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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-cdft 1.2
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CDFt
Licenses: GPL 2+
Build system: r
Synopsis: Downscaling and Bias Correction via Non-Parametric CDF-Transform
Description:

Statistical downscaling and bias correction (model output statistics) method based on cumulative distribution functions (CDF) transformation. See Michelangeli, Vrac, Loukos (2009) Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophysical Research Letters, 36, L11708, <doi:10.1029/2009GL038401>. ; and Vrac, Drobinski, Merlo, Herrmann, Lavaysse, Li, Somot (2012) Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Nat. Hazards Earth Syst. Sci., 12, 2769-2784, www.nat-hazards-earth-syst-sci.net/12/2769/2012/, <doi:10.5194/nhess-12-2769-2012>.

r-crch 1.2-2
Propagated dependencies: r-scoringrules@1.1.3 r-sandwich@3.1-1 r-ordinal@2023.12-4.1 r-formula@1.2-5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://topmodels.R-Forge.R-project.org/crch/
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Censored Regression with Conditional Heteroscedasticity
Description:

Different approaches to censored or truncated regression with conditional heteroscedasticity are provided. First, continuous distributions can be used for the (right and/or left censored or truncated) response with separate linear predictors for the mean and variance. Second, cumulative link models for ordinal data (obtained by interval-censoring continuous data) can be employed for heteroscedastic extended logistic regression (HXLR). In the latter type of models, the intercepts depend on the thresholds that define the intervals. Infrastructure for working with censored or truncated normal, logistic, and Student-t distributions, i.e., d/p/q/r functions and distributions3 objects.

r-cseqtl 1.0.0
Propagated dependencies: r-smarter@1.0.1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-r-utils@2.13.0 r-multcomp@1.4-29 r-matrixeqtl@2.3 r-helpersmg@2026.2.28 r-ggplot2@4.0.1 r-genomicfeatures@1.62.0 r-emdbook@1.3.14 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CSeQTL
Licenses: GPL 3+
Build system: r
Synopsis: Cell Type-Specific Expression Quantitative Trail Loci Mapping
Description:

Perform bulk and cell type-specific expression quantitative trail loci mapping with our novel method (Little et al. (2023) <doi:10.1038/s41467-023-38795-w>).

r-cis-dglm 0.1.0
Propagated dependencies: r-rcolorbrewer@1.1-3 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-dglm@1.8.6
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CIS.DGLM
Licenses: GPL 2+
Build system: r
Synopsis: Covariates, Interaction, and Selection for DGLM
Description:

An implementation of double generalized linear model (DGLM) building with variable selection procedures and handling of interaction terms and other complex situations. We also provide a method of handling convergence issues within the dglm() function. The package offers a simulation function for generating simulated data for testing purposes and utilizes the forward stepwise variable selection procedure in model-building. It also provides a new custom bootstrap function for mean and standard deviation estimation and functions for building crossplots and squareplots from a data set.

r-ctmcmove 1.2.10
Propagated dependencies: r-sp@2.2-0 r-raster@3.6-32 r-matrix@1.7-4 r-gdistance@1.6.5 r-fda@6.3.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=ctmcmove
Licenses: GPL 2
Build system: r
Synopsis: Modeling Animal Movement with Continuous-Time Discrete-Space Markov Chains
Description:

Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework. As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.

r-calf 1.0.17
Propagated dependencies: r-ggplot2@4.0.1 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CALF
Licenses: GPL 2
Build system: r
Synopsis: Coarse Approximation Linear Function
Description:

This package contains greedy algorithms for coarse approximation linear functions.

r-caffsim 0.2.2
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-shiny@1.11.1 r-mgcv@1.9-4 r-markdown@2.0 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/asancpt/caffsim
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Simulation of Plasma Caffeine Concentrations by Using Population Pharmacokinetic Model
Description:

Simulate plasma caffeine concentrations using population pharmacokinetic model described in Lee, Kim, Perera, McLachlan and Bae (2015) <doi:10.1007/s00431-015-2581-x>.

