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r-rcmdrplugin.survival
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public |
An R Commander plug-in for the survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves, and testing for differences in survival curves, along with data-management facilities and a variety of tests, diagnostics and graphs.
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2025-03-25 |
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r-ncmeta
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Extract metadata from 'NetCDF' data sources, these can be files, file handles or servers. This package leverages and extends the lower level functions of the 'RNetCDF' package providing a consistent set of functions that all return data frames. We introduce named concepts of 'grid', 'axis' and 'source' which are all meaningful entities without formal definition in the 'NetCDF' library <https://www.unidata.ucar.edu/software/netcdf/>. 'RNetCDF' matches the library itself with only the named concepts of 'variables', 'dimensions' and 'attributes'.
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2025-03-25 |
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r-ztable
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public |
Makes zebra-striped tables (tables with alternating row colors) in LaTeX and HTML formats easily from a data.frame, matrix, lm, aov, anova, glm, coxph, nls, fitdistr, mytable and cbind.mytable objects.
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2025-03-25 |
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r-zyp
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An efficient implementation of the slope method described by Sen (1968) <doi:10.1080/01621459.1968.10480934> plus implementation of prewhitening approaches to determining trends in climate data described by Zhang, Vincent, Hogg, and Niitsoo (2000) <doi:10.1080/07055900.2000.9649654> and Yue, Pilon, Phinney, and Cavadias (2002) <doi:10.1002/hyp.1095>.
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2025-03-25 |
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r-zipcoder
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public |
Make working with ZIP codes in R painless with an integrated dataset of U.S. ZIP codes and functions for working with them. Search ZIP codes by multiple geographies, including state, county, city & across time zones. Also included are functions for relating ZIP codes to Census data, geocoding & distance calculations.
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2025-03-25 |
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r-zcompositions
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Principled methods for the imputation of zeros, left-censored and missing data in compositional data sets (Palarea-Albaladejo and Martin-Fernandez (2015) <doi:10.1016/j.chemolab.2015.02.019>).
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2025-03-25 |
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r-yhat
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The purpose of this package is to provide methods to interpret multiple linear regression and canonical correlation results including beta weights,structure coefficients, validity coefficients, product measures, relative weights, all-possible-subsets regression, dominance analysis, commonality analysis, and adjusted effect sizes.
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2025-03-25 |
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r-yarrr
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Contains a mixture of functions and data sets referred to in the introductory e-book "YaRrr!: The Pirate's Guide to R". The latest version of the e-book is available for free at <https://www.thepiratesguidetor.com>.
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2025-03-25 |
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r-yfr
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Facilitates download of financial data from Yahoo Finance <https://finance.yahoo.com/>, a vast repository of stock price data across multiple financial exchanges. The package offers a local caching system and support for parallel computation.
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2025-03-25 |
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r-xrf
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An implementation of the RuleFit algorithm as described in Friedman & Popescu (2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient Boosting ('XGBoost') is used to build rules, and 'glmnet' is used to fit a sparse linear model on the raw and rule features. The result is a model that learns similarly to a tree ensemble, while often offering improved interpretability and achieving improved scoring runtime in live applications. Several algorithms for reducing rule complexity are provided, most notably hyperrectangle de-overlapping. All algorithms scale to several million rows and support sparse representations to handle tens of thousands of dimensions.
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2025-03-25 |
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r-xls
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Given the date column as an ascending entry, future errors are included in the sum of squares of error that should be minimized based on the number of steps and weights you determine. Thus, it is prevented that the variables affect each other's coefficients unrealistically.
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2025-03-25 |
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r-xlconnect
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public |
Provides comprehensive functionality to read, write and format Excel data.
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2025-03-25 |
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r-xaringanthemer
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Create beautifully color-coordinated and customized themes for your 'xaringan' slides, without writing any CSS. Complete your slide theme with 'ggplot2' themes that match the font and colors used in your slides. Customized styles can be created directly in your slides' 'R Markdown' source file or in a separate external script.
