This package provides a collection of color palettes that were extracted from various books on my sons(Wren) bookshelf. Also included are a number of functions and wrappers to utilize them, as well as to subset the palettes to desired number/specific colors.

This package provides data from the United Nation's World Population Prospects 2019.

Access and analyze the World Bank's International Debt Statistics (IDS) <https://www.worldbank.org/en/programs/debt-statistics/ids>. IDS provides creditor-debtor relationships between countries, regions, and institutions. wbids enables users to download, process and work with IDS series across multiple geographies, counterparts, and time periods.

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'.

This package provides a new inverse probability of selection weighted Cox model to deal with outcome-dependent sampling in survival analysis.

Create dense vector representation of words and documents using quanteda'. Implements Word2vec (Mikolov et al., 2013) <doi:10.48550/arXiv.1310.4546>, Doc2vec (Le & Mikolov, 2014) <doi:10.48550/arXiv.1405.4053> and Latent Semantic Analysis (Deerwester et al., 1990) <doi:10.1002/(SICI)1097-4571(199009)41:6%3C391::AID-ASI1%3E3.0.CO;2-9>.

This package performs 1, 2 and 3D real and complex-valued wavelet transforms, nondecimated transforms, wavelet packet transforms, nondecimated wavelet packet transforms, multiple wavelet transforms, complex-valued wavelet transforms, wavelet shrinkage for various kinds of data, locally stationary wavelet time series, nonstationary multiscale transfer function modeling, density estimation.

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'.

It shows the connections between selected clusters from the latest time point and the clusters from all the previous time points. The transition matrices between time point t and t+1 are obtained from Waddington-OT analysis <https://github.com/ScialdoneLab/WOTPLY>.

This package provides functions to calculate the Water Deficit Index (WDI) and the Evaporative Fraction (EF) using geospatial raster data such as fractional vegetation cover (FVC) and surface-air temperature difference (TS-TA). The package automates regression-based edge fitting and produces continuous spatial maps of surface moisture and evaporative dynamics.

The employment of the Wavelet decomposition technique proves to be highly advantageous in the modelling of noisy time series data. Wavelet decomposition technique using the "haar" algorithm has been incorporated to formulate a hybrid Wavelet KNN (K-Nearest Neighbour) model for time series forecasting, as proposed by Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.

Meta testing is the ability to test a function without having to provide its parameter values. Those values will be generated, based on semantic naming of parameters, as introduced by package wyz.code.offensiveProgramming'. Value generation logic can be completed with your own data types and generation schemes. This to meet your most specific requirements and to answer to a wide variety of usages, from general use case to very specific ones. While using meta testing, it becomes easier to generate stress test campaigns, non-regression test campaigns and robustness test campaigns, as generated tests can be saved and reused from session to session. Main benefits of using wyz.code.metaTesting is ability to discover valid and invalid function parameter combinations, ability to infer valid parameter values, and to provide smart summaries that allows you to focus on dysfunctional cases.

The web version WebGestalt <https://www.webgestalt.org> supports 12 organisms, 354 gene identifiers and 321,251 function categories. Users can upload the data and functional categories with their own gene identifiers. In addition to the Over-Representation Analysis, WebGestalt also supports Gene Set Enrichment Analysis and Network Topology Analysis. The user-friendly output report allows interactive and efficient exploration of enrichment results. The WebGestaltR package not only supports all above functions but also can be integrated into other pipeline or simultaneously analyze multiple gene lists.

This package provides functions for calculating the fetch (length of open water distance along given directions) and estimating wave energy from wind and wave monitoring data.

This package provides functions aiming to facilitate the analysis of the structure of animal acoustic signals in R'. warbleR makes use of the basic sound analysis tools from the packages tuneR and seewave', and offers new tools for exploring and quantifying acoustic signal structure. The package allows to organize and manipulate multiple sound files, create spectrograms of complete recordings or individual signals in different formats, run several measures of acoustic structure, and characterize different structural levels in acoustic signals (Araya-Salas et al 2016 <doi:10.1111/2041-210X.12624>).

It generates the roster of turn for an outlet which is flowing (water) 24X7 or 168 hours towards the area under command or agricutural area (to be irrigated). The area under command is differentially owned by different individual farmers. The Outlet runs for free of cost to irrigate the area under command 24X7. So, flow time of the outlet has to be divided based on an area owned by an individual farmer and the location of his land or farm. This roster is known as warabandi and its generation in agriculture practices is a very tedious task. Calculations of time in microseconds are more error-prone, especially whenever it is performed by hands. That division of flow time for an individual farmer can be calculated by warabandi'. However, it generates a full publishable report for an outlet and all the farmers who have farms subjected to be irrigated. It reduces error risk and makes a more reproducible roster. For more details about warabandi system you can found elsewhere in Bandaragoda DJ(1995) <https://publications.iwmi.org/pdf/H_17571i.pdf>.

