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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-cdatanet 2.2.2
Propagated dependencies: r-rcppprogress@0.4.2 r-rcppnumerical@0.6-0 r-rcppeigen@0.3.4.0.2 r-rcppdist@0.1.1.1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrixcalc@1.0-6 r-matrix@1.7-4 r-formula-tools@1.7.1 r-formula@1.2-5 r-foreach@1.5.2 r-dorng@1.8.6.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/ahoundetoungan/CDatanet
Licenses: GPL 3
Build system: r
Synopsis: Econometrics of Network Data
Description:

Simulating and estimating peer effect models and network formation models. The class of peer effect models includes linear-in-means models (Lee, 2004; <doi:10.1111/j.1468-0262.2004.00558.x>), Tobit models (Xu and Lee, 2015; <doi:10.1016/j.jeconom.2015.05.004>), and discrete numerical data models (Houndetoungan, 2025; <doi:10.48550/arXiv.2405.17290>). The network formation models include pair-wise regressions with degree heterogeneity (Graham, 2017; <doi:10.3982/ECTA12679>) and exponential random graph models (Mele, 2017; <doi:10.3982/ECTA10400>).

r-coastlinefd 1.1.2
Propagated dependencies: r-writexl@1.5.4 r-tidyr@1.3.1 r-sfheaders@0.4.5 r-sf@1.0-23 r-readxl@1.4.5 r-progress@1.2.3 r-ggplot2@4.0.1 r-fields@17.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/redworld123/CoastlineFD
Licenses: Expat
Build system: r
Synopsis: Calculation of the Fractal Dimension of a Coastline
Description:

Calculating the fractal dimension of a coastline using the boxes and dividers methods.

r-cmprskcoxmsm 0.2.1
Propagated dependencies: r-twang@2.6.2 r-survival@3.8-3 r-sandwich@3.1-1 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cmprskcoxmsm
Licenses: GPL 2+
Build system: r
Synopsis: Use IPW to Estimate Treatment Effect under Competing Risks
Description:

Uses inverse probability weighting methods to estimate treatment effect under marginal structure model for the cause-specific hazard of competing risk events. Estimates also the cumulative incidence function (i.e. risk) of the potential outcomes, and provides inference on risk difference and risk ratio. Reference: Kalbfleisch & Prentice (2002)<doi:10.1002/9781118032985>; Hernan et al (2001)<doi:10.1198/016214501753168154>.

r-cragg 0.0.1
Propagated dependencies: r-expm@1.0-0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cragg
Licenses: GPL 3+
Build system: r
Synopsis: Tests for Weak Instruments in R
Description:

This package implements Cragg-Donald (1993) <doi:10.1017/S0266466600007519> and Stock and Yogo (2005) <doi:10.1017/CBO9780511614491.006> tests for weak instruments in R.

r-chessgmoog 0.1.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=ChessGmooG
Licenses: GPL 2+
Build system: r
Synopsis: FIDE Chess Players Ratings for 2015 and 2020
Description:

Datasets of the International Chess Federation's player ratings and country information analysed in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics".

r-cdata 1.2.1
Propagated dependencies: r-wrapr@2.1.0 r-rquery@1.4.99 r-rqdatatable@1.3.3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/WinVector/cdata/
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Fluid Data Transformations
Description:

Supplies higher-order coordinatized data specification and fluid transform operators that include pivot and anti-pivot as special cases. The methodology is describe in Zumel', 2018, "Fluid data reshaping with cdata'", <https://winvector.github.io/FluidData/FluidDataReshapingWithCdata.html> , <DOI:10.5281/zenodo.1173299> . This package introduces the idea of explicit control table specification of data transforms. Works on in-memory data or on remote data using rquery and SQL database interfaces.

r-cspstandsegmentation 0.1.2
Propagated dependencies: r-terra@1.8-86 r-sf@1.0-23 r-rgl@1.3.31 r-rcsf@1.0.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-magrittr@2.0.4 r-lidr@4.2.3 r-igraph@2.2.1 r-foreach@1.5.2 r-doparallel@1.0.17 r-dbscan@1.2.3 r-data-table@1.17.8 r-conicfit@1.0.4 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/JulFrey/CspStandSegmentation
Licenses: GPL 3
Build system: r
Synopsis: Comparative Shortest Path Forest Stand Segmentation from LiDAR Data
Description:

Functionality for segmenting individual trees from a forest stand scanned with a close-range (e.g., terrestrial or mobile) laser scanner. The complete workflow from a raw point cloud to a complete tabular forest inventory is provided. The package contains several algorithms for detecting tree bases and a graph-based algorithm to attach all remaining points to these tree bases. It builds heavily on the lidR package. A description of the segmentation algorithm can be found in Larysch et al. (2025) <doi:10.1007/s10342-025-01796-z>.

