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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-paswr2 1.0.5
Propagated dependencies: r-lattice@0.22-7 r-ggplot2@4.0.1 r-e1071@1.7-16
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/alanarnholt/PASWR2
Licenses: GPL 2
Build system: r
Synopsis: Probability and Statistics with R, Second Edition
Description:

This package provides functions and data sets for the text Probability and Statistics with R, Second Edition.

r-plug 0.1.0
Propagated dependencies: r-tibble@3.3.0 r-keyring@1.4.1 r-httr2@1.2.1 r-glue@1.8.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: <https://github.com/StrategicProjects/plug>
Licenses: Expat
Build system: r
Synopsis: Secure and Intuitive Access to 'Plug' Interface
Description:

This package provides a secure and user-friendly interface to interact with the Plug <https://plugbytpf.com.br> API'. It enables developers to store and manage tokens securely using the keyring package, retrieve data from API endpoints with the httr2 package, and handle large datasets with chunked data fetching. Designed for simplicity and security, the package facilitates seamless integration with Plug ecosystem.

r-phantsem 1.0.1
Propagated dependencies: r-tidyr@1.3.1 r-lavaan@0.6-20 r-dplyr@1.1.4 r-corpcor@1.6.10
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=phantSEM
Licenses: Expat
Build system: r
Synopsis: Create Phantom Variables in Structural Equation Models for Sensitivity Analyses
Description:

Create phantom variables, which are variables that were not observed, for the purpose of sensitivity analyses for structural equation models. The package makes it easier for a user to test different combinations of covariances between the phantom variable(s) and observed variables. The package may be used to assess a model's or effect's sensitivity to temporal bias (e.g., if cross-sectional data were collected) or confounding bias.

r-plorn 0.1.1
Propagated dependencies: r-kernlab@0.9-33 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/takakoizumi/PLORN
Licenses: Expat
Build system: r
Synopsis: Prediction with Less Overfitting and Robust to Noise
Description:

This package provides a method for the quantitative prediction with much predictors. This package provides functions to construct the quantitative prediction model with less overfitting and robust to noise.

r-pautilities 1.2.1
Propagated dependencies: r-rlang@1.1.6 r-reshape2@1.4.5 r-rcpp@1.1.0 r-magrittr@2.0.4 r-lubridate@1.9.4 r-lazyeval@0.2.2 r-ggplot2@4.0.1 r-equivalence@0.8.2 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/paulhibbing/PAutilities
Licenses: GPL 3
Build system: r
Synopsis: Streamline Physical Activity Research
Description:

This package provides functions that support a broad range of common tasks in physical activity research, including but not limited to creation of Bland-Altman plots (<doi:10.1136/bmj.313.7049.106>), metabolic calculations such as basal metabolic rate predictions (<https://europepmc.org/article/med/4044297/reloa>), demographic calculations such as age-for-body-mass-index percentile (<https://www.cdc.gov/growthcharts/cdc_charts.htm>), and analysis of bout detection algorithm performance (<https://pubmed.ncbi.nlm.nih.gov/34258524/>).

r-prinvars 1.0.0
Propagated dependencies: r-rdpack@2.6.4 r-pma@1.2-4 r-elasticnet@1.3
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/Ronho/prinvars
Licenses: Expat
Build system: r
Synopsis: Principal Variables
Description:

This package provides methods for reducing the number of features within a data set. See Bauer JO (2021) <doi:10.1145/3475827.3475832> and Bauer JO, Drabant B (2021) <doi:10.1016/j.jmva.2021.104754> for more information on principal loading analysis.

r-plotbart 0.1.7
Propagated dependencies: r-tidyr@1.3.1 r-rpart@4.1.24 r-ggplot2@4.0.1 r-ggdendro@0.2.0 r-dplyr@1.1.4 r-bartcause@1.0-10
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://priism-center.github.io/plotBart/
Licenses: Expat
Build system: r
Synopsis: Diagnostic and Plotting Functions to Supplement 'bartCause'
Description:

This package provides functions to assist in diagnostics and plotting during the causal inference modeling process. Supplements the bartCause package.

r-proxirr 0.5
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=proxirr
Licenses: Expat
Build system: r
Synopsis: Alpha, Beta and Gamma Proximity to Irreplaceability
Description:

This package provides functions to measure Alpha, Beta and Gamma Proximity to Irreplaceability. The methods for Alpha and Beta irreplaceability were first described in: Baisero D., Schuster R. & Plumptre A.J. Redefining and Mapping Global Irreplaceability. Conservation Biology 2021;1-11. <doi:10.1111/cobi.13806>.

r-passt 0.1.3
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/johannes-titz/passt
Licenses: GPL 3
Build system: r
Synopsis: Probability Associator Time (PASS-T)
Description:

Simulates judgments of frequency and duration based on the Probability Associator Time (PASS-T) model. PASS-T is a memory model based on a simple competitive artificial neural network. It can imitate human judgments of frequency and duration, which have been extensively studied in cognitive psychology (e.g. Hintzman (1970) <doi:10.1037/h0028865>, Betsch et al. (2010) <https://psycnet.apa.org/record/2010-18204-003>). The PASS-T model is an extension of the PASS model (Sedlmeier, 2002, ISBN:0198508638). The package provides an easy way to run simulations, which can then be compared with empirical data in human judgments of frequency and duration.

r-pocrm 0.13
Propagated dependencies: r-nnet@7.3-20 r-dfcrm@0.2-2.1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=pocrm
Licenses: GPL 2
Build system: r
Synopsis: Dose Finding in Drug Combination Phase I Trials Using PO-CRM
Description:

This package provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) of Wages, Conaway and O'Quigley (2011) <doi:10.1177/1740774511408748> for use in Phase I trials of combinations of agents. Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user.

r-plis 1.2
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=PLIS
Licenses: GPL 3
Build system: r
Synopsis: Multiplicity Control using Pooled LIS Statistic
Description:

This package provides a multiple testing procedure for testing several groups of hypotheses is implemented. Linear dependency among the hypotheses within the same group is modeled by using hidden Markov Models. It is noted that a smaller p value does not necessarily imply more significance due to the dependency. A typical application is to analyze genome wide association studies datasets, where SNPs from the same chromosome are treated as a group and exhibit strong linear genomic dependency. See Wei Z, Sun W, Wang K, Hakonarson H (2009) <doi:10.1093/bioinformatics/btp476> for more details.

r-pattern-checks 0.1.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=pattern.checks
Licenses: GPL 2+
Build system: r
Synopsis: Identifies Patterned Responses in Scales
Description:

Identifies the entries with patterned responses for psychometric scales. The patterns included in the package are identical (a, a, a), ascending (a, b, c), descending (c, b, a), alternative (a, b, a, b / a, b, c, a, b, c).

r-psychmeta 2.7.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-purrr@1.2.0 r-progress@1.2.3 r-metafor@4.8-0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-curl@7.0.0 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=psychmeta
Licenses: GPL 3+
Build system: r
Synopsis: Psychometric Meta-Analysis Toolkit
Description:

This package provides tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to <https://github.com/psychmeta/psychmeta/issues> or <issues@psychmeta.com>.

r-proton 1.0
Propagated dependencies: r-digest@0.6.39
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=proton
Licenses: GPL 2
Build system: r
Synopsis: The Proton Game
Description:

The Proton Game is a console-based data-crunching game for younger and older data scientists. Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server. You have to solve four data-based puzzles to find the login and password. There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools. Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try. The knowledge of dplyr is not required but may be very helpful. This game is linked with the ,,Pietraszko's Cave story available at http://biecek.pl/BetaBit/Warsaw. It's a part of Beta and Bit series. You will find more about the Beta and Bit series at http://biecek.pl/BetaBit.

r-polyrad 2.0.0
Propagated dependencies: r-stringi@1.8.7 r-rcpp@1.1.0 r-pcamethods@2.2.0 r-fastmatch@1.1-6
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/lvclark/polyRAD
Licenses: GPL 2+
Build system: r
Synopsis: Genotype Calling with Uncertainty from Sequencing Data in Polyploids and Diploids
Description:

Read depth data from genotyping-by-sequencing (GBS) or restriction site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian probability estimates of genotypes in polyploids or diploids. The genotype probabilities, posterior mean genotypes, or most probable genotypes can then be exported for downstream analysis. polyRAD is described by Clark et al. (2019) <doi:10.1534/g3.118.200913>, and the Hind/He statistic for marker filtering is described by Clark et al. (2022) <doi:10.1186/s12859-022-04635-9>. A variant calling pipeline for highly duplicated genomes is also included and is described by Clark et al. (2020, Version 1) <doi:10.1101/2020.01.11.902890>.

