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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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2024-01-16 |
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r-xllim
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Provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <DOI:10.1016/j.jmva.2017.09.009>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <arXiv:1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).
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2024-01-16 |
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r-xlink
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The expression of X-chromosome undergoes three possible biological processes: X-chromosome inactivation (XCI), escape of the X-chromosome inactivation (XCI-E),and skewed X-chromosome inactivation (XCI-S). To analyze the X-linked genetic association for phenotype such as continuous, binary, and time-to-event outcomes with the actual process unknown, we propose a unified approach of maximizing the likelihood or partial likelihood over all of the potential biological processes. The methods are described in Wei Xu, Meiling Hao (2017) <doi:10.1002/gepi.22097>. And also see Dongxiao Han, Meiling Hao, Lianqiang Qu, Wei Xu (2019) <doi:10.1177/0962280219859037>.
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2024-01-16 |
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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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2024-01-16 |
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r-ximple
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Provides a simple XML tree parser/generator. It includes functions to read XML files into R objects, get information out of and into nodes, and write R objects back to XML code. It's not as powerful as the 'XML' package and doesn't aim to be, but for simple XML handling it could be useful. It was originally developed for the R GUI and IDE 'RKWard' <https://rkward.kde.org>, to make plugin development easier.
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2024-01-16 |
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r-wvplots
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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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2024-01-16 |
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r-x13binary
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The US Census Bureau provides a seasonal adjustment program now called 'X-13ARIMA-SEATS' building on both earlier programs called X-11 and X-12 as well as the SEATS program by the Bank of Spain. The US Census Bureau offers both source and binary versions -- which this package integrates for use by other R packages.
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2024-01-16 |
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r-xaringan
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Create HTML5 slides with R Markdown and the JavaScript library 'remark.js' (<https://remarkjs.com>).
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2024-01-16 |
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r-x12
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The 'X13-ARIMA-SEATS' <https://www.census.gov/data/software/x13as.html> methodology and software is a widely used software and developed by the US Census Bureau. It can be accessed from 'R' with this package and 'X13-ARIMA-SEATS' binaries are provided by the 'R' package 'x13binary'.
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2024-01-16 |
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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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2024-01-16 |
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r-wrightmap
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A powerful yet simple graphical tool available in the field of psychometrics is the Wright Map (also known as item maps or item-person maps), which presents the location of both respondents and items on the same scale. Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The 'WrightMap' package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. 'TAM', 'mirt', 'eRm' or 'IRToys' in 'R', or 'Stata', 'Mplus', etc.), 'WrightMap' features special integration with 'ConQuest' to facilitate reading and plotting its output directly.The 'wrightMap' function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The 'CQmodel' function reads output files created using 'ConQuest' software and creates a set of data frames for easy data manipulation, bundled in a 'CQmodel' object. The 'wrightMap' function can take a 'CQmodel' object as input or it can be used to create Wright Maps directly from data frames of person and item parameters.
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2024-01-16 |
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r-wwgbook
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Book is "Linear Mixed Models: A Practical Guide Using Statistical Software" published in 2006 by Chapman Hall / CRC Press.
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2024-01-16 |
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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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2024-01-16 |
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r-writexls
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Cross-platform Perl based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX) files from one or more data frames. Each data frame will be written to a separate named worksheet in the Excel spreadsheet. The worksheet name will be the name of the data frame it contains or can be specified by the user.
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2024-01-16 |
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r-write.snns
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Function for writing a SNNS pattern file from a data.frame or matrix.
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2024-01-16 |
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r-wpp2019
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Provides data from the United Nation's World Population Prospects 2019.
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2024-01-16 |
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r-wpp2017
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Provides data from the United Nation's World Population Prospects 2017.
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2024-01-16 |
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r-wpp2015
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Provides data from the United Nation's World Population Prospects 2015.
