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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-aggrecat 1.0.0
Propagated dependencies: r-vgam@1.1-13 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-r2jags@0.8-9 r-purrr@1.2.0 r-precrec@0.14.5 r-mlmetrics@1.1.3 r-mathjaxr@1.8-0 r-magrittr@2.0.4 r-insight@1.4.3 r-gofkernel@2.1-3 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-desctools@0.99.60 r-crayon@1.5.3 r-coda@0.19-4.1 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://replicats.research.unimelb.edu.au/
Licenses: Expat
Build system: r
Synopsis: Mathematically Aggregating Expert Judgments
Description:

The use of structured elicitation to inform decision making has grown dramatically in recent decades, however, judgements from multiple experts must be aggregated into a single estimate. Empirical evidence suggests that mathematical aggregation provides more reliable estimates than enforcing behavioural consensus on group estimates. aggreCAT provides state-of-the-art mathematical aggregation methods for elicitation data including those defined in Hanea, A. et al. (2021) <doi:10.1371/journal.pone.0256919>. The package also provides functions to visualise and evaluate the performance of your aggregated estimates on validation data.

r-aprof 0.4.1
Propagated dependencies: r-testthat@3.3.0
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: http://github.com/MarcoDVisser/aprof
Licenses: GPL 2+
Build system: r
Synopsis: Amdahl's Profiler, Directed Optimization Made Easy
Description:

Assists the evaluation of whether and where to focus code optimization, using Amdahl's law and visual aids based on line profiling. Amdahl's profiler organizes profiling output files (including memory profiling) in a visually appealing way. It is meant to help to balance development vs. execution time by helping to identify the most promising sections of code to optimize and projecting potential gains. The package is an addition to R's standard profiling tools and is not a wrapper for them.

r-adapt4pv 0.2-3
Propagated dependencies: r-xgboost@1.7.11.1 r-speedglm@0.3-5 r-matrix@1.7-4 r-glmnet@4.1-10 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=adapt4pv
Licenses: GPL 2
Build system: r
Synopsis: Adaptive Approaches for Signal Detection in Pharmacovigilance
Description:

This package provides a collection of several pharmacovigilance signal detection methods based on adaptive lasso. Additional lasso-based and propensity score-based signal detection approaches are also supplied. See Courtois et al <doi:10.1186/s12874-021-01450-3>.

r-asymmetricsords 1.0.0
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=AsymmetricSORDs
Licenses: GPL 2+
Build system: r
Synopsis: Asymmetric Second Order Rotatable Designs (AsymmetricSORDs)
Description:

Response surface designs (RSDs) are widely used for Response Surface Methodology (RSM) based optimization studies, which aid in exploring the relationship between a group of explanatory variables and one or more response variable(s) (G.E.P. Box and K.B. Wilson (1951), "On the experimental attainment of optimum conditions" ; M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI: 10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts".). Second order rotatable designs are the most prominent and popular class of designs used for process and product optimization trials but it is suitable for situations when all the number of levels for each factor is the same. In many practical situations, RSDs with asymmetric levels (J.S. Mehta and M.N. Das (1968). "Asymmetric rotatable designs and orthogonal transformations" ; M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2020) <DOI: 10.1080/02664763.2020.1864817>. "Sequential asymmetric third order rotatable designs (SATORDs)" .) are more suitable as these designs explore more regions in the design space.This package contains functions named Asords() ,CCD_coded(), CCD_original(), SORD_coded() and SORD_original() for generating asymmetric/symmetric RSDs along with the randomized layout. It also contains another function named Pred.var() for generating the variance of predicted response as well as the moment matrix based on a second order model.

r-aslib 0.1.3
Propagated dependencies: r-yaml@2.3.10 r-stringr@1.6.0 r-rweka@0.4-46 r-reshape2@1.4.5 r-plyr@1.8.9 r-paramhelpers@1.14.2 r-parallelmap@1.5.1 r-mlr@2.19.3 r-llama@0.10.1 r-ggplot2@4.0.1 r-data-table@1.17.8 r-corrplot@0.95 r-checkmate@2.3.3 r-bbmisc@1.13 r-batchtools@0.9.18
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://github.com/coseal/aslib-r/
Licenses: GPL 3
Build system: r
Synopsis: Interface to the Algorithm Selection Benchmark Library
Description:

This package provides an interface to the algorithm selection benchmark library at <https://www.coseal.net/aslib/> and the LLAMA package (<https://cran.r-project.org/package=llama>) for building algorithm selection models; see Bischl et al. (2016) <doi:10.1016/j.artint.2016.04.003>.

r-animl 3.2.0
Propagated dependencies: r-reticulate@1.44.1 r-pbapply@1.7-4
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=animl
Licenses: Expat
Build system: r
Synopsis: Collection of ML Tools for Conservation Research
Description:

This package provides functions required to classify subjects within camera trap field data. The package can handle both images and videos. The authors recommend a two-step approach using Microsoft's MegaDector model and then a second model trained on the classes of interest.

r-automerge 0.2.1
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://github.com/posit-dev/automerge-r
Licenses: Expat
Build system: r
Synopsis: R Bindings for 'Automerge' 'CRDT' Library
Description:

This package provides R bindings to the Automerge Conflict-free Replicated Data Type ('CRDT') library. Automerge enables automatic merging of concurrent changes without conflicts, making it ideal for distributed systems, collaborative applications, and offline-first architectures. The approach of local-first software was proposed in Kleppmann, M., Wiggins, A., van Hardenberg, P., McGranaghan, M. (2019) <doi:10.1145/3359591.3359737>. This package supports all Automerge data types (maps, lists, text, counters) and provides both low-level and high-level synchronization protocols for seamless interoperability with JavaScript and other Automerge implementations.

r-anchorregression 0.1.3
Propagated dependencies: r-selectiveinference@1.2.5 r-mgcv@1.9-4 r-glmnet@4.1-10
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://github.com/simzim96/AnchorRegression
Licenses: Expat
Build system: r
Synopsis: Perform AnchorRegression
Description:

This package performs AnchorRegression proposed by Rothenhäusler et al. 2020. The code is adapted from the original paper repository. (<https://github.com/rothenhaeusler/anchor-regression>) The code was developed independently from the authors of the paper.

r-abseil 2023.8.2.1
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://abseil.xingchi.li
Licenses: FSDG-compatible
Build system: r
Synopsis: 'C++' Header Files from 'Abseil'
Description:

Wraps the Abseil C++ library for use by R packages. Original files are from <https://github.com/abseil/abseil-cpp>. Patches are located at <https://github.com/doccstat/abseil-r/tree/main/local/patches>.

r-ampd 0.2
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=ampd
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: An Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals
Description:

This package provides a method for automatic detection of peaks in noisy periodic and quasi-periodic signals. This method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima. For further information see <doi:10.3390/a5040588>.

r-arimaann 0.1.0
Propagated dependencies: r-tseries@0.10-58 r-forecast@8.24.0
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=ARIMAANN
Licenses: GPL 3
Build system: r
Synopsis: Time Series Forecasting using ARIMA-ANN Hybrid Model
Description:

Testing, Implementation, and Forecasting of the ARIMA-ANN hybrid model. The ARIMA-ANN hybrid model combines the distinct strengths of the Auto-Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Network (ANN) model for time series forecasting.For method details see Zhang, GP (2003) <doi:10.1016/S0925-2312(01)00702-0>.

r-apex 1.0.7
Propagated dependencies: r-phangorn@2.12.1 r-ape@5.8-1 r-adegenet@2.1.11
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://github.com/thibautjombart/apex
Licenses: GPL 2+
Build system: r
Synopsis: Phylogenetic Methods for Multiple Gene Data
Description:

Toolkit for the analysis of multiple gene data (Jombart et al. 2017) <doi:10.1111/1755-0998.12567>. apex implements the new S4 classes multidna', multiphyDat and associated methods to handle aligned DNA sequences from multiple genes.