r-csa 0.7.1
Propagated dependencies: r-scales@1.4.0 r-reshape2@1.4.5 r-raster@3.6-32 r-moments@0.14.1 r-lmoments@1.3-2 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-foreach@1.5.2 r-doparallel@1.0.17 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/imarkonis/csa
Licenses: GPL 2
Build system: r
Synopsis: Cross-Scale Analysis Tool for Model-Observation Visualization and Integration
Description:

Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatio-temporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. csa package offers a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatio-temporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatio-temporal continuum.

r-cat2cat 0.4.7
Propagated dependencies: r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/Polkas/cat2cat
Licenses: GPL 2+ FSDG-compatible
Build system: r
Synopsis: Handling an Inconsistently Coded Categorical Variable in a Longitudinal Dataset
Description:

Unifying an inconsistently coded categorical variable between two different time points in accordance with a mapping table. The main rule is to replicate the observation if it could be assigned to a few categories. Then using frequencies or statistical methods to approximate the probabilities of being assigned to each of them. This procedure was invented and implemented in the paper by Nasinski, Majchrowska, and Broniatowska (2020) <doi:10.24425/cejeme.2020.134747>.

r-crank 1.1-2
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=crank
Licenses: GPL 2+
Build system: r
Synopsis: Completing Ranks
Description:

This package provides functions for completing and recalculating rankings and sorting.

r-cransearcher 1.0.0
Propagated dependencies: r-stringr@1.6.0 r-shinyjs@2.1.0 r-shiny@1.11.1 r-miniui@0.1.2 r-lubridate@1.9.4 r-dt@0.34.0 r-dplyr@1.1.4 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/RhoInc/CRANsearcher
Licenses: Expat
Build system: r
Synopsis: RStudio Addin for Searching Packages in CRAN Database Based on Keywords
Description:

One of the strengths of R is its vast package ecosystem. Indeed, R packages extend from visualization to Bayesian inference and from spatial analyses to pharmacokinetics (<https://cran.r-project.org/web/views/>). There is probably not an area of quantitative research that isn't represented by at least one R package. At the time of this writing, there are more than 10,000 active CRAN packages. Because of this massive ecosystem, it is important to have tools to search and learn about packages related to your personal R needs. For this reason, we developed an RStudio addin capable of searching available CRAN packages directly within RStudio.

r-calsim 0.5.4
Propagated dependencies: r-spatstat-geom@3.6-1 r-spatstat@3.4-1 r-exactmultinom@0.1.3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CalSim
Licenses: GPL 2
Build system: r
Synopsis: The Calibration Simplex
Description:

Generates the calibration simplex (a generalization of the reliability diagram) for three-category probability forecasts, as proposed by Wilks (2013) <doi:10.1175/WAF-D-13-00027.1>.

r-cropdatape 1.0.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/omarbenites/cropdatape
Licenses: Expat
Build system: r
Synopsis: Open Data of Agricultural Production of Crops of Peru
Description:

This package provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes 6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially, in excel files which has been transformed and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell. The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014.

r-cusp 2.3.8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cusp
Licenses: GPL 2
Build system: r
Synopsis: Cusp-Catastrophe Model Fitting Using Maximum Likelihood
Description:

Cobb's maximum likelihood method for cusp-catastrophe modeling (Grasman, van der Maas, and Wagenmakers (2009) <doi:10.18637/jss.v032.i08>; Cobb (1981), Behavioral Science, 26(1), 75-78). Includes a cusp() function for model fitting, and several utility functions for plotting, and for comparing the model to linear regression and logistic curve models.

r-cyclomort 1.0.3
Propagated dependencies: r-survival@3.8-3 r-scales@1.4.0 r-plyr@1.8.9 r-mvtnorm@1.3-3 r-magrittr@2.0.4 r-lubridate@1.9.4 r-flexsurv@2.3.2
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/EliGurarie/cyclomort
Licenses: GPL 3+
Build system: r
Synopsis: Survival Modeling with a Periodic Hazard Function
Description:

Modeling periodic mortality (or other time-to event) processes from right-censored data. Given observations of a process with a known period (e.g. 365 days, 24 hours), functions determine the number, intensity, timing, and duration of peaks of periods of elevated hazard within a period. The underlying model is a mixed wrapped Cauchy function fitted using maximum likelihoods (details in Gurarie et al. (2020) <doi:10.1111/2041-210X.13305>). The development of these tools was motivated by the strongly seasonal mortality patterns observed in many wild animal populations. Thus, the respective periods of higher mortality can be identified as "mortality seasons".

