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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-cleannlp 3.1.0
Dependencies: python@3.11.14
Propagated dependencies: r-udpipe@0.8.16 r-stringi@1.8.7 r-reticulate@1.44.1 r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://statsmaths.github.io/cleanNLP/
Licenses: LGPL 2.0
Build system: r
Synopsis: Tidy Data Model for Natural Language Processing
Description:

This package provides a set of fast tools for converting a textual corpus into a set of normalized tables. Users may make use of the udpipe back end with no external dependencies, or a Python back ends with spaCy <https://spacy.io>. Exposed annotation tasks include tokenization, part of speech tagging, named entity recognition, and dependency parsing.

r-cpmbigdata 0.0.2
Propagated dependencies: r-rms@8.1-0 r-iterators@1.0.14 r-hmisc@5.2-4 r-foreach@1.5.2 r-doparallel@1.0.17 r-benchmarkme@1.0.8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cpmBigData
Licenses: GPL 2+
Build system: r
Synopsis: Fitting Semiparametric Cumulative Probability Models for Big Data
Description:

This package provides a big data version for fitting cumulative probability models using the orm() function from the rms package. See Liu et al. (2017) <DOI:10.1002/sim.7433> for details.

r-catalog 0.1.1
Propagated dependencies: r-sparklyr@1.9.3 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://nathaneastwood.github.io/catalog/
Licenses: GPL 2+
Build system: r
Synopsis: Access the 'Spark Catalog' API via 'sparklyr'
Description:

Gain access to the Spark Catalog API making use of the sparklyr API. Catalog <https://spark.apache.org/docs/2.4.3/api/java/org/apache/spark/sql/catalog/Catalog.html> is the interface for managing a metastore (aka metadata catalog) of relational entities (e.g. database(s), tables, functions, table columns and temporary views).

r-clusevol 1.0.1
Propagated dependencies: r-viridis@0.6.5 r-plotly@4.11.0 r-ggplot2@4.0.1 r-fpc@2.2-13 r-dplyr@1.1.4 r-clustersim@0.51-6 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/vmoprojs/clusEvol
Licenses: GPL 3+
Build system: r
Synopsis: Procedure for Cluster Evolution Analytics
Description:

Cluster Evolution Analytics allows us to use exploratory what if questions in the sense that the present information of an object is plugged-in a dataset in a previous time frame so that we can explore its evolution (and of its neighbors) to the present. See the URL for the papers associated with this package, as for instance, Morales-Oñate and Morales-Oñate (2024) <doi:10.1016/j.softx.2024.101921>.

r-cthist 2.1.12
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-lubridate@1.9.4 r-jsonlite@2.0.0 r-httr@1.4.7 r-dplyr@1.1.4 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/bgcarlisle/cthist
Licenses: AGPL 3+
Build system: r
Synopsis: Clinical Trial Registry History
Description:

Retrieves historical versions of clinical trial registry entries from <https://ClinicalTrials.gov>. Package functionality and implementation for v 1.0.0 is documented in Carlisle (2022) <DOI:10.1371/journal.pone.0270909>.

r-causact 0.6.0
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-reticulate@1.44.1 r-purrr@1.2.0 r-magrittr@2.0.4 r-lifecycle@1.0.4 r-igraph@2.2.1 r-ggplot2@4.0.1 r-forcats@1.0.1 r-dplyr@1.1.4 r-diagrammer@1.0.11 r-cowplot@1.2.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/flyaflya/causact
Licenses: Expat
Build system: r
Synopsis: Fast, Easy, and Visual Bayesian Inference
Description:

Accelerate Bayesian analytics workflows in R through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the numpyro python package.

r-cbctools 0.7.1
Propagated dependencies: r-rlang@1.1.6 r-randtoolbox@2.0.5 r-logitr@1.1.3 r-idefix@1.1.0 r-ggplot2@4.0.1 r-fastdummies@1.7.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/jhelvy/cbcTools
Licenses: Expat
Build system: r
Synopsis: Design and Analyze Choice-Based Conjoint Experiments
Description:

Design and evaluate choice-based conjoint survey experiments. Generate a variety of survey designs, including random designs, frequency-based designs, and D-optimal designs, as well as "labeled" designs (also known as "alternative-specific designs"), designs with "no choice" options, and designs with dominant alternatives removed. Conveniently inspect and compare designs using a variety of metrics, including design balance, overlap, and D-error, and simulate choice data for a survey design either randomly or according to a utility model defined by user-provided prior parameters. Conduct a power analysis for a given survey design by estimating the same model on different subsets of the data to simulate different sample sizes. Bayesian D-efficient designs using the cea and modfed methods are obtained using the idefix package by Traets et al (2020) <doi:10.18637/jss.v096.i03>. Choice simulation and model estimation in power analyses are handled using the logitr package by Helveston (2023) <doi:10.18637/jss.v105.i10>.

r-cloudstor 0.2.0
Propagated dependencies: r-xml@3.99-0.20 r-rio@1.2.4 r-keyring@1.4.1 r-httr@1.4.7 r-getpass@0.2-4 r-curl@7.0.0 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://pdparker.github.io/cloudstoR/
Licenses: GPL 3+
Build system: r
Synopsis: Simplifies Access to Cloudstor API
Description:

Access Cloudstor via their WebDAV API. This package can read, write, and navigate Cloudstor from R.

r-covid19srilanka 1.1.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=covid19srilanka
Licenses: Expat
Build system: r
Synopsis: The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data in Sri Lanka
Description:

This package provides a daily counts of the Coronavirus (COVID19) cases by districts and country. Data source: Epidemiological Unit, Ministry of Health, Sri Lanka <https://www.epid.gov.lk/web/>.

r-cmars 0.1.4
Propagated dependencies: r-stringr@1.6.0 r-ryacas@1.1.6 r-rocr@1.0-11 r-rmosek@1.3.5 r-mpv@2.0 r-matrix@1.7-4 r-earth@5.3.4 r-auc@0.3.2
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cmaRs
Licenses: GPL 2+
Build system: r
Synopsis: Implementation of the Conic Multivariate Adaptive Regression Splines in R
Description:

An implementation of Conic Multivariate Adaptive Regression Splines (CMARS) in R. See Weber et al. (2011) CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization, <DOI:10.1080/17415977.2011.624770>. It constructs models by using the terms obtained from the forward step of MARS and then estimates parameters by using Tikhonov regularization and conic quadratic optimization. It is possible to construct models for prediction and binary classification. It provides performance measures for the model developed. The package needs the optimisation software MOSEK <https://www.mosek.com/> to construct the models. Please follow the instructions in Rmosek for the installation.

r-chopper 1.0
Propagated dependencies: r-scales@1.4.0 r-purrr@1.2.0 r-normalp@0.7.2.1 r-lubridate@1.9.4 r-imputets@3.4 r-ggplot2@4.0.1 r-generalizedhyperbolic@0.8-7 r-forecast@8.24.0 r-fgarch@4052.93 r-evd@2.3-7.1 r-changepoint@2.3 r-ald@1.3.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://rpubs.com/giancarlo_vercellino/chopper
Licenses: GPL 3
Build system: r
Synopsis: Changepoint-Aware Ensemble for Probabilistic Modeling
Description:

This package implements a changepoint-aware ensemble forecasting algorithm that combines Theta, TBATS (Trigonometric, Box-Cox transformation, ARMA errors, Trend, Seasonal components), and ARFIMA (AutoRegressive, Fractionally Integrated, Moving Average) using a product-of-experts approach for robust probabilistic prediction.

r-cmfrec 3.5.1-3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/david-cortes/cmfrec
Licenses: Expat
Build system: r
Synopsis: Collective Matrix Factorization for Recommender Systems
Description:

Collective matrix factorization (a.k.a. multi-view or multi-way factorization, Singh, Gordon, (2008) <doi:10.1145/1401890.1401969>) tries to approximate a (potentially very sparse or having many missing values) matrix X as the product of two low-dimensional matrices, optionally aided with secondary information matrices about rows and/or columns of X', which are also factorized using the same latent components. The intended usage is for recommender systems, dimensionality reduction, and missing value imputation. Implements extensions of the original model (Cortes, (2018) <arXiv:1809.00366>) and can produce different factorizations such as the weighted implicit-feedback model (Hu, Koren, Volinsky, (2008) <doi:10.1109/ICDM.2008.22>), the weighted-lambda-regularization model, (Zhou, Wilkinson, Schreiber, Pan, (2008) <doi:10.1007/978-3-540-68880-8_32>), or the enhanced model with implicit features (Rendle, Zhang, Koren, (2019) <arXiv:1905.01395>), with or without side information. Can use gradient-based procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009) <doi:10.1109/MC.2009.263>), with either a Cholesky solver, a faster conjugate gradient solver (Takacs, Pilaszy, Tikk, (2011) <doi:10.1145/2043932.2043987>), or a non-negative coordinate descent solver (Franc, Hlavac, Navara, (2005) <doi:10.1007/11556121_50>), providing efficient methods for sparse and dense data, and mixtures thereof. Supports L1 and L2 regularization in the main models, offers alternative most-popular and content-based models, and implements functionality for cold-start recommendations and imputation of 2D data.

r-condmvnorm 2025.1
Propagated dependencies: r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=condMVNorm
Licenses: GPL 2
Build system: r
Synopsis: Conditional Multivariate Normal Distribution
Description:

Computes conditional multivariate normal densities, probabilities, and random deviates.

r-cureplots 1.1.1
Propagated dependencies: r-glue@1.8.0 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/gbasulto/cureplots
Licenses: AGPL 3+
Build system: r
Synopsis: CURE (Cumulative Residual) Plots
Description:

This package creates ggplot2 Cumulative Residual (CURE) plots to check the goodness-of-fit of a count model; or the tables to create a customized version. A dataset of crashes in Washington state is available for illustrative purposes.

r-cnmap 0.1.2
Propagated dependencies: r-terra@1.8-86 r-sf@1.0-23
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/PanfengZhang/cnmap
Licenses: GPL 3
Build system: r
Synopsis: China Map Data from AutoNavi Map
Description:

According to the codes and names of county-level and above administrative divisions released in 2022 by the Ministry of Civil Affairs of the People's Republic of China, the online vector map files were retrieved from the website (available at: <http://datav.aliyun.com/portal/school/atlas/area_selector>). This study was supported by the National Natural Science Foundation of China (NSFC, Grant No. 42205177).

r-coneproj 1.23
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=coneproj
Licenses: GPL 2+
Build system: r
Synopsis: Primal or Dual Cone Projections with Routines for Constrained Regression
Description:

Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013)<doi:10.1080/03610918.2012.659820> for more details.

r-climind 0.1-3
Propagated dependencies: r-weathermetrics@1.2.2 r-spei@1.8.1 r-chron@2.3-62
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://gitlab.com/indecis-eu/indecis
Licenses: GPL 3+
Build system: r
Synopsis: Climate Indices
Description:

Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5).

r-climclass 2.1.1
Propagated dependencies: r-reshape2@1.4.5 r-ggplot2@4.0.1 r-geosphere@1.5-20
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=ClimClass
Licenses: GPL 3+
Build system: r
Synopsis: Climate Classification According to Several Indices
Description:

Classification of climate according to Koeppen - Geiger, of aridity indices, of continentality indices, of water balance after Thornthwaite, of viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy, Bagnouls-Gaussen.

r-chapensk 0.4
Propagated dependencies: r-bessel@0.7-0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/langenbergstefan/chapensk
Licenses: GPL 3
Build system: r
Synopsis: Estimation of Gas Properties from the Lennard-Jones Potential
Description:

Estimation of gas transport properties (viscosity, diffusion, thermal conductivity) using Chapman-Enskok theory (Chapman and Larmor 1918, <doi:10.1098/rsta.1918.0005>) and of the second virial coefficient (Vargas et al. 2001, <doi:10.1016/s0378-4371(00)00362-9>) using the Lennard-Jones (12-6) potential. Up to the third order correction is taken into account for viscosity and thermal conductivity. It is also possible to calculate the binary diffusion coefficients of polar and non-polar gases in non-polar bath gases (Brown et al. 2011, <doi:10.1016/j.pecs.2010.12.001>). 16 collision integrals are calculated with four digit accuracy over the reduced temperature range [0.3, 400] using an interpolation function of Kim and Monroe (2014, <doi:10.1016/j.jcp.2014.05.018>).

r-carms 1.0.1
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-diagram@1.6.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: http://www.openreliability.org
Licenses: GPL 3+
Build system: r
Synopsis: Continuous Time Markov Rate Modeling for Reliability Analysis
Description:

Emulation of an application originally created by Paul Pukite. Computer Aided Rate Modeling and Simulation. Jan Pukite and Paul Pukite, (1998, ISBN 978-0-7803-3482), William J. Stewart, (1994, ISBN: 0-691-03699-3).

r-cbanalysis 0.2.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cbanalysis
Licenses: GPL 2
Build system: r
Synopsis: Coffee Break Descriptive Analysis
Description:

This package provides a set of functions that helps you to generate descriptive statistics based on the variable types.

r-corona 0.3.0
Propagated dependencies: r-reshape2@1.4.5 r-qicharts2@0.8.1 r-plyr@1.8.9 r-gridextra@2.3 r-ggplot2@4.0.1 r-gganimate@1.0.11
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=corona
Licenses: GPL 3
Build system: r
Synopsis: Coronavirus ('Rona') Data Exploration
Description:

Manipulate and view coronavirus data and other societally relevant data at a basic level.

r-connect 0.7.27
Propagated dependencies: r-qgraph@1.9.8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=ConNEcT
Licenses: GPL 2+
Build system: r
Synopsis: Contingency Measure-Based Networks for Binary Time Series
Description:

The ConNEcT approach investigates the pairwise association strength of binary time series by calculating contingency measures and depicts the results in a network. The package includes features to explore and visualize the data. To calculate the pairwise concurrent or temporal sequenced relationship between the variables, the package provides seven contingency measures (proportion of agreement, classical & corrected Jaccard, Cohen's kappa, phi correlation coefficient, odds ratio, and log odds ratio), however, others can easily be implemented. The package also includes non-parametric significance tests, that can be applied to test whether the contingency value quantifying the relationship between the variables is significantly higher than chance level. Most importantly this test accounts for auto-dependence and relative frequency.See Bodner et al.(2021) <doi: 10.1111/bmsp.12222>.Finally, a network can be drawn. Variables depicted the nodes of the network, with the node size adapted to the prevalence. The association strength between the variables defines the undirected (concurrent) or directed (temporal sequenced) links between the nodes. The results of the non-parametric significance test can be included by depicting either all links or only the significant ones. Tutorial see Bodner et al.(2021) <doi:10.3758/s13428-021-01760-w>.

r-copulasfm 0.2.0
Propagated dependencies: r-vinecopula@2.6.1 r-truncnorm@1.0-9 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=copulaSFM
Licenses: GPL 3
Build system: r
Synopsis: Copula-Based Stochastic Frontier Models
Description:

This package provides estimation procedures for copula-based stochastic frontier models for cross-sectional data. The package implements maximum likelihood estimation of stochastic frontier models allowing flexible dependence structures between inefficiency and noise terms through various copula families (e.g., Gaussian and Student-t). It enables estimation of technical efficiency scores, log-likelihood values, and information criteria (AIC and BIC). The implemented framework builds upon stochastic frontier analysis introduced by Aigner, Lovell and Schmidt (1977) <doi:10.1016/0304-4076(77)90052-5> and the copula theory described in Joe (2014, ISBN:9781466583221). Empirical applications of copula-based stochastic frontier models can be found in Wiboonpongse et al. (2015) <doi:10.1016/j.ijar.2015.06.001> and Maneejuk et al. (2017, ISBN:9783319562176).

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