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r-tcplfit2
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Performs the basic concentration response curve fitting used in the 'tcpl' package. It is a substitute for the original tcplFit() function (and sub-functions) and allows a wider variety of concentration-response models. All of the models included in the 'BMDExpress' package are now part of this package, and the output includes a calculation of the bmd (Benchmark Dose or concentration) value.
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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r-sylly.en
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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r-svdialogs
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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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2025-04-22 |
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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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2025-04-22 |
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r-survminer
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
