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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-alarmdata 0.2.4
Propagated dependencies: r-tinytiger@0.0.11 r-tidyselect@1.2.1 r-stringr@1.6.0 r-sf@1.0-23 r-rlang@1.1.6 r-redistmetrics@1.0.11 r-redist@4.3.1 r-readr@2.1.6 r-rappdirs@0.3.3 r-geomander@2.5.2 r-dplyr@1.1.4 r-dataverse@0.3.16 r-curl@7.0.0 r-cli@3.6.5 r-censable@0.0.8
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://github.com/alarm-redist/alarmdata/
Licenses: Expat
Synopsis: Download, Merge, and Process Redistricting Data
Description:

Utility functions to download and process data produced by the ALARM Project, including 2020 redistricting files Kenny and McCartan (2021) <https://alarm-redist.org/posts/2021-08-10-census-2020/> and the 50-State Redistricting Simulations of McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki, and Imai (2022) <doi:10.7910/DVN/SLCD3E>. The package extends the data introduced in McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki, and Imai (2022) <doi:10.1038/s41597-022-01808-2> to also include states with only a single district. The package also includes the Japanese 2022 redistricting files from the 47-Prefecture Redistricting Simulations of Miyazaki, Yamada, Yatsuhashi, and Imai (2022) <doi:10.7910/DVN/Z9UKSH>.

r-animbook 1.0.1
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-rcolorbrewer@1.1-3 r-purrr@1.2.0 r-plotly@4.11.0 r-ggplot2@4.0.1 r-gganimate@1.0.11 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://github.com/KrisanatA/animbook
Licenses: Expat
Synopsis: Visualizing Changes in Performance Measures and Demographic Affiliations using Animation
Description:

Create an interactive visualization to be used for communication purposes. Providing the function for preparing, plotting, and animating the data. Krisanat Anukarnsakulchularp (2023) <https://github.com/KrisanatA/animbook-journal>.

r-acroname 0.1.0
Propagated dependencies: r-stringr@1.6.0 r-readr@2.1.6 r-r-utils@2.13.0 r-purrr@1.2.0 r-magrittr@2.0.4 r-hunspell@3.0.6 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=acroname
Licenses: GPL 3
Synopsis: Engine for Acronyms and Initialisms
Description:

This package provides a tool for generating acronyms and initialisms from arbitrary text input.

r-adlp 0.1.0
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://github.com/agi-lab/ADLP
Licenses: GPL 3
Synopsis: Accident and Development Period Adjusted Linear Pools for Actuarial Stochastic Reserving
Description:

Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" <doi:10.48550/arXiv.2206.08541>.

r-amazonspr 0.1.0
Propagated dependencies: r-jsonlite@2.0.0
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://windsor.ai/
Licenses: GPL 3
Synopsis: Get Amazon Sp Data via the 'Windsor.ai' API
Description:

Collect your data on digital marketing campaigns from Amazon Sp using the Windsor.ai API <https://windsor.ai/api-fields/>.

r-autotester 0.1.7
Propagated dependencies: r-nortest@1.0-4 r-multcompview@0.1-10 r-ggplot2@4.0.1 r-fsa@0.10.0 r-dplyr@1.1.4 r-crayon@1.5.3 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=autotesteR
Licenses: Expat
Synopsis: Automated Functions for Basic Statistical Tests
Description:

This package provides simple and intuitive functions for basic statistical analyses. Methods include the t-test (Student 1908 <doi:10.1093/biomet/6.1.1>), the Mann-Whitney U test (Mann and Whitney 1947 <doi:10.1214/aoms/1177730491>), Pearson's correlation (Pearson 1895 <doi:10.1098/rspl.1895.0041>), and analysis of variance (Fisher 1925, <doi:10.1007/978-1-4612-4380-9_5>). Functions are compatible with ggplot2 and dplyr'.