r-clustvarlv 2.1.1
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-plyr@1.8.9 r-iterators@1.0.14 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=ClustVarLV
Licenses: GPL 3
Build system: r
Synopsis: Clustering of Variables Around Latent Variables
Description:

This package provides functions for the clustering of variables around Latent Variables, for 2-way or 3-way data. Each cluster of variables, which may be defined as a local or directional cluster, is associated with a latent variable. External variables measured on the same observations or/and additional information on the variables can be taken into account. A "noise" cluster or sparse latent variables can also be defined.

r-clinpk 0.13.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/InsightRX/clinPK
Licenses: Expat
Build system: r
Synopsis: Clinical Pharmacokinetics Toolkit
Description:

This package provides equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function.

r-collett 0.1.2
Propagated dependencies: r-tinyplot@0.6.0 r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/mclements/collett
Licenses: Expat
Build system: r
Synopsis: Datasets from "Modelling Survival Data in Medical Research" by Collett
Description:

Datasets for the book entitled "Modelling Survival Data in Medical Research" by Collett (2023) <doi:10.1201/9781003282525>. The datasets provide extensive examples of time-to-event data.

r-cookiecutter 0.1.0
Propagated dependencies: r-whisker@0.4.1 r-stringr@1.6.0 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-mime@0.13 r-jsonlite@2.0.0 r-fs@1.6.6
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/felixhenninger/cookiecutter/
Licenses: FSDG-compatible
Build system: r
Synopsis: Generate Project Files from a Template
Description:

Generate project files and directories following a pre-made template. You can specify variables to customize file names and content, and flexibly adapt the template to your needs. cookiecutter for R implements a subset of the excellent cookiecutter package for the Python programming language (<https://github.com/cookiecutter/>), and aims to be largely compatible with the original cookiecutter template format.

r-csdm 1.0.0
Propagated dependencies: 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=csdm
Licenses: GPL 3
Build system: r
Synopsis: Cross-Sectional Dependence Models
Description:

This package provides estimators and utilities for large panel-data models with cross-sectional dependence, including mean group (MG), common correlated effects (CCE) and dynamic CCE (DCCE) estimators, and cross-sectionally augmented ARDL (CS-ARDL) specifications, plus related inference and diagnostics.

r-comparegroups 4.10.2
Propagated dependencies: r-writexl@1.5.4 r-survival@3.8-3 r-rstatix@0.7.3 r-rmdformats@1.0.4 r-rmarkdown@2.30 r-pmcmrplus@1.9.12 r-officer@0.7.1 r-knitr@1.50 r-kableextra@1.4.0 r-hardyweinberg@1.7.9 r-flextable@0.9.10 r-chron@2.3-62
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://isubirana.github.io/compareGroups/index.html
Licenses: GPL 2+
Build system: r
Synopsis: Descriptive Analysis by Groups
Description:

Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.

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-compositionalml 1.0
Propagated dependencies: r-rfast2@0.1.5.6 r-rfast@2.1.5.2 r-ranger@0.17.0 r-foreach@1.5.2 r-e1071@1.7-16 r-doparallel@1.0.17 r-compositional@8.1 r-boruta@9.0.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CompositionalML
Licenses: GPL 2+
Build system: r
Synopsis: Machine Learning with Compositional Data
Description:

Machine learning algorithms for predictor variables that are compositional data and the response variable is either continuous or categorical. Specifically, the Boruta variable selection algorithm, random forest, support vector machines and projection pursuit regression are included. Relevant papers include: Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. <doi:10.48550/arXiv.1106.1451> and Alenazi, A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. <doi:10.1080/03610926.2021.2014890>.

r-costsensitive 0.1.2.10
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/david-cortes/costsensitive
Licenses: FreeBSD
Build system: r
Synopsis: Cost-Sensitive Multi-Class Classification
Description:

Reduction-based techniques for cost-sensitive multi-class classification, in which each observation has a different cost for classifying it into one class, and the goal is to predict the class with the minimum expected cost for each new observation. Implements Weighted All-Pairs (Beygelzimer, A., Langford, J., & Zadrozny, B., 2008, <doi:10.1007/978-0-387-79361-0_1>), Weighted One-Vs-Rest (Beygelzimer, A., Dani, V., Hayes, T., Langford, J., & Zadrozny, B., 2005, <https://dl.acm.org/citation.cfm?id=1102358>) and Regression One-Vs-Rest. Works with arbitrary classifiers taking observation weights, or with regressors. Also implements cost-proportionate rejection sampling for working with classifiers that don't accept observation weights.