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2025-03-25 |
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r-xefun
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public |
Miscellaneous functions used for x-engineering (feature engineering) or for supporting in other packages maintained by 'Shichen Xie'.
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2025-03-25 |
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r-xgxr
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Supports a structured approach for exploring PKPD data <https://opensource.nibr.com/xgx/>. It also contains helper functions for enabling the modeler to follow best R practices (by appending the program name, figure name location, and draft status to each plot). In addition, it enables the modeler to follow best graphical practices (by providing a theme that reduces chart ink, and by providing time-scale, log-scale, and reverse-log-transform-scale functions for more readable axes). Finally, it provides some data checking and summarizing functions for rapidly exploring pharmacokinetics and pharmacodynamics (PKPD) datasets.
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2025-03-25 |
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r-wvplots
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public |
Select data analysis plots, under a standardized calling interface implemented on top of 'ggplot2' and 'plotly'. Plots of interest include: 'ROC', gain curve, scatter plot with marginal distributions, conditioned scatter plot with marginal densities, box and stem with matching theoretical distribution, and density with matching theoretical distribution.
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2025-03-25 |
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r-wrgraph
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Additional options for making graphics in the context of analyzing high-throughput data are available here. This includes automatic segmenting of the current device (eg window) to accommodate multiple new plots, automatic checking for optimal location of legends in plots, small histograms to insert as legends, histograms re-transforming axis labels to linear when plotting log2-transformed data, a violin-plot <doi:10.1080/00031305.1998.10480559> function for a wide variety of input-formats, principal components analysis (PCA) <doi:10.1080/14786440109462720> with bag-plots <doi:10.1080/00031305.1999.10474494> to highlight and compare the center areas for groups of samples, generic MA-plots (differential- versus average-value plots) <doi:10.1093/nar/30.4.e15>, staggered count plots and generation of mouse-over interactive html pages.
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2025-03-25 |
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r-wrmisc
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The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). Other functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Many times large experimental datasets need some additional filtering, adequate functions are provided. Batch reading (or writing) of sets of files and combining data to arrays is supported, too. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too.
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2025-03-25 |
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r-worldmet
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Functions to import data from more than 30,000 surface meteorological sites around the world managed by the National Oceanic and Atmospheric Administration (NOAA) Integrated Surface Database (ISD).
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2025-03-25 |
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r-worldfootballr
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Allow users to obtain clean and tidy football (soccer) game, team and player data. Data is collected from a number of popular sites, including 'FBref', transfer and valuations data from 'Transfermarkt'<https://www.transfermarkt.com/> and shooting location and other match stats data from 'Understat'<https://understat.com/> and 'fotmob'<https://www.fotmob.com/>. It gives users the ability to access data more efficiently, rather than having to export data tables to files before being able to complete their analysis.
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2025-03-25 |
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r-worldflora
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World Flora Online is an online flora of all known plants, available from <http://www.worldfloraonline.org/>. Methods are provided of matching a list of plant names (scientific names, taxonomic names, botanical names) against a static copy of the World Flora Online Taxonomic Backbone data that can be downloaded from the World Flora Online website. The World Flora Online Taxonomic Backbone is an updated version of The Plant List (<http://www.theplantlist.org/>), a working list of plant names that has become static since 2013.
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2025-03-25 |
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r-workflowsets
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A workflow is a combination of a model and preprocessors (e.g, a formula, recipe, etc.) (Kuhn and Silge (2021) <https://www.tmwr.org/>). In order to try different combinations of these, an object can be created that contains many workflows. There are functions to create workflows en masse as well as training them and visualizing the results.
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2025-03-25 |
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r-workflows
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Managing both a 'parsnip' model and a preprocessor, such as a model formula or recipe from 'recipes', can often be challenging. The goal of 'workflows' is to streamline this process by bundling the model alongside the preprocessor, all within the same object.
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2025-03-25 |
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r-workflowr
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Provides a workflow for your analysis projects by combining literate programming ('knitr' and 'rmarkdown') and version control ('Git', via 'git2r') to generate a website containing time-stamped, versioned, and documented results.
