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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-writer 0.1.0
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://github.com/talegari/writer
Licenses: LGPL 3+
Build system: r
Synopsis: Write from Multiple Sources to a Database Table
Description:

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.

r-winputall 1.0.1
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://cran.r-project.org/package=winputall
Licenses: GPL 3+
Build system: r
Synopsis: Variable Input Allocation Among Crops
Description:

Using a time-varying random parameters model developed in Koutchade et al., (2024) <https://hal.science/hal-04318163>, this package allows allocating variable input costs among crops produced by farmers based on panel data including information on input expenditure aggregated at the farm level and acreage shares. It also considers in fairly way the weighting data and can allow integrating time-varying and time-constant control variables.

r-whep 0.3.0
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://eduaguilera.github.io/whep/
Licenses: Expat
Build system: r
Synopsis: Processing Agro-Environmental Data
Description:

This package provides a set of tools for processing and analyzing data developed in the context of the "Who Has Eaten the Planet" (WHEP) project, funded by the European Research Council (ERC). For more details on multi-regional inputâ output model "Food and Agriculture Biomass Inputâ Output" (FABIO) see Bruckner et al. (2019) <doi:10.1021/acs.est.9b03554>.

r-wto 2.1
Propagated dependencies: r-visnetwork@2.1.4 r-som@0.3-5.2 r-rfast@2.1.5.2 r-reshape2@1.4.5 r-plyr@1.8.9 r-magrittr@2.0.4 r-igraph@2.2.1 r-hiclimr@2.2.1 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://cran.r-project.org/package=wTO
Licenses: GPL 2
Build system: r
Synopsis: Computing Weighted Topological Overlaps (wTO) & Consensus wTO Network
Description:

Computes the Weighted Topological Overlap with positive and negative signs (wTO) networks given a data frame containing the mRNA count/ expression/ abundance per sample, and a vector containing the interested nodes of interaction (a subset of the elements of the full data frame). It also computes the cut-off threshold or p-value based on the individuals bootstrap or the values reshuffle per individual. It also allows the construction of a consensus network, based on multiple wTO networks. The package includes a visualization tool for the networks. More about the methodology can be found at <doi:10.1186/s12859-018-2351-7>.

r-whisper 0.1.0
Propagated dependencies: r-torch@0.16.3 r-safetensors@0.2.0 r-jsonlite@2.0.0 r-hfhub@0.1.1 r-av@0.9.6
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://github.com/cornball-ai/whisper
Licenses: Expat
Build system: r
Synopsis: Native R 'torch' Implementation of 'OpenAI' 'Whisper'
Description:

Speech-to-text transcription using a native R torch implementation of OpenAI Whisper model <https://github.com/openai/whisper>. Supports multiple model sizes from tiny (39M parameters) to large-v3 (1.5B parameters) with integrated download from HuggingFace <https://huggingface.co/> via the hfhub package. Provides automatic speech recognition with optional language detection and translation to English. Audio preprocessing, mel spectrogram computation, and transformer-based encoder-decoder inference are all implemented in R using the torch package.

r-wrangle 0.6.4
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://cran.r-project.org/package=wrangle
Licenses: GPL 3
Build system: r
Synopsis: Systematic Data Wrangling Idiom
Description:

Supports systematic scrutiny, modification, and integration of data. The function status() counts rows that have missing values in grouping columns (returned by na() ), have non-unique combinations of grouping columns (returned by dup() ), and that are not locally sorted (returned by unsorted() ). Functions enumerate() and itemize() give sorted unique combinations of columns, with or without occurrence counts, respectively. Function ignore() drops columns in x that are present in y, and informative() drops columns in x that are entirely NA; constant() returns values that are constant, given a key. Data that have defined unique combinations of grouping values behave more predictably during merge operations.

r-wwntests 1.1.0
Propagated dependencies: r-sde@2.0.21 r-rainbow@3.8 r-mass@7.3-65 r-ftsa@6.6 r-fda@6.3.0
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://cran.r-project.org/package=wwntests
Licenses: GPL 3
Build system: r
Synopsis: Hypothesis Tests for Functional Time Series
Description:

This package provides a collection of white noise hypothesis tests for functional time series and related visualizations. These include tests based on the norms of autocovariance operators that are built under both strong and weak white noise assumptions. Additionally, tests based on the spectral density operator and on principal component dimensional reduction are included, which are built under strong white noise assumptions. Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models. These methods are described in Kokoszka et al. (2017) <doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019) <doi:10.1016/j.ecosta.2019.01.003>, Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>, and Kim et al. (2023) <doi: 10.1214/23-SS143> respectively.

r-wgteff 0.1.2
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://cran.r-project.org/package=WgtEff
Licenses: GPL 2+
Build system: r
Synopsis: Functions for Weighting Effects
Description:

This package provides functions for determining the effect of data weights on the variance of survey data: users will load a data set which has a weights column, and the package will calculate the design effect (DEFF), weighting loss, root design effect (DEFT), effective sample size (ESS), and/or weighted margin of error.

r-wyz-code-metatesting 1.1.22
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://neonira.github.io/offensiveProgrammingBook_v1.2.2/
Licenses: GPL 3
Build system: r
Synopsis: Wizardry Code Meta Testing
Description:

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.

r-waveletsvr 0.1.0
Propagated dependencies: r-wavelets@0.3-0.2 r-tsutils@0.9.4 r-fracdiff@1.5-3 r-forecast@8.24.0 r-e1071@1.7-16
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://cran.r-project.org/package=WaveletSVR
Licenses: GPL 3
Build system: r
Synopsis: Wavelet-SVR Hybrid Model for Time Series Forecasting
Description:

The main aim of this package is to combine the advantage of wavelet and support vector machine models for time series forecasting. This package also gives the accuracy measurements in terms of RMSE and MAPE. This package fits the hybrid Wavelet SVR model for time series forecasting The main aim of this package is to combine the advantage of wavelet and Support Vector Regression (SVR) models for time series forecasting. This package also gives the accuracy measurements in terms of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Raimundo and Okamoto (2018) <DOI: 10.1109/INFOCT.2018.8356851>.

r-waveletml 0.1.0
Propagated dependencies: r-wavelets@0.3-0.2 r-tseries@0.10-58 r-pso@1.0.4 r-neuralnet@1.44.2 r-lsts@2.1 r-forecast@8.24.0 r-fints@0.4-9 r-fgarch@4052.93 r-earth@5.3.4 r-e1071@1.7-16 r-caret@7.0-1 r-atsa@3.1.2.1
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://cran.r-project.org/package=WaveletML
Licenses: GPL 3
Build system: r
Synopsis: Wavelet Decomposition Based Hybrid Machine Learning Models
Description:

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>.

r-wpp2012 2.2-1
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: http://esa.un.org/wpp
Licenses: GPL 2+
Build system: r
Synopsis: World Population Prospects 2012
Description:

Data from the United Nation's World Population Prospects 2012.

r-windex 2.1.0
Propagated dependencies: r-scatterplot3d@0.3-44 r-phytools@2.5-2 r-phangorn@2.12.1 r-geiger@2.0.11 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://cran.r-project.org/package=windex
Licenses: GPL 2
Build system: r
Synopsis: Analysing Convergent Evolution using the Wheatsheaf Index
Description:

Analysing convergent evolution using the Wheatsheaf index, described in Arbuckle et al. (2014) <doi: 10.1111/2041-210X.12195>, and some other unrelated but perhaps useful functions.

r-wildcard 1.1.0
Propagated dependencies: r-stringi@1.8.7 r-magrittr@2.0.4
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://github.com/wlandau/wildcard
Licenses: GPL 3+
Build system: r
Synopsis: Templates for Data Frames
Description:

Generate data frames from templates.

r-wavesampling 0.1.4
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://github.com/RJauslin/WaveSampling
Licenses: GPL 2+
Build system: r
Synopsis: Weakly Associated Vectors (WAVE) Sampling
Description:

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>.

r-walrus 1.0.5
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://github.com/jamovi/walrus
Licenses: GPL 3
Build system: r
Synopsis: Robust Statistical Methods
Description:

This package provides a toolbox of common robust statistical tests, including robust descriptives, robust t-tests, and robust ANOVA. It is also available as a module for jamovi (see <https://www.jamovi.org> for more information). Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on the scripts and work of Rand Wilcox. These analyses are described in depth in the book Introduction to Robust Estimation & Hypothesis Testing'.

