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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-eemdelm 0.1.1
Propagated dependencies: r-rlibeemd@1.4.4 r-nnfor@0.9.9 r-forecast@8.24.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EEMDelm
Licenses: GPL 3
Build system: r
Synopsis: Ensemble Empirical Mode Decomposition and Its Variant Based ELM Model
Description:

Forecasting univariate time series with different decomposition based Extreme Learning Machine models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>, Parida M, Behera MK, Nayak N (2018). <doi:10.1109/ICSESP.2018.8376723>.

r-easyverification 0.4.5
Propagated dependencies: r-specsverification@0.5-3 r-rcpp@1.1.0 r-pbapply@1.7-4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://www.meteoswiss.admin.ch
Licenses: GPL 3
Build system: r
Synopsis: Ensemble Forecast Verification for Large Data Sets
Description:

Set of tools to simplify application of atomic forecast verification metrics for (comparative) verification of ensemble forecasts to large data sets. The forecast metrics are imported from the SpecsVerification package, and additional forecast metrics are provided with this package. Alternatively, new user-defined forecast scores can be implemented using the example scores provided and applied using the functionality of this package.

r-elja 1.0.0
Propagated dependencies: r-mass@7.3-65 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-devtools@2.4.6
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/EHMarwan/Elja
Licenses: GPL 3+
Build system: r
Synopsis: Linear, Logistic and Generalized Linear Models Regressions for the EnvWAS/EWAS Approach
Description:

Tool for Environment-Wide Association Studies (EnvWAS / EWAS) which are repeated analysis. It includes three functions. One function for linear regression, a second for logistic regression and a last one for generalized linear models.

r-eclrmc 1.0
Propagated dependencies: r-softimpute@1.4-3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ECLRMC
Licenses: GPL 2
Build system: r
Synopsis: Ensemble Correlation-Based Low-Rank Matrix Completion
Description:

Ensemble correlation-based low-rank matrix completion method (ECLRMC) is an extension to the LRMC based methods. Traditionally, the LRMC based methods give identical importance to the whole data which results in emphasizing on the commonality of the data and overlooking the subtle but crucial differences. This method aims to overcome the equality assumption problem that exists in the current LRMS based methods. Ensemble correlation-based low-rank matrix completion (ECLRMC) takes consideration of the specific characteristic of each sample and performs LRMC on the set of samples with a strong correlation. It uses an ensemble learning method to improve the imputation performance. Since each sample is analyzed independently this method can be parallelized by distributing imputation across many computation units or GPU platforms. This package provides three different methods (LRMC, CLRMC and ECLRMC) for data imputation. There is also an NRMS function for evaluating the result. Chen, Xiaobo, et al (2017) <doi:10.1016/j.knosys.2017.06.010>.

r-ergmargins 1.6.1
Propagated dependencies: r-sna@2.8 r-sampling@2.11 r-numderiv@2016.8-1.1 r-network@1.19.0 r-matrix@1.7-4 r-ergm@4.11.0 r-dplyr@1.1.4 r-btergm@1.11.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ergMargins
Licenses: GPL 2+
Build system: r
Synopsis: Process Analysis for Exponential Random Graph Models
Description:

Calculates marginal effects and conducts process analysis in exponential family random graph models (ERGM). Includes functions to conduct mediation and moderation analyses and to diagnose multicollinearity. URL: <https://github.com/sduxbury/ergMargins>. BugReports: <https://github.com/sduxbury/ergMargins/issues>. Duxbury, Scott W (2021) <doi:10.1177/0049124120986178>. Long, J. Scott, and Sarah Mustillo (2018) <doi:10.1177/0049124118799374>. Mize, Trenton D. (2019) <doi:10.15195/v6.a4>. Karlson, Kristian Bernt, Anders Holm, and Richard Breen (2012) <doi:10.1177/0081175012444861>. Duxbury, Scott W (2018) <doi:10.1177/0049124118782543>. Duxbury, Scott W, Jenna Wertsching (2023) <doi:10.1016/j.socnet.2023.02.003>. Huang, Peng, Carter Butts (2023) <doi:10.1016/j.socnet.2023.07.001>.

r-eufootball 0.0.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EUfootball
Licenses: GPL 3+
Build system: r
Synopsis: Football Match Data of European Leagues
Description:

This package contains match results from seven European men's football leagues, namely Premier League (England), Ligue 1 (France), Bundesliga (Germany), Serie A (Italy), Primera Division (Spain), Eredivisie (The Netherlands), Super Lig (Turkey). Includes Seasons 2010/2011 until 2019/2020 and a set of interesting covariates. Can be used all purposes.

r-ecocbo 1.0.0
Propagated dependencies: r-vegan@2.7-2 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-ssp@1.1.0 r-rlang@1.1.6 r-plotly@4.11.0 r-parallelly@1.45.1 r-parabar@1.4.2 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/arturoSP/ecocbo
Licenses: GPL 3+
Build system: r
Synopsis: Calculating Optimum Sampling Effort in Community Ecology
Description:

This package provides a system for calculating the optimal sampling effort, based on the ideas of "Ecological cost-benefit optimization" as developed by A. Underwood (1997, ISBN 0 521 55696 1). Data is obtained from simulated ecological communities with prep_data() which formats and arranges the initial data, and then the optimization follows the following procedure of four functions: (1) prep_data() takes the original dataset and creates simulated sets that can be used as a basis for estimating statistical power and type II error. (2) sim_beta() is used to estimate the statistical power for the different sampling efforts specified by the user. (3) sim_cbo() calculates then the optimal sampling effort, based on the statistical power and the sampling costs. Additionally, (4) scompvar() calculates the variation components necessary for (5) Underwood_cbo() to calculate the optimal combination of number of sites and samples depending on either an economic budget or on a desired statistical accuracy. Lastly, (6) plot_power() helps the user visualize the results of sim_beta().

r-era 0.5.0
Propagated dependencies: r-vctrs@0.6.5 r-rlang@1.1.6 r-pillar@1.11.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://era.joeroe.io
Licenses: Expat
Build system: r
Synopsis: Year-Based Time Scales
Description:

This package provides a consistent representation of year-based time scales as a numeric vector with an associated era'. There are built-in era definitions for many year numbering systems used in contemporary and historic calendars (e.g. Common Era, Islamic Hijri years); year-based time scales used in archaeology, astronomy, geology, and other palaeosciences (e.g. Before Present, SI-prefixed annus'); and support for arbitrary user-defined eras. Years can converted from any one era to another using a generalised transformation function. Methods are also provided for robust casting and coercion between years and other numeric types, type-stable arithmetic with years, and pretty-printing in tables.

r-expectreg 0.54
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-quadprog@1.5-8 r-mboost@2.9-11 r-matrix@1.7-4 r-fields@17.1 r-colorspace@2.1-2 r-bayesx@0.3-3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=expectreg
Licenses: GPL 2
Build system: r
Synopsis: Expectile and Quantile Regression
Description:

Expectile and quantile regression of models with nonlinear effects e.g. spatial, random, ridge using least asymmetric weighed squares / absolutes as well as boosting; also supplies expectiles for common distributions.

r-excluder 0.5.2
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-stringr@1.6.0 r-rlang@1.1.6 r-maps@3.4.3 r-magrittr@2.0.4 r-lubridate@1.9.4 r-janitor@2.2.1 r-ipaddress@1.0.3 r-dplyr@1.1.4 r-curl@7.0.0 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://docs.ropensci.org/excluder/
Licenses: GPL 3+
Build system: r
Synopsis: Checks for Exclusion Criteria in Online Data
Description:

Data that are collected through online sources such as Mechanical Turk may require excluding rows because of IP address duplication, geolocation, or completion duration. This package facilitates exclusion of these data for Qualtrics datasets.

r-evacluster 0.1.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=Evacluster
Licenses: LGPL 2.0+
Build system: r
Synopsis: Evaluation Clustering Methods for Disease Subtypes Diagnosis
Description:

This package contains a set of clustering methods and evaluation metrics to select the best number of the clusters based on clustering stability. Two references describe the methodology: Fahimeh Nezhadmoghadam, and Jose Tamez-Pena (2021)<doi:10.1016/j.compbiomed.2021.104753>, and Fahimeh Nezhadmoghadam, et al.(2021)<doi:10.2174/1567205018666210831145825>.

r-eatgads 1.2.0
Propagated dependencies: r-tibble@3.3.0 r-stringi@1.8.7 r-plyr@1.8.9 r-hms@1.1.4 r-haven@2.5.5 r-eattools@0.7.9 r-eatdb@0.5.0 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/beckerbenj/eatGADS
Licenses: GPL 2+
Build system: r
Synopsis: Data Management of Large Hierarchical Data
Description:

Import SPSS data, handle and change SPSS meta data, store and access large hierarchical data in SQLite data bases.

r-evolvability 2.0.1
Propagated dependencies: r-matrix@1.7-4 r-lme4@1.1-37 r-coda@0.19-4.1 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=evolvability
Licenses: GPL 2+
Build system: r
Synopsis: Calculation of Evolvability Parameters
Description:

This package provides tools for calculating evolvability parameters from estimated G-matrices as defined in Hansen and Houle (2008) <doi:10.1111/j.1420-9101.2008.01573.x> and fits phylogenetic comparative models that link the rate of evolution of a trait to the state of another evolving trait (see Hansen et al. 2021 Systematic Biology <doi:10.1093/sysbio/syab079>). The package was released with Bolstad et al. (2014) <doi:10.1098/rstb.2013.0255>, which contains some examples of use.

r-easycoda 0.40.2
Propagated dependencies: r-vegan@2.7-2 r-ellipse@0.5.0 r-ca@0.71.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/michaelgreenacre/CODAinPractice/
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Compositional Data Analysis in Practice
Description:

Univariate and multivariate methods for compositional data analysis, based on logratios. The package implements the approach in the book Compositional Data Analysis in Practice by Michael Greenacre (2018), where accent is given to simple pairwise logratios. Selection can be made of logratios that account for a maximum percentage of logratio variance. Various multivariate analyses of logratios are included in the package.

r-epinow2 1.8.0
Propagated dependencies: r-truncnorm@1.0-9 r-stanheaders@2.32.10 r-scales@1.4.0 r-runner@0.4.4 r-rstantools@2.5.0 r-rstan@2.32.7 r-rlang@1.1.6 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-r-utils@2.13.0 r-purrr@1.2.0 r-primarycensored@1.3.0 r-posterior@1.6.1 r-patchwork@1.3.2 r-lubridate@1.9.4 r-lifecycle@1.0.4 r-ggplot2@4.0.1 r-futile-logger@1.4.3 r-data-table@1.17.8 r-cli@3.6.5 r-checkmate@2.3.3 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://epiforecasts.io/EpiNow2/
Licenses: Expat
Build system: r
Synopsis: Estimate and Forecast Real-Time Infection Dynamics
Description:

Estimates the time-varying reproduction number, rate of spread, and doubling time using a renewal equation approach combined with Bayesian inference via Stan. Supports Gaussian process and random walk priors for modelling changes in transmission over time. Accounts for delays between infection and observation (incubation period, reporting delays), right-truncation in recent data, day-of-week effects, and observation overdispersion. Can estimate relationships between primary and secondary outcomes (e.g., cases to hospitalisations or deaths) and forecast both. Runs across multiple regions in parallel. Based on Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1> and Gostic et al. (2020) <doi:10.1101/2020.06.18.20134858>.

r-elmso 1.0.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: <https://journals.sagepub.com/doi/abs/10.1509/jmr.15.0307>
Licenses: GPL 3
Build system: r
Synopsis: Implementation of the Efficient Large-Scale Online Display Advertising Algorithm
Description:

An implementation of the algorithm described in "Efficient Large- Scale Internet Media Selection Optimization for Online Display Advertising" by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below for journal text/citation and <http://faculty.marshall.usc.edu/gareth-james/Research/ELMSO.pdf> for a full-text version of the paper). The algorithm here is designed to allocate budget across a set of online advertising opportunities using a coordinate-descent approach, but it can be used in any resource-allocation problem with a matrix of visitation (in the case of the paper, website page- views) and channels (in the paper, websites). The package contains allocation functions both in the presence of bidding, when allocation is dependent on channel-specific cost curves, and when advertising costs are fixed at each channel.

r-fec16 0.1.6
Propagated dependencies: r-vroom@1.6.6 r-usethis@3.2.1 r-readr@2.1.6 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/baumer-lab/fec16
Licenses: CC0
Build system: r
Synopsis: Data Package for the 2016 United States Federal Elections
Description:

Easily analyze relational data from the United States 2016 federal election cycle as reported by the Federal Election Commission. This package contains data about candidates, committees, and a variety of different financial expenditures. Data is from <https://www.fec.gov/data/browse-data/?tab=bulk-data>.

r-funwithnumbers 1.2
Propagated dependencies: r-rmpfr@1.1-2 r-gmp@0.7-5 r-bigbits@1.4
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=FunWithNumbers
Licenses: LGPL 3
Build system: r
Synopsis: Fun with Fractions and Number Sequences
Description:

This package provides a collection of toys to do things like generate Collatz and other interesting sequences, calculate a fraction which is a close approximation to some value (e.g., 22/7 or 355/113 for pi), and so on.

r-fchange 2.1.0
Propagated dependencies: r-vars@1.6-1 r-tidyr@1.3.1 r-tensora@0.36.2.1 r-scales@1.4.0 r-sandwich@3.1-1 r-rfast@2.1.5.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-rainbow@3.8 r-plotly@4.11.0 r-plot3d@1.4.2 r-mass@7.3-65 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-ftsa@6.6 r-fda@6.3.0 r-fastmatrix@0.6-6 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://jrvanderdoes.github.io/fChange/
Licenses: GPL 3+
Build system: r
Synopsis: Functional Change Point Detection and Analysis
Description:

Analyze functional data and its change points. Includes functionality to store and process data, summarize and validate assumptions, characterize and perform inference of change points, and provide visualizations. Data is stored as discretely collected observations without requiring the selection of basis functions. For more details see chapter 8 of Horvath and Rice (2024) <doi:10.1007/978-3-031-51609-2>. Additional papers are forthcoming. Focused works are also included in the documentation of corresponding functions.

r-funviewr 0.1.1
Propagated dependencies: r-visnetwork@2.1.4 r-magrittr@2.0.4 r-igraph@2.2.1 r-htmltools@0.5.8.1 r-codetools@0.2-20
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/deamonpog/funviewR
Licenses: GPL 3
Build system: r
Synopsis: Visualize Function Call Dependencies in R Source Code
Description:

This package provides tools to analyze R source code and detect function definitions and their internal dependencies across multiple files. Creates interactive network visualizations using visNetwork to display function call relationships, with detailed tooltips showing function arguments, return values, and documentation. Supports both individual files and directory-based analysis with automatic file detection. Useful for understanding code structure, identifying dependencies, and documenting R projects.

r-featurizer 0.2
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=featurizer
Licenses: GPL 2+
Build system: r
Synopsis: Some Helper Functions that Help Create Features from Data
Description:

This package provides a collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down.

r-forrel 1.8.1
Propagated dependencies: r-verbalisr@0.7.2 r-ribd@1.7.1 r-pedtools@2.10.0 r-pedprobr@1.0.1 r-pbapply@1.7-4 r-glue@1.8.0
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/magnusdv/forrel
Licenses: GPL 2+
Build system: r
Synopsis: Forensic Pedigree Analysis and Relatedness Inference
Description:

Forensic applications of pedigree analysis, including likelihood ratios for relationship testing, general relatedness inference, marker simulation, and power analysis. forrel is part of the pedsuite', a collection of packages for pedigree analysis, further described in the book Pedigree Analysis in R (Vigeland, 2021, ISBN:9780128244302). Several functions deal specifically with power analysis in missing person cases, implementing methods described in Vigeland et al. (2020) <doi:10.1016/j.fsigen.2020.102376>. Data import from the Familias software (Egeland et al. (2000) <doi:10.1016/S0379-0738(00)00147-X>) is supported through the pedFamilias package.

r-fuzzynumbers 0.4-7
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/gagolews/FuzzyNumbers/
Licenses: LGPL 3+
Build system: r
Synopsis: Tools to Deal with Fuzzy Numbers
Description:

S4 classes and methods to deal with fuzzy numbers. They allow for computing any arithmetic operations (e.g., by using the Zadeh extension principle), performing approximation of arbitrary fuzzy numbers by trapezoidal and piecewise linear ones, preparing plots for publications, computing possibility and necessity values for comparisons, etc.

r-fdaconcur 0.1.3
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-fdapace@0.6.0
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/functionaldata/tFDAconcur
Licenses: Modified BSD
Build system: r
Synopsis: Concurrent Regression and History Index Models for Functional Data
Description:

This package provides an implementation of concurrent or varying coefficient regression methods for functional data. The implementations are done for both dense and sparsely observed functional data. Pointwise confidence bands can be constructed for each case. Further, the influence of past predictor values are modeled by a smooth history index function, while the effects on the response are described by smooth varying coefficient functions, which are very useful in analyzing real data such as COVID data. References: Yao, F., Müller, H.G., Wang, J.L. (2005) <doi:10.1214/009053605000000660>. Sentürk, D., Müller, H.G. (2010) <doi:10.1198/jasa.2010.tm09228>.

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