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2025-03-25 |
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r-worcs
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Create reproducible and transparent research projects in 'R'. This package is based on the Workflow for Open Reproducible Code in Science (WORCS), a step-by-step procedure based on best practices for Open Science. It includes an 'RStudio' project template, several convenience functions, and all dependencies required to make your project reproducible and transparent. WORCS is explained in the tutorial paper by Van Lissa, Brandmaier, Brinkman, Lamprecht, Struiksma, & Vreede (2021). <doi:10.3233/DS-210031>.
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2025-03-25 |
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r-wildmeta
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Conducts single coefficient tests and multiple-contrast hypothesis tests of meta-regression models using cluster wild bootstrapping, based on methods examined in Joshi, Pustejovsky, and Beretvas (2022) <DOI:10.1002/jrsm.1554>.
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2025-03-25 |
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r-whitebox
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An R frontend for the 'WhiteboxTools' library, which is an advanced geospatial data analysis platform developed by Prof. John Lindsay at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. 'WhiteboxTools' can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. 'WhiteboxTools' also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. Suggested citation: Lindsay (2016) <doi:10.1016/j.cageo.2016.07.003>.
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2025-03-25 |
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r-widyr
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Encapsulates the pattern of untidying data into a wide matrix, performing some processing, then turning it back into a tidy form. This is useful for several operations such as co-occurrence counts, correlations, or clustering that are mathematically convenient on wide matrices.
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2025-03-25 |
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r-wemix
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Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels. Random effects are estimated using the PIRLS algorithm from 'lme4pureR' (Walker and Bates (2013) <https://github.com/lme4/lme4pureR>).
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2025-03-25 |
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r-weightr
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Estimates the Vevea and Hedges (1995) weight-function model. By specifying arguments, users can also estimate the modified model described in Vevea and Woods (2005), which may be more practical with small datasets. Users can also specify moderators to estimate a linear model. The package functionality allows users to easily extract the results of these analyses as R objects for other uses. In addition, the package includes a function to launch both models as a Shiny application. Although the Shiny application is also available online, this function allows users to launch it locally if they choose.
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2025-03-25 |
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r-weightit
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Generates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality of several R packages and providing in-house estimation methods. Available methods include propensity score weighting using generalized linear models, gradient boosting machines, the covariate balancing propensity score algorithm, Bayesian additive regression trees, and SuperLearner, and directly estimating balancing weights using entropy balancing, energy balancing, and optimization-based weights. Also allows for assessment of weights and checking of covariate balance by interfacing directly with the 'cobalt' package. See the vignette "Installing Supporting Packages" for instructions on how to install any package 'WeightIt' uses, including those that may not be on CRAN.
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2025-03-25 |
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r-webshot2
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public |
Takes screenshots of web pages, including Shiny applications and R Markdown documents. 'webshot2' uses headless Chrome or Chromium as the browser back-end.
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2025-03-25 |
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r-webr
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Several analysis-related functions for the book entitled "Web-based Analysis without R in Your Computer"(written in Korean, ISBN 978-89-5566-185-9) by Keon-Woong Moon. The main function plot.htest() shows the distribution of statistic for the object of class 'htest'.
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2025-03-25 |
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r-webpower
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This is a collection of tools for conducting both basic and advanced statistical power analysis including correlation, proportion, t-test, one-way ANOVA, two-way ANOVA, linear regression, logistic regression, Poisson regression, mediation analysis, longitudinal data analysis, structural equation modeling and multilevel modeling. It also serves as the engine for conducting power analysis online at <https://webpower.psychstat.org>.
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2025-03-25 |
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r-webexercises
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Functions for easily creating interactive web pages using 'R Markdown' that students can use in self-guided learning.
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2025-03-25 |
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r-webmockr
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public |
Stubbing and setting expectations on 'HTTP' requests. Includes tools for stubbing 'HTTP' requests, including expected request conditions and response conditions. Match on 'HTTP' method, query parameters, request body, headers and more. Can be used for unit tests or outside of a testing context.