This package provides a set of utility function to prevent the spread of utility scripts in W4M (Workflow4Metabolomics) tools, and centralize them in a single package. To note, some are meant to be replaced by the use of dedicated packages in the future, like the parse_args() function: it is here only to prepare the ground for more global changes in W4M scripts and tools. This package is used by part of the W4M Galaxy modules, some of them being available on the community-maintained GitHub repository for Metabolomics Galaxy tools <https://github.com/workflow4metabolomics/tools-metabolomics>. See Delporte et al (2025) <doi:10.1002/cpz1.70095> for more details.

Data analysis of proteomics experiments by mass spectrometry is supported by this collection of functions mostly dedicated to the analysis of (bottom-up) quantitative (XIC) data. Fasta-formatted proteomes (eg from UniProt Consortium <doi:10.1093/nar/gky1049>) can be read with automatic parsing and multiple annotation types (like species origin, abbreviated gene names, etc) extracted. Initial results from multiple software for protein (and peptide) quantitation can be imported (to a common format): MaxQuant (Tyanova et al 2016 <doi:10.1038/nprot.2016.136>), Dia-NN (Demichev et al 2020 <doi:10.1038/s41592-019-0638-x>), Fragpipe (da Veiga et al 2020 <doi:10.1038/s41592-020-0912-y>), ionbot (Degroeve et al 2021 <doi:10.1101/2021.07.02.450686>), MassChroq (Valot et al 2011 <doi:10.1002/pmic.201100120>), OpenMS (Strauss et al 2021 <doi:10.1038/nmeth.3959>), ProteomeDiscoverer (Orsburn 2021 <doi:10.3390/proteomes9010015>), Proline (Bouyssie et al 2020 <doi:10.1093/bioinformatics/btaa118>), AlphaPept (preprint Strauss et al <doi:10.1101/2021.07.23.453379>) and Wombat-P (Bouyssie et al 2023 <doi:10.1021/acs.jproteome.3c00636>. Meta-data provided by initial analysis software and/or in sdrf format can be integrated to the analysis. Quantitative proteomics measurements frequently contain multiple NA values, due to physical absence of given peptides in some samples, limitations in sensitivity or other reasons. Help is provided to inspect the data graphically to investigate the nature of NA-values via their respective replicate measurements and to help/confirm the choice of NA-replacement algorithms. Meta-data in sdrf-format (Perez-Riverol et al 2020 <doi:10.1021/acs.jproteome.0c00376>) or similar tabular formats can be imported and included. Missing values can be inspected and imputed based on the concept of NA-neighbours or other methods. Dedicated filtering and statistical testing using the framework of package limma <doi:10.18129/B9.bioc.limma> can be run, enhanced by multiple rounds of NA-replacements to provide robustness towards rare stochastic events. Multi-species samples, as frequently used in benchmark-tests (eg Navarro et al 2016 <doi:10.1038/nbt.3685>, Ramus et al 2016 <doi:10.1016/j.jprot.2015.11.011>), can be run with special options considering such sub-groups during normalization and testing. Subsequently, ROC curves (Hand and Till 2001 <doi:10.1023/A:1010920819831>) can be constructed to compare multiple analysis approaches. As detailed example the data-set from Ramus et al 2016 <doi:10.1016/j.jprot.2015.11.011>) quantified by MaxQuant, ProteomeDiscoverer, and Proline is provided with a detailed analysis of heterologous spike-in proteins.

High-level tools to attach gridded weather data from the NASA POWER Project to event-based datasets. The package plans efficient spatio-temporal API calls via the nasapower R package, caches downloaded segments locally, and joins weather variables back to the input table using exact or rolling joins. This package is not affiliated with or endorsed by NASA.

Wavelet decomposes a series into multiple sub series called detailed and smooth components which helps to capture volatility at multi resolution level by various models. Two hybrid Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression have been used) have been developed in combination with stochastic models, feature selection, and optimization algorithms for prediction of the data. The algorithms have been developed following Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.

This package provides a set of wrappers intended to check, read and download information from the Wikimedia sources. It is specifically created to work with names of celebrities, in which case their information and statistics can be downloaded. Additionally, it also builds links and snippets to use in combination with the function gallery() in netCoin package.

This package provides unified syntax to write data from lazy dplyr tbl or dplyr sql query or a dataframe to a database table with modes such as create, append, insert, update, upsert, patch, delete, overwrite, overwrite_schema.

This package provides a tool to fit and compare the wind turbine power curves with successful curve fitting techniques. Facilitates to examine and compare the performance of a user-defined power curve fitting techniques. Also, provide features to generate power curve discrete points from a graphical power curves. Data on the power curves of the wind turbine from major manufacturers are provided.

Collect multichannel marketing data from sources such as Google analytics, Facebook Ads, and many others using the Windsor.ai API <https://www.windsor.ai/api-fields/>.