r-adismf 0.1.1
Propagated dependencies: r-nls2@0.3-4 r-ggplot2@4.0.1 r-aiccmodavg@2.3-4
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=AdIsMF
Licenses: GPL 3
Synopsis: Adsorption Isotherm Model Fitting
Description:

The Langmuir and Freundlich adsorption isotherms are pivotal in characterizing adsorption processes, essential across various scientific disciplines. Proper interpretation of adsorption isotherms involves robust fitting of data to the models, accurate estimation of parameters, and efficiency evaluation of the models, both in linear and non-linear forms. For researchers and practitioners in the fields of chemistry, environmental science, soil science, and engineering, a comprehensive package that satisfies all these requirements would be ideal for accurate and efficient analysis of adsorption data, precise model selection and validation for rigorous scientific inquiry and real-world applications. Details can be found in Langmuir (1918) <doi:10.1021/ja02242a004> and Giles (1973) <doi:10.1111/j.1478-4408.1973.tb03158.x>.

r-afcolours 1.0.0
Propagated dependencies: r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=afcolours
Licenses: Expat
Synopsis: Government Analysis Function Recommended Accessible Colour Palette
Description:

Government Analysis Function recommended colours for use in charts on gov.uk to help meet accessibility guidance.

r-adbcsqlite 0.21.0
Dependencies: sqlite@3.39.3
Propagated dependencies: r-adbcdrivermanager@0.21.0
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://arrow.apache.org/adbc/current/r/adbcsqlite/
Licenses: FSDG-compatible
Synopsis: 'Arrow' Database Connectivity ('ADBC') 'SQLite' Driver
Description:

This package provides a developer-facing interface to the Arrow Database Connectivity ('ADBC') SQLite driver for the purposes of building high-level database interfaces for users. ADBC <https://arrow.apache.org/adbc/> is an API standard for database access libraries that uses Arrow for result sets and query parameters.

r-awdb 0.1.3
Propagated dependencies: r-sf@1.0-23 r-rlang@1.1.6 r-httr2@1.2.1 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://github.com/kbvernon/awdb
Licenses: Expat
Synopsis: Query the USDA NWCC Air and Water Database REST API
Description:

Query the four endpoints of the Air and Water Database (AWDB) REST API maintained by the National Water and Climate Center (NWCC) at the United States Department of Agriculture (USDA). Endpoints include data, forecast, reference-data, and metadata. The package is extremely light weight, with Rust via extendr doing most of the heavy lifting to deserialize and flatten deeply nested JSON responses. The AWDB can be found at <https://wcc.sc.egov.usda.gov/awdbRestApi/swagger-ui/index.html>.

r-accsamplingdesign 0.0.7
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://github.com/vietha/AccSamplingDesign
Licenses: GPL 3
Synopsis: Acceptance Sampling Plans Design
Description:

This package provides tools for designing and analyzing Acceptance Sampling plans. Supports both Attributes Sampling (Binomial and Poisson distributions) and Variables Sampling (Normal and Beta distributions), enabling quality control for fractional and compositional data. Uses nonlinear programming for sampling plan optimization, minimizing sample size while controlling producer's and consumer's risks. Operating Characteristic curves are available for plan visualization.

r-anxietysleep 0.0.1
Propagated dependencies: r-lifecycle@1.0.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://github.com/NIM-ACh/AnxietySleep
Licenses: FSDG-compatible
Synopsis: Sleep Quality and Anxiety in Confinement
Description:

Data from the anxiety and confinement study from Alvarado-Aravena et al. (2022) <doi:10.3390/bs12100398>.

r-ashapesampler 1.0.0
Propagated dependencies: r-truncnorm@1.0-9 r-tda@1.9.4 r-rvcg@0.25 r-pracma@2.4.6 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17 r-alphashape3d@1.3.3 r-alphahull@2.5
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=ashapesampler
Licenses: GPL 3+
Synopsis: Generating Alpha Shapes
Description:

Understanding morphological variation is an important task in many applications. Recent studies in computational biology have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the landscape of current shape simulation methods has been mostly limited to approaches that use black-box inference---making it difficult to systematically assess the power and calibration of sub-image models. In this R package, we introduce the alpha-shape sampler: a probabilistic framework for simulating realistic 2D and 3D shapes based on probability distributions which can be learned from real data or explicitly stated by the user. The ashapesampler package supports two mechanisms for sampling shapes in two and three dimensions. The first, empirically sampling based on an existing data set, was highlighted in the original main text of the paper. The second, probabilistic sampling from a known distribution, is the computational implementation of the theory derived in that paper. Work based on Winn-Nunez et al. (2024) <doi:10.1101/2024.01.09.574919>.

r-aftables 1.0.2
Propagated dependencies: r-pillar@1.11.1 r-openxlsx@4.2.8.1
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://best-practice-and-impact.github.io/aftables/
Licenses: Expat
Synopsis: Create Spreadsheet Publications Following Best Practice
Description:

Generate spreadsheet publications that follow best practice guidance from the UK government's Analysis Function, available at <https://analysisfunction.civilservice.gov.uk/policy-store/releasing-statistics-in-spreadsheets/>, with a focus on accessibility. See also the Python package gptables'.

r-argentum 1.0.0
Propagated dependencies: r-xml2@1.5.0 r-sf@1.0-23 r-readr@2.1.6 r-httr@1.4.7
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://github.com/thomasartopoulos/argentum/
Licenses: Expat
Synopsis: Access and Import WMS and WFS Data from Argentine Organizations
Description:

This package provides functions to retrieve information from Web Feature Service (WFS) and Web Map Service (WMS) layers from various Argentine organizations and import them into R for further analysis. WFS and WMS are standardized protocols for serving georeferenced map data over the internet. For more information on these services, see <https://www.ogc.org/publications/standard/wfs/> and <https://www.ogc.org/publications/standard/wms/>.

r-autodb 3.2.4
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://charnelmouse.github.io/autodb/
Licenses: Modified BSD
Synopsis: Automatic Database Normalisation for Data Frames
Description:

Automatic normalisation of a data frame to third normal form, with the intention of easing the process of data cleaning. (Usage to design your actual database for you is not advised.) Originally inspired by the AutoNormalize library for Python by Alteryx (<https://github.com/alteryx/autonormalize>), with various changes and improvements. Automatic discovery of functional or approximate dependencies, normalisation based on those, and plotting of the resulting "database" via Graphviz', with options to exclude some attributes at discovery time, or remove discovered dependencies at normalisation time.

r-aftr2 0.1.0
Propagated dependencies: r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=aftR2
Licenses: GPL 3
Synopsis: R-Squared Measure under Accelerated Failure Time (AFT) Models
Description:

Compute the R-squared measure under the accelerated failure time (AFT) models proposed in Chan et. al. (2018) <doi:10.1080/03610918.2016.1177072>.

r-anofa 0.1.3
Propagated dependencies: r-superb@1.0.1 r-rrapply@1.2.8 r-rdpack@2.6.4 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://dcousin3.github.io/ANOFA/
Licenses: GPL 3
Synopsis: Analyses of Frequency Data
Description:

Analyses of frequencies can be performed using an alternative test based on the G statistic. The test has similar type-I error rates and power as the chi-square test. However, it is based on a total statistic that can be decomposed in an additive fashion into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVA. We call this set of tools ANOFA (Analysis of Frequency data) to highlight its similarities with ANOVA. This framework also renders plots of frequencies along with confidence intervals. Finally, effect sizes and planning statistical power are easily done under this framework. The ANOFA is a tool that assesses the significance of effects instead of the significance of parameters; as such, it is more intuitive to most researchers than alternative approaches based on generalized linear models. See Laurencelle and Cousineau (2023) <doi:10.20982/tqmp.19.2.p173>.

r-anocva 0.1.1
Propagated dependencies: r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=anocva
Licenses: GPL 3+
Synopsis: Non-Parametric Statistical Test to Compare Clustering Structures
Description:

This package provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test to compare clustering structures with applications in functional magnetic resonance imaging data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering.

r-arkhe 1.11.0
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://codeberg.org/tesselle/arkhe
Licenses: GPL 3+
Synopsis: Tools for Cleaning Rectangular Data
Description:

This package provides a dependency-free collection of simple functions for cleaning rectangular data. This package allows to detect, count and replace values or discard rows/columns using a predicate function. In addition, it provides tools to check conditions and return informative error messages.

r-admmdensestsubmatrix 0.1.0
Propagated dependencies: r-rdpack@2.6.4
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=admmDensestSubmatrix
Licenses: CC0
Synopsis: Alternating Direction Method of Multipliers to Solve Dense Dubmatrix Problem
Description:

Solves the problem of identifying the densest submatrix in a given or sampled binary matrix, Bombina et al. (2019) <arXiv:1904.03272>.

r-ahm 1.0.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-quadprog@1.5-8 r-plgp@1.1-12 r-mixexp@1.2.7.1 r-matrix@1.7-4 r-glmnet@4.1-10 r-dplyr@1.1.4 r-devtools@2.4.6
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=AHM
Licenses: GPL 3
Synopsis: Additive Heredity Model: Method for the Mixture-of-Mixtures Experiments
Description:

An implementation of the additive heredity model for the mixture-of-mixtures experiments of Shen et al. (2019) in Technometrics <doi:10.1080/00401706.2019.1630010>. The additive heredity model considers an additive structure to inherently connect the major components with the minor components. The additive heredity model has a meaningful interpretation for the estimated model because of the hierarchical and heredity principles applied and the nonnegative garrote technique used for variable selection.

r-asm 0.2.4
Propagated dependencies: r-quantreg@6.1 r-pracma@2.4.6 r-mass@7.3-65 r-iso@0.0-21 r-fdrtool@1.2.18
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=asm
Licenses: GPL 3+
Synopsis: Optimal Convex M-Estimation for Linear Regression via Antitonic Score Matching
Description:

This package performs linear regression with respect to a data-driven convex loss function that is chosen to minimize the asymptotic covariance of the resulting M-estimator. The convex loss function is estimated in 5 steps: (1) form an initial OLS (ordinary least squares) or LAD (least absolute deviation) estimate of the regression coefficients; (2) use the resulting residuals to obtain a kernel estimator of the error density; (3) estimate the score function of the errors by differentiating the logarithm of the kernel density estimate; (4) compute the L2 projection of the estimated score function onto the set of decreasing functions; (5) take a negative antiderivative of the projected score function estimate. Newton's method (with Hessian modification) is then used to minimize the convex empirical risk function. Further details of the method are given in Feng et al. (2024) <doi:10.48550/arXiv.2403.16688>.

r-anca 0.1.0
Propagated dependencies: r-units@1.0-0 r-tidyr@1.3.1 r-tern@0.9.10 r-stringr@1.6.0 r-scales@1.4.0 r-rlistings@0.2.13 r-rlang@1.1.6 r-purrr@1.2.0 r-plotly@4.11.0 r-pknca@0.12.1 r-officer@0.7.1 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-formatters@0.5.12 r-flextable@0.9.10 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://pharmaverse.github.io/aNCA/
Licenses: FSDG-compatible
Synopsis: (Pre-)Clinical NCA in a Dynamic Shiny App
Description:

An interactive shiny application for performing non-compartmental analysis (NCA) on pre-clinical and clinical pharmacokinetic data. The package builds on PKNCA for core estimators and provides interactive visualizations, CDISC outputs ('ADNCA', PP', ADPP') and configurable TLGs (tables, listings, and graphs). Typical use cases include exploratory analysis, validation, reporting or teaching/demonstration of NCA methods. Methods and core estimators are described in Denney, Duvvuri, and Buckeridge (2015) "Simple, Automatic Noncompartmental Analysis: The PKNCA R Package" <doi:10.1007/s10928-015-9432-2>.

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