r-perspectev 1.1
Propagated dependencies: r-sp@2.2-0 r-mapproj@1.2.12 r-ggplot2@4.0.1 r-foreach@1.5.2 r-doparallel@1.0.17 r-boot@1.3-32 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=perspectev
Licenses: GPL 3
Build system: r
Synopsis: Permutation of Species During Turnover Events
Description:

This package provides a robust framework for analyzing the extent to which differential survival with respect to higher level trait variation is reducible to lower level variation. In addition to its primary test, it also provides functions for simulation-based power analysis, reading in common data set formats, and visualizing results. Temporarily contains an edited version of function hr.mcp() from package wild1', written by Glen Sargeant. For tutorial see: http://evolve.zoo.ox.ac.uk/Evolve/Perspectev.html.

r-properties 0.0-9
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://www.rservicebus.io/
Licenses: GPL 2
Build system: r
Synopsis: Parse 'Java' Properties Files for 'R Service Bus' Applications
Description:

Allows to parse Java properties files in the context of R Service Bus applications.

r-peaxai 1.0.0
Propagated dependencies: r-rms@8.1-0 r-rminer@1.5.0 r-prroc@1.4 r-proc@1.19.0.1 r-isotone@1.1-2 r-iml@0.11.4 r-fastshap@0.1.1 r-dplyr@1.1.4 r-dear@1.5.4 r-caret@7.0-1 r-benchmarking@0.33
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/rgonzalezmoyano/PEAXAI
Licenses: GPL 3
Build system: r
Synopsis: Probabilistic Efficiency Analysis Using Explainable Artificial Intelligence
Description:

This package provides a probabilistic framework that integrates Data Envelopment Analysis (DEA) (Banker et al., 1984) <doi:10.1287/mnsc.30.9.1078> with machine learning classifiers (Kuhn, 2008) <doi:10.18637/jss.v028.i05> to estimate both the (in)efficiency status and the probability of efficiency for decision-making units. The approach trains predictive models on DEA-derived efficiency labels (Charnes et al., 1985) <doi:10.1016/0304-4076(85)90133-2>, enabling explainable artificial intelligence (XAI) workflows with global and local interpretability tools, including permutation importance (Molnar et al., 2018) <doi:10.21105/joss.00786>, Shapley value explanations (Strumbelj & Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and sensitivity analysis (Cortez, 2011) <https://CRAN.R-project.org/package=rminer>. The framework also supports probability-threshold peer selection and counterfactual improvement recommendations for benchmarking and policy evaluation. The probabilistic efficiency framework is detailed in González-Moyano et al. (2025) "Probability-based Technical Efficiency Analysis through Machine Learning", in review for publication.

r-psricalcsm 1.0.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/RFeissIV/PSRICalcSM
Licenses: Expat
Build system: r
Synopsis: Plant Stress Response Index Calculator - Softmax Method
Description:

This package implements the softmax aggregation method for calculating Plant Stress Response Index (PSRI) from time-series germination data under environmental stressors including prions, xenobiotics, osmotic stress, heavy metals, and chemical contaminants. Provides zero-robust PSRI computation through adaptive softmax weighting of germination components (Maximum Stress-adjusted Germination, Maximum Rate of Germination, complementary Mean Time to Germination, and Radicle Vigor Score), eliminating the zero-collapse failure mode of the geometric mean approach implemented in PSRICalc'. Includes perplexity-based temperature parameter calibration and modular component functions for transparent germination analysis. Built on the methodological foundation of the Osmotic Stress Response Index (OSRI) framework developed by Walne et al. (2020) <doi:10.1002/agg2.20087>. Note: This package implements methodology currently under peer review. Please contact the author before publication using this approach. Development followed an iterative human-machine collaboration where all algorithmic design, statistical methodologies, and biological validation logic were conceptualized, tested, and iteratively refined by Richard A. Feiss through repeated cycles of running experimental data, evaluating analytical outputs, and selecting among candidate algorithms and approaches. AI systems (Anthropic Claude and OpenAI GPT) served as coding assistants and analytical sounding boards under continuous human direction. The selection of statistical methods, evaluation of biological plausibility, and all final methodology decisions were made by the human author. AI systems did not independently originate algorithms, statistical approaches, or scientific methodologies.

r-pinma 1.1-2