r-agricolaeplotr 0.6.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-stplanr@1.2.3 r-sp@2.2-0 r-sf@1.0-23 r-raster@3.6-32 r-ggspatial@1.1.10 r-ggplot2@4.0.1 r-fieldhub@1.4.2 r-dplyr@1.1.4 r-agricolae@1.3-7
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://github.com/jensharbers/agricolaeplotr
Licenses: GPL 3+
Build system: r
Synopsis: Visualization of Design of Experiments from the 'agricolae' Package
Description:

Visualization of Design of Experiments from the agricolae package with ggplot2 framework The user provides an experiment design from the agricolae package, calls the corresponding function and will receive a visualization with ggplot2 based functions that are specific for each design. As there are many different designs, each design is tested on its type. The output can be modified with standard ggplot2 commands or with other packages with ggplot2 function extensions.

r-atrisk 0.2.0
Propagated dependencies: r-sn@2.1.1 r-quantreg@6.1 r-ggridges@0.5.7 r-ggplot2@4.0.1 r-dfoptim@2023.1.0
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=atRisk
Licenses: GPL 3
Build system: r
Synopsis: At-Risk
Description:

The at-Risk (aR) approach is based on a two-step parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth to financial conditions, the aR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al. (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers.

r-amapgeocode 1.0.0
Propagated dependencies: r-xml2@1.5.0 r-tibble@3.3.0 r-rlang@1.1.6 r-jsonlite@2.0.0 r-httr2@1.2.1 r-dplyr@1.1.4 r-digest@0.6.39
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://womeimingzi11.github.io/amapGeocode/
Licenses: Expat
Build system: r
Synopsis: An Interface to the 'AutoNavi Maps' API Geocoding Services
Description:

Getting and parsing data of location geocode/reverse-geocode and administrative regions from AutoNavi Maps'<https://lbs.amap.com/api/webservice/summary> API.

r-acledr 1.0.1
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-rlang@1.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-lubridate@1.9.4 r-lifecycle@1.0.4 r-httr2@1.2.1 r-httr@1.4.7 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://dtacled.github.io/acledR/
Licenses: GPL 3+
Build system: r
Synopsis: Manipulate ACLED Data
Description:

This package provides tools working with data from ACLED (Armed Conflict Location and Event Data). Functions include simplified access to ACLED's API (<https://apidocs.acleddata.com/>), methods for keeping local versions of ACLED data up-to-date, and functions for common ACLED data transformations.

r-admm 0.3.4
Propagated dependencies: r-rdpack@2.6.4 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrix@1.7-4 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=ADMM
Licenses: GPL 3+
Build system: r
Synopsis: Algorithms using Alternating Direction Method of Multipliers
Description:

This package provides algorithms to solve popular optimization problems in statistics such as regression or denoising based on Alternating Direction Method of Multipliers (ADMM). See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.

r-aftsem 1.0
Propagated dependencies: r-survival@3.8-3 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-quantreg@6.1 r-optimx@2025-4.9
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://github.com/benedma2/aftsem-package
Licenses: GPL 3+
Build system: r
Synopsis: Semiparametric Accelerated Failure Time Model
Description:

This package implements several basic algorithms for estimating regression parameters for semiparametric accelerated failure time (AFT) model. The main methods are: Jin rank-based method (Jin (2003) <doi:10.1093/biomet/90.2.341>), Hellerâ s estimating method (Heller (2012) <doi:10.1198/016214506000001257>), Polynomial smoothed Gehan function method (Chung (2013) <doi:10.1007/s11222-012-9333-9>), Buckley-James method (Buckley (1979) <doi:10.2307/2335161>) and Jin`s improved least squares method (Jin (2006) <doi:10.1093/biomet/93.1.147>). This package can be used for modeling right-censored data and for comparing different estimation algorithms.

r-aiuq 0.5.3
Propagated dependencies: r-supergauss@2.0.4 r-plot3d@1.4.2 r-fftwtools@0.9-11
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=AIUQ
Licenses: GPL 3+
Build system: r
Synopsis: Ab Initio Uncertainty Quantification
Description:

Uncertainty quantification and inverse estimation by probabilistic generative models from the beginning of the data analysis. An example is a Fourier basis method for inverse estimation in scattering analysis of microscopy videos. It does not require specifying a certain range of Fourier bases and it substantially reduces computational cost via the generalized Schur algorithm. See the reference: Mengyang Gu, Yue He, Xubo Liu and Yimin Luo (2023), <doi:10.48550/arXiv.2309.02468>.

r-abasequence 0.1.0
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=abasequence
Licenses: GPL 3
Build system: r
Synopsis: Coding 'ABA' Patterns for Sequence Data
Description:

This package provides a suite of functions for analyzing sequences of events. Users can generate and code sequences based on predefined rules, with a special focus on the identification of sequences coded as ABA (when one element appears, followed by a different one, and then followed by the first). Additionally, the package offers the ability to calculate the length of consecutive ABA'-coded sequences sharing common elements. The methods implemented in this package are based on the work by Ziembowicz, K., Rychwalska, A., & Nowak, A. (2022). <doi:10.1177/10464964221118674>.

r-airgrdatasets 0.2.3
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://gitlab.irstea.fr/HYCAR-Hydro/airgrgalaxy/airgrdatasets
Licenses: FSDG-compatible
Build system: r
Synopsis: Hydro-Meteorological Catchments Datasets for the 'airGR' Packages
Description:

Sample of hydro-meteorological datasets extracted from the CAMELS-FR French database <doi:10.57745/WH7FJR>. It provides metadata and catchment-scale aggregated hydro-meteorological time series on a pool of French catchments for use by the airGR packages.

r-allometry 0.1.1
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=allometry
Licenses: GPL 3+
Build system: r
Synopsis: Examples of Datasets on Allometry
Description:

Examples of datasets on allometry, the study of the relationship of biological traits to body size. This package contains the dataset of morphological measurement taken from 113 maritime earwigs (Anisolabis maritima) by Matsuzawa and Konuma (2025) <doi:10.1093/biolinnean/blaf031>.

r-adwave 1.4
Propagated dependencies: r-waveslim@1.8.5
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://doi.org/10.1534/genetics.115.176842
Licenses: GPL 2+
Build system: r
Synopsis: Wavelet Analysis of Genomic Data from Admixed Populations
Description:

This package implements wavelet-based approaches for describing population admixture. Principal Components Analysis (PCA) is used to define the population structure and produce a localized admixture signal for each individual. Wavelet summaries of the PCA output describe variation present in the data and can be related to population-level demographic processes. For more details, see J Sanderson, H Sudoyo, TM Karafet, MF Hammer and MP Cox. 2015. Reconstructing past admixture processes from local genomic ancestry using wavelet transformation. Genetics 200:469-481 <doi:10.1534/genetics.115.176842>.

r-asmbpls 1.0.0
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-ggpubr@0.6.2 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://cran.r-project.org/package=asmbPLS
Licenses: GPL 2+
Build system: r
Synopsis: Predicting and Classifying Patient Phenotypes with Multi-Omics Data
Description:

Adaptive Sparse Multi-block Partial Least Square, a supervised algorithm, is an extension of the Sparse Multi-block Partial Least Square, which allows different quantiles to be used in different blocks of different partial least square components to decide the proportion of features to be retained. The best combinations of quantiles can be chosen from a set of user-defined quantiles combinations by cross-validation. By doing this, it enables us to do the feature selection for different blocks, and the selected features can then be further used to predict the outcome. For example, in biomedical applications, clinical covariates plus different types of omics data such as microbiome, metabolome, mRNA data, methylation data, copy number variation data might be predictive for patients outcome such as survival time or response to therapy. Different types of data could be put in different blocks and along with survival time to fit the model. The fitted model can then be used to predict the survival for the new samples with the corresponding clinical covariates and omics data. In addition, Adaptive Sparse Multi-block Partial Least Square Discriminant Analysis is also included, which extends Adaptive Sparse Multi-block Partial Least Square for classifying the categorical outcome.

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