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2024-01-16 |
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r-wrapr
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Tools for writing and debugging R code. Provides: '%.>%' dot-pipe (an 'S3' configurable pipe), unpack/to (R style multiple assignment/return), 'build_frame()'/'draw_frame()' ('data.frame' example tools), 'qc()' (quoting concatenate), ':=' (named map builder), 'let()' (converts non-standard evaluation interfaces to parametric standard evaluation interfaces, inspired by 'gtools::strmacro()' and 'base::bquote()'), and more.
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2024-01-16 |
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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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2024-01-16 |
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r-wpp2012
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Data from the United Nation's World Population Prospects 2012
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2024-01-16 |
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r-wpp2010
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Data from the United Nation's World Population Prospects 2010
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2024-01-16 |
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r-worldmet
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public |
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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2024-01-16 |
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r-wpp2008
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Data from the United Nation's World Population Prospects 2008
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2024-01-16 |
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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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2024-01-16 |
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r-wperm
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Supplies permutation-test alternatives to traditional hypothesis-test procedures such as two-sample tests for means, medians, and standard deviations; correlation tests; tests for homogeneity and independence; and more. Suitable for general audiences, including individual and group users, introductory statistics courses, and more advanced statistics courses that desire an introduction to permutation tests.
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2024-01-16 |
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r-worrms
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Client for World Register of Marine Species (<https://www.marinespecies.org/>). Includes functions for each of the API methods, including searching for names by name, date and common names, searching using external identifiers, fetching synonyms, as well as fetching taxonomic children and taxonomic classification.
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2024-01-16 |
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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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2024-01-16 |
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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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2024-01-16 |
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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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2024-01-16 |
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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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2024-01-16 |
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r-wordpools
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Collects several classical word pools used most often to provide lists of words in psychological studies of learning and memory. It provides a simple function, 'pickList' for selecting random samples of words within given ranges.
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2024-01-16 |
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r-wordcloud2
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A fast visualization tool for creating wordcloud by using 'wordcloud2.js'. 'wordcloud2.js' is a JavaScript library to create wordle presentation on 2D canvas or HTML <https://timdream.org/wordcloud2.js/>.
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2024-01-16 |
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r-wordnet
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An interface to WordNet using the Jawbone Java API to WordNet. WordNet (<https://wordnet.princeton.edu/>) is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations. Please note that WordNet(R) is a registered tradename. Princeton University makes WordNet available to research and commercial users free of charge provided the terms of their license (<https://wordnet.princeton.edu/license-and-commercial-use>) are followed, and proper reference is made to the project using an appropriate citation (<https://wordnet.princeton.edu/citing-wordnet>).
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2024-01-16 |
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r-word.alignment
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For a given Sentence-Aligned Parallel Corpus, it aligns words for each sentence pair. It considers one-to-many and symmetrization alignments. Moreover, it evaluates the quality of word alignment based on this package and some other software. It also builds an automatic dictionary of two languages based on given parallel corpus.
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2024-01-16 |
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r-wooldridge
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Students learning both econometrics and R may find the introduction to both challenging. The wooldridge data package aims to lighten the task by efficiently loading any data set found in the text with a single command. Data sets have been compressed to a fraction of their original size. Documentation files contain page numbers, the original source, time of publication, and notes from the author suggesting avenues for further analysis and research. If one needs an introduction to R model syntax, a vignette contains solutions to examples from chapters of the text. Data sets are from the 7th edition (Wooldridge 2020, ISBN-13 978-1-337-55886-0), and are backwards compatible with all previous versions of the text.
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2024-01-16 |
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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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2024-01-16 |
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r-woebinning
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Implements an automated binning of numeric variables and factors with respect to a dichotomous target variable. Two approaches are provided: An implementation of fine and coarse classing that merges granular classes and levels step by step. And a tree-like approach that iteratively segments the initial bins via binary splits. Both procedures merge, respectively split, bins based on similar weight of evidence (WOE) values and stop via an information value (IV) based criteria. The package can be used with single variables or an entire data frame. It provides flexible tools for exploring different binning solutions and for deploying them to (new) data.