r-cops 1.12-1
Propagated dependencies: r-subplex@1.9 r-smacofx@1.22-0 r-smacof@2.1-7 r-rsolnp@2.0.1 r-rgenoud@5.9-0.11 r-pso@1.0.4 r-nloptr@2.2.1 r-nlcoptim@0.6 r-minqa@1.2.8 r-gensa@1.1.15 r-dfoptim@2023.1.0 r-crs@0.15-39 r-cordillera@1.0-3 r-cmaes@1.0-12 r-analogue@0.18.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://r-forge.r-project.org/projects/stops/
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Cluster Optimized Proximity Scaling
Description:

Multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>). They achieve this by transforming proximities/distances with explicit power functions and penalizing the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two variants: One for finding the configuration directly (COPS-C) with given explicit power transformations and implicit ratio, interval and non-metric optimal scaling transformations (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal hyperparameters for the explicit transformations (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different MDS models (most of the functionality in smacofx) in the COPS framework. The package further contains a function for pattern search optimization, the ``Adaptive Luus-Jaakola Algorithm (Rusch, Mair & Hornik, 2021,<doi:10.1080/10618600.2020.1869027>) and a functions to calculate the phi-distances for count data or histograms.

r-customknitrender 1.0.2
Propagated dependencies: r-rmarkdown@2.30
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=customknitrender
Licenses: Expat
Build system: r
Synopsis: Easily Switch Output Format of 'Rmarkdown' Files with Shared Frontmatter
Description:

Define the output format of rmarkdown files with shared output yaml frontmatter content. Rather than modifying a shared yaml file, use integers to easily switch output formats for rmarkdown files.

r-cofm 1.1.4
Propagated dependencies: r-psych@2.5.6 r-matrixcalc@1.0-6 r-mass@7.3-65 r-copula@1.1-6
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CoFM
Licenses: Expat
Build system: r
Synopsis: Copula Factor Models
Description:

This package provides tools for factor analysis in high-dimensional settings under copula-based factor models. It includes functions to simulate factor-model data with copula-distributed idiosyncratic errors (e.g., Clayton, Gumbel, Frank, Student t and Gaussian copulas) and to perform diagnostic tests such as the Kaiser-Meyer-Olkin measure and Bartlett's test of sphericity. Estimation routines include principal component based factor analysis, projected principal component analysis, and principal orthogonal complement thresholding for large covariance matrix estimation. The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

r-cemco 0.2
Propagated dependencies: r-rootsolve@1.8.2.4 r-nnet@7.3-20 r-mvtnorm@1.3-3 r-mclust@6.1.2 r-mass@7.3-65 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=cemco
Licenses: GPL 2+
Build system: r
Synopsis: Fit 'CemCO' Algorithm
Description:

CemCO algorithm, a model-based (Gaussian) clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process both in cluster centroids and in cluster covariance structures (Relvas C. & Fujita A., (2020) <arXiv:2004.02333>).

r-clidamonger 1.5.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/IWUGERMANY/clidamonger
Licenses: FSDG-compatible
Build system: r
Synopsis: Monthly Climate Data for Germany, Usable for Heating and Cooling Calculations
Description:

This data package contains monthly climate data in Germany, it can be used for heating and cooling calculations (external temperature, heating / cooling days, solar radiation).

r-cschange 0.1.7
Propagated dependencies: r-survival@3.8-3 r-rms@8.1-0 r-hmisc@5.2-4 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CsChange
Licenses: GPL 3
Build system: r
Synopsis: Testing for Change in C-Statistic
Description:

Calculate the confidence interval and p value for change in C-statistic. The adjusted C-statistic is calculated by using formula as "Somers Dxy rank correlation"/2+0.5. The confidence interval was calculated by using the bootstrap method. The p value was calculated by using the Z testing method. Please refer to the article of Peter Ganz et al. (2016) <doi:10.1001/jama.2016.5951>.

r-cdparcoord 1.0.1
Propagated dependencies: r-plotly@4.11.0 r-partools@1.1.7 r-freqparcoord@1.0.1 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=cdparcoord
Licenses: GPL 2+
Build system: r
Synopsis: Top Frequency-Based Parallel Coordinates
Description:

Parallel coordinate plotting with resolutions for large data sets and missing values.

r-crimcv 1.0.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=crimCV
Licenses: GPL 2+
Build system: r
Synopsis: Group-Based Modelling of Longitudinal Data
Description:

This package provides a finite mixture of Zero-Inflated Poisson (ZIP) models for analyzing criminal trajectories.

r-csmes 1.0.1
Propagated dependencies: r-zoo@1.8-14 r-rpart@4.1.24 r-rocr@1.0-11 r-mco@1.17 r-data-table@1.17.8 r-catools@1.18.3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CSMES
Licenses: GPL 2+
Build system: r
Synopsis: Cost-Sensitive Multi-Criteria Ensemble Selection for Uncertain Cost Conditions
Description:

This package provides functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) <doi:10.1016/j.ejor.2020.01.052>) for cost-sensitive learning under unknown cost conditions.

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