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2025-03-25 |
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r-webdriver
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A client for the 'WebDriver' 'API'. It allows driving a (probably headless) web browser, and can be used to test web applications, including 'Shiny' apps. In theory it works with any 'WebDriver' implementation, but it was only tested with 'PhantomJS'.
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2025-03-25 |
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r-wdpar
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Fetch and clean data from the World Database on Protected Areas (WDPA) and the World Database on Other Effective Area-Based Conservation Measures (WDOECM). Data is obtained from Protected Planet <https://www.protectedplanet.net/en>. To augment data cleaning procedures, users can install the 'prepr' R package (available at <https://github.com/dickoa/prepr>). For more information on this package, see Hanson (2022) <doi:10.21105/joss.04594>.
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2025-03-25 |
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r-wdman
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There are a number of binary files associated with the 'Webdriver'/'Selenium' project. This package provides functions to download these binaries and to manage processes involving them.
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2025-03-25 |
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r-waterfalls
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A not uncommon task for quants is to create 'waterfall charts'. There seems to be no simple way to do this in 'ggplot2' currently. This package contains a single function (waterfall) that simply draws a waterfall chart in a 'ggplot2' object. Some flexibility is provided, though often the object created will need to be modified through a theme.
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2025-03-25 |
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r-wakefield
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public |
Generates random data sets including: data.frames, lists, and vectors.
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2025-03-25 |
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r-wbstats
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Search and download data from the World Bank Data API.
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2025-03-25 |
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r-waffle
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public |
Square pie charts (a.k.a. waffle charts) can be used to communicate parts of a whole for categorical quantities. To emulate the percentage view of a pie chart, a 10x10 grid should be used with each square representing 1% of the total. Modern uses of waffle charts do not necessarily adhere to this rule and can be created with a grid of any rectangular shape. Best practices suggest keeping the number of categories small, just as should be done when creating pie charts. Tools are provided to create waffle charts as well as stitch them together, and to use glyphs for making isotype pictograms.
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2025-03-25 |
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r-vtreat
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A 'data.frame' processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. 'vtreat' prepares variables so that data has fewer exceptional cases, making it easier to safely use models in production. Common problems 'vtreat' defends against: 'Inf', 'NA', too many categorical levels, rare categorical levels, and new categorical levels (levels seen during application, but not during training). Reference: "'vtreat': a data.frame Processor for Predictive Modeling", Zumel, Mount, 2016, <DOI:10.5281/zenodo.1173313>.
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2025-03-25 |
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r-vtable
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public |
Automatically generates HTML variable documentation including variable names, labels, classes, value labels (if applicable), value ranges, and summary statistics. See the vignette "vtable" for a package overview.
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2025-03-25 |
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r-vpc
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public |
Visual predictive checks are a commonly used diagnostic plot in pharmacometrics, showing how certain statistics (percentiles) for observed data compare to those same statistics for data simulated from a model. The package can generate VPCs for continuous, categorical, censored, and (repeated) time-to-event data.
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2025-03-25 |
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r-vitae
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Provides templates and functions to simplify the production and maintenance of curriculum vitae.
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2025-03-25 |
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r-vistime
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A library for creating time based charts, like Gantt or timelines. Possible outputs include 'ggplot2' diagrams, 'plotly.js' graphs, 'Highcharts.js' widgets and data.frames. Results can be used in the 'RStudio' viewer pane, in 'RMarkdown' documents or in Shiny apps. In the interactive outputs created by vistime() and hc_vistime(), you can interact with the plot using mouse hover or zoom.
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2025-03-25 |
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r-visdat
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Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'.
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2025-03-25 |
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r-vip
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public |
A general framework for constructing variable importance plots from various types of machine learning models in R. Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include 1) an efficient permutation-based variable importance measure, 2) variable importance based on Shapley values (Strumbelj and Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based approach described in Greenwell et al. (2018) <arXiv:1805.04755>. A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).
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2025-03-25 |