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2024-01-16 |
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r-woe
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Shows the relationship between an independent and dependent variable through Weight of Evidence and Information Value.
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2024-01-16 |
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r-wnl
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This is a set of minimization tools (maximum likelihood estimation and least square fitting) to solve examples in the Johan Gabrielsson and Dan Weiner's book "Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications" 5th ed. (ISBN:9198299107). Examples include linear and nonlinear compartmental model, turn-over model, single or multiple dosing bolus/infusion/oral models, allometry, toxicokinetics, reversible metabolism, in-vitro/in-vivo extrapolation, enterohepatic circulation, metabolite modeling, Emax model, inhibitory model, tolerance model, oscillating response model, enantiomer interaction model, effect compartment model, drug-drug interaction model, receptor occupancy model, and rebound phenomena model.
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2024-01-16 |
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r-wnnsel
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New tools for the imputation of missing values in high-dimensional data are introduced using the non-parametric nearest neighbor methods. It includes weighted nearest neighbor imputation methods that use specific distances for selected variables. It includes an automatic procedure of cross validation and does not require prespecified values of the tuning parameters. It can be used to impute missing values in high-dimensional data when the sample size is smaller than the number of predictors. For more information see Faisal and Tutz (2017) <doi:10.1515/sagmb-2015-0098>.
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2024-01-16 |
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r-wmwssp
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Calculates the minimal sample size for the Wilcoxon-Mann-Whitney test that is needed for a given power and two sided type I error rate. The method works for metric data with and without ties, count data, ordered categorical data, and even dichotomous data. But data is needed for the reference group to generate synthetic data for the treatment group based on a relevant effect. For details, see Brunner, E., Bathke A. C. and Konietschke, F: Rank- and Pseudo-Rank Procedures in Factorial Designs - Using R and SAS, Springer Verlag, to appear.
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2024-01-16 |
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r-wktmo
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Converts weekly data to monthly data. Users can use three types of week formats: ISO week, epidemiology week (epi week) and calendar date.
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2024-01-16 |
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r-wkb
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Utility functions to convert between the 'Spatial' classes specified by the package 'sp', and the well-known binary '(WKB)' representation for geometry specified by the 'Open Geospatial Consortium'. Supports 'Spatial' objects of class 'SpatialPoints', 'SpatialPointsDataFrame', 'SpatialLines', 'SpatialLinesDataFrame', 'SpatialPolygons', and 'SpatialPolygonsDataFrame'. Supports 'WKB' geometry types 'Point', 'LineString', 'Polygon', 'MultiPoint', 'MultiLineString', and 'MultiPolygon'. Includes extensions to enable creation of maps with 'TIBCO Spotfire'.
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2024-01-16 |
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r-withr
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A set of functions to run code 'with' safely and temporarily modified global state. Many of these functions were originally a part of the 'devtools' package, this provides a simple package with limited dependencies to provide access to these functions.
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2024-01-16 |
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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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2024-01-16 |
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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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2024-01-16 |
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r-wisam
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In the course of a genome-wide association study, the situation often arises that some phenotypes are known with greater precision than others. It could be that some individuals are known to harbor more micro-environmental variance than others. In the case of inbred strains of model organisms, it could be the case that more organisms were observed from some strains than others, so the strains with more organisms have better-estimated means. Package 'wISAM' handles this situation by allowing for weighting of each observation according to residual variance. Specifically, the 'weight' parameter to the function conduct_scan() takes the precision of each observation (one over the variance).
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2024-01-16 |
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r-wildpoker
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Provides insight into how the best hand for a poker game changes based on the game dealt, players who stay in until the showdown and wildcards added to the base game. At this time the package does not support player tactics, so draw poker variants are not included.
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2024-01-16 |
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r-wildcard
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Generate data frames from templates.
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2024-01-16 |