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-ctv 0.9-7
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/cran-task-views/ctv/
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: CRAN Task Views
Description:

Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated packages (client-side tools), generating HTML pages and storing task view information in the repository (server-side tools).

r-collpcm 1.4
Propagated dependencies: r-network@1.19.0 r-latentnet@2.12.0 r-gtools@3.9.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=collpcm
Licenses: GPL 3
Build system: r
Synopsis: Collapsed Latent Position Cluster Model for Social Networks
Description:

Markov chain Monte Carlo based inference routines for collapsed latent position cluster models or social networks, which includes searches over the model space (number of clusters in the latent position cluster model). The label switching algorithm used is that of Nobile and Fearnside (2007) <doi:10.1007/s11222-006-9014-7> which relies on the algorithm of Carpaneto and Toth (1980) <doi:10.1145/355873.355883>.

r-ccmnet 1.0.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-rbest@1.8-2 r-network@1.19.0 r-mvtnorm@1.3-3 r-kableextra@1.4.0 r-intergraph@2.0-4 r-igraph@2.2.1 r-gtools@3.9.5 r-ggplot2@4.0.1 r-ergm@4.12.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CCMnet
Licenses: GPL 3
Build system: r
Synopsis: Congruence Class Models for Networks
Description:

This package provides an implementation of Congruence Class Models for generating networks. It facilitates sampling networks based on specific topological properties and attribute mixing patterns using a Markov Chain Monte Carlo framework. The implementation builds upon code from the ergm package; see Handcock et al. (2008) <doi:10.18637/jss.v024.i01>.

r-csppdata 0.2.61
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=csppData
Licenses: GPL 3
Build system: r
Synopsis: Data Only: The Correlates of State Policy Project Dataset
Description:

This package contains the Correlates of State Policy Project dataset (+ codebook) assembled by Marty P. Jordan and Matt Grossmann (2020) <http://ippsr.msu.edu/public-policy/correlates-state-policy> used by the cspp package. The Correlates data contains over 3000 variables across more than 100 years that pertain to state politics and policy in the United States.

r-causalhypergraph 0.1.0
Propagated dependencies: r-useful@1.2.6.1 r-stringr@1.6.0 r-diagrammersvg@0.1 r-diagrammer@1.0.11 r-cna@4.0.3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=causalHyperGraph
Licenses: GPL 2+
Build system: r
Synopsis: Drawing Causal Hypergraphs
Description:

Draws causal hypergraph plots from models output by configurational comparative methods such as Coincidence Analysis (CNA) or Qualitative Comparative Analysis (QCA).

r-cito 1.1
Propagated dependencies: r-torchvision@0.8.0 r-torch@0.16.3 r-tibble@3.3.0 r-progress@1.2.3 r-parabar@1.4.2 r-lme4@1.1-37 r-gridextra@2.3 r-coro@1.1.0 r-cli@3.6.5 r-checkmate@2.3.3 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://citoverse.github.io/cito/
Licenses: GPL 3+
Build system: r
Synopsis: Building and Training Neural Networks
Description:

The cito package provides a user-friendly interface for training and interpreting deep neural networks (DNN). cito simplifies the fitting of DNNs by supporting the familiar formula syntax, hyperparameter tuning under cross-validation, and helps to detect and handle convergence problems. DNNs can be trained on CPU, GPU and MacOS GPUs. In addition, cito has many downstream functionalities such as various explainable AI (xAI) metrics (e.g. variable importance, partial dependence plots, accumulated local effect plots, and effect estimates) to interpret trained DNNs. cito optionally provides confidence intervals (and p-values) for all xAI metrics and predictions. At the same time, cito is computationally efficient because it is based on the deep learning framework torch'. The torch package is native to R, so no Python installation or other API is required for this package.

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