r-wikiprofiler 0.1.6
Propagated dependencies: r-yulab-utils@0.2.1 r-rsvg@2.7.0 r-gson@0.1.0 r-ggplotify@0.1.3 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://yulab-smu.top/contribution-knowledge-mining/
Licenses: Artistic License 2.0
Build system: r
Synopsis: 'WikiPathway' Based Data Integration and Visualization
Description:

Queries online WikiPathway graphics and allows mapping user data (e.g., expression values) on the graph. The package designs a grammar of graphic syntax that using pipe operator to add graphic layer.

r-webr 0.1.5
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://github.com/cardiomoon/webr
Licenses: GPL 3
Build system: r
Synopsis: Data and Functions for Web-Based Analysis
Description:

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'.

r-woodburymatrix 0.0.4
Propagated dependencies: r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://github.com/mbertolacci/WoodburyMatrix
Licenses: Expat
Build system: r
Synopsis: Fast Matrix Operations via the Woodbury Matrix Identity
Description:

This package provides a hierarchy of classes and methods for manipulating matrices formed implicitly from the sums of the inverses of other matrices, a situation commonly encountered in spatial statistics and related fields. Enables easy use of the Woodbury matrix identity and the matrix determinant lemma to allow computation (e.g., solving linear systems) without having to form the actual matrix. More information on the underlying linear algebra can be found in Harville, D. A. (1997) <doi:10.1007/b98818>.

r-wflo 1.9
Propagated dependencies: r-terra@1.8-86 r-sf@1.0-23 r-progress@1.2.3 r-plotrix@3.8-13 r-emstreer@3.1.3
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://cran.r-project.org/package=wflo
Licenses: GPL 3
Build system: r
Synopsis: Data Set and Helper Functions for Wind Farm Layout Optimization Problems
Description:

This package provides a convenient data set, a set of helper functions, and a benchmark function for economically (profit) driven wind farm layout optimization. This enables researchers in the field of the NP-hard (non-deterministic polynomial-time hard) problem of wind farm layout optimization to focus on their optimization methodology contribution and also provides a realistic benchmark setting for comparability among contributions. See Croonenbroeck, Carsten & Hennecke, David (2020) <doi:10.1016/j.energy.2020.119244>.

r-waspr 1.0.1
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://github.com/joliencremers/waspr
Licenses: GPL 3
Build system: r
Synopsis: Wasserstein Barycenters of Subset Posteriors
Description:

This package provides functions to compute Wasserstein barycenters of subset posteriors using the swapping algorithm developed by Puccetti, Rüschendorf and Vanduffel (2020) <doi:10.1016/j.jmaa.2017.02.003>. The Wasserstein barycenter is a geometric approach for combining subset posteriors. It allows for parallel and distributed computation of the posterior in case of complex models and/or big datasets, thereby increasing computational speed tremendously.

r-wrappr 0.1.0
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://cran.r-project.org/package=wrappr
Licenses: Expat
Build system: r
Synopsis: Collection of Helper and Wrapper Functions
Description:

Helper functions to easily add functionality to functions. The package can assign functions to have an lazy evaluation allowing you to save and update the arguments before and after each function call. You can set a temporary working directory within functions and wrap console messages around other functions.

r-wildmeta 0.3.2
Propagated dependencies: r-sandwich@3.1-1 r-robumeta@2.1 r-metafor@4.8-0 r-clubsandwich@0.6.1
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://meghapsimatrix.github.io/wildmeta/index.html
Licenses: GPL 3
Build system: r
Synopsis: Cluster Wild Bootstrapping for Meta-Analysis
Description:

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>.

r-winfapreader 0.1-7
Propagated dependencies: r-lubridate@1.9.4
Channel: guix-cran
Location: guix-cran/packages/w.scm (guix-cran packages w)
Home page: https://ilapros.github.io/winfapReader/
Licenses: GPL 3
Build system: r
Synopsis: Interact with Peak Flow Data in the United Kingdom
Description:

Obtain information on peak flow data from the National River Flow Archive (NRFA) in the United Kingdom, either from the Peak Flow Dataset files <https://nrfa.ceh.ac.uk/data/peak-flow-dataset> once these have been downloaded to the user's computer or using the NRFA's API. These files are in a format suitable for direct use in the WINFAP software, hence the name of the package.

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