Create, store, read and manage structured collections of datasets and other objects using a workspace', then bundle it into a compressed archive. Using open and interoperable formats makes it possible to exchange bundled data from R to other languages such as Python or Julia'. Multiple formats are supported Parquet', JSON', yaml', spatial data and raster data are supported.

This package provides a weather generator to simulate precipitation and temperature for regions with seasonality. Users input training data containing precipitation, temperature, and seasonality (up to 26 seasons). Including weather season as a training variable allows users to explore the effects of potential changes in season duration as well as average start- and end-time dates due to phenomena like climate change. Data for training should be a single time series but can originate from station data, basin averages, grid cells, etc. Bearup, L., Gangopadhyay, S., & Mikkelson, K. (2021). "Hydroclimate Analysis Lower Santa Cruz River Basin Study (Technical Memorandum No ENV-2020-056)." Bureau of Reclamation. Gangopadhyay, S., Bearup, L. A., Verdin, A., Pruitt, T., Halper, E., & Shamir, E. (2019, December 1). "A collaborative stochastic weather generator for climate impacts assessment in the Lower Santa Cruz River Basin, Arizona." Fall Meeting 2019, American Geophysical Union. <https://ui.adsabs.harvard.edu/abs/2019AGUFMGC41G1267G>.

Conducts a goodness-of-fit test for the Weibull distribution (referred to as the weibullness test) and furnishes parameter estimations for both the two-parameter and three-parameter Weibull distributions. Notably, the threshold parameter is derived through correlation from the Weibull plot. Additionally, this package conducts goodness-of-fit assessments for the exponential, Gumbel, and inverse Weibull distributions, accompanied by parameter estimations. For more details, see Park (2017) <doi:10.23055/ijietap.2017.24.4.2848>, Park (2018) <doi:10.1155/2018/6056975>, and Park (2023) <doi:10.3390/math11143156>. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528).

First, we provide functions to calculate the partial derivative of the first-passage time diffusion probability density function (PDF) and cumulative distribution function (CDF) with respect to the first-passage time t (only for PDF), the upper barrier a, the drift rate v, the relative starting point w, the non-decision time t0, the inter-trial variability of the drift rate sv, the inter-trial variability of the rel. starting point sw, and the inter-trial variability of the non-decision time st0. In addition the PDF and CDF themselves are also provided. Most calculations are done on the logarithmic scale to make it more stable. Since the PDF, CDF, and their derivatives are represented as infinite series, we give the user the option to control the approximation errors with the argument precision'. For the numerical integration we used the C library cubature by Johnson, S. G. (2005-2013) <https://github.com/stevengj/cubature>. Numerical integration is required whenever sv, sw, and/or st0 is not zero. Note that numerical integration reduces speed of the computation and the precision cannot be guaranteed anymore. Therefore, whenever numerical integration is used an estimate of the approximation error is provided in the output list. Note: The large number of contributors (ctb) is due to copying a lot of C/C++ code chunks from the GNU Scientific Library (GSL). Second, we provide methods to sample from the first-passage time distribution with or without user-defined truncation from above. The first method is a new adaptive rejection sampler building on the works of Gilks and Wild (1992; <doi:10.2307/2347565>) and Hartmann and Klauer (in press). The second method is a rejection sampler provided by Drugowitsch (2016; <doi:10.1038/srep20490>). The third method is an inverse transformation sampler. The fourth method is a "pseudo" adaptive rejection sampler that builds on the first method. For more details see the corresponding help files.

This package implements inferential and graphic procedures for the semiparametric proportional means regression of weighted composite endpoint of recurrent event and death (Mao and Lin, 2016, <doi:10.1093/biostatistics/kxv050>).

This package implements the whitening methods (ZCA, PCA, Cholesky, ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018) "Optimal whitening and decorrelation", <doi:10.1080/00031305.2016.1277159>, as well as the whitening approach to canonical correlation analysis allowing negative canonical correlations described in Jendoubi and Strimmer (2019) "A whitening approach to probabilistic canonical correlation analysis for omics data integration", <doi:10.1186/s12859-018-2572-9>. The package also offers functions to simulate random orthogonal matrices, compute (correlation) loadings and explained variation. It also contains four example data sets (extended UCI wine data, TCGA LUSC data, nutrimouse data, extended pitprops data).

R clients to the Web of Science and InCites <https://clarivate.com/products/data-integration/> APIs, which allow you to programmatically download publication and citation data indexed in the Web of Science and InCites databases.

Imports variables from ReaderBench (Dascalu et al., 2018)<doi:10.1007/978-3-319-66610-5_48>, Coh-Metrix (McNamara et al., 2014)<doi:10.1017/CBO9780511894664>, and/or GAMET (Crossley et al., 2019) <doi:10.17239/jowr-2019.11.02.01> output files; downloads predictive scoring models described in Mercer & Cannon (2022)<doi:10.31244/jero.2022.01.03> and Mercer et al.(2021)<doi:10.1177/0829573520987753>; and generates predicted writing quality and curriculum-based measurement (McMaster & Espin, 2007)<doi:10.1177/00224669070410020301> scores.

Lossless webp images are 26% smaller in size compared to PNG. Lossy webp images are 25-34% smaller in size compared to JPEG. This package reads and writes webp images into a 3 (rgb) or 4 (rgba) channel bitmap array using conventions from the jpeg and png packages.

Spatial data are generally auto-correlated, meaning that if two units selected are close to each other, then it is likely that they share the same properties. For this reason, when sampling in the population it is often needed that the sample is well spread over space. A new method to draw a sample from a population with spatial coordinates is proposed. This method is called wave (Weakly Associated Vectors) sampling. It uses the less correlated vector to a spatial weights matrix to update the inclusion probabilities vector into a sample. For more details see Raphaël Jauslin and Yves Tillé (2019) <doi:10.1007/s13253-020-00407-1>.

This package provides a weighted selection probability to locate rare variants associated with multiple phenotypes.

R binding for libfswatch', a file system monitoring library. Watch files, or directories recursively, for changes in the background. Log activity, or call an R function, upon every change event.

The Model Disability Survey (MDS) <https://www.who.int/activities/collection-of-data-on-disability> is a World Health Organization (WHO) general population survey instrument to assess the distribution of disability within a country or region, grounded in the International Classification of Functioning, Disability and Health <https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health>. This package provides fit-for-purpose functions for calculating and presenting the results from this survey, as used by the WHO. The package primarily provides functions for implementing Rasch Analysis (see Andrich (2011) <doi:10.1586/erp.11.59>) to calculate a metric scale for disability.

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'.

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>.

This package provides 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.

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.

This package provides 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, see <https://www.ncei.noaa.gov/products/land-based-station/integrated-surface-database>).

Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference (with filtering options), significance with simulation algorithms.

This package performs an analysis of time-to-event clinical trial data using various "win time" methods, including ewt', ewtr', rmt', ewtp', rewtp', ewtpr', rewtpr', max', wtr', rwtr', pwt', and rpwt'. These methods are used to calculate and compare treatment effects on ordered composite endpoints. The package handles event times, event indicators, and treatment arm indicators and supports calculations on observed and resampled data. Detailed explanations of each method and usage examples are provided in "Use of win time for ordered composite endpoints in clinical trials," by Troendle et al. (2024)<https://pubmed.ncbi.nlm.nih.gov/38417455/>. For more information, see the package documentation or the vignette titled "Introduction to wintime.".

Makes research involving EMDAT and related datasets easier. These Datasets are manually filled and have several formatting and compatibility issues. Weed aims to resolve these with its functions.

Toolkit to support and perform discrete event simulations with and without resource constraints in the context of health technology assessments (HTA). The package focuses on cost-effectiveness modelling and aims to be submission-ready to relevant HTA bodies in alignment with NICE TSD 15 <https://sheffield.ac.uk/nice-dsu/tsds/patient-level-simulation>. More details an examples can be found in the package website <https://jsanchezalv.github.io/WARDEN/>.

Implementation of Weighted Fast Greedy algorithm for community detection in networks with mixed types of attributes.

Interactive tools for generating random samples. Users select an .xlsx, .csv, or delimited .txt file with population data and are walked through selecting the sample type (Simple Random Sample or Stratified), the number of backups desired, and a "stratify_on" value (if desired). The sample size is determined using a normal approximation to the hypergeometric distribution based on Nicholson (1956) <doi:10.1214/aoms/1177728270>. An .xlsx file is created with the sample and key metadata for reference. It is menu-driven and lets users pick an output directory. See vignettes for a detailed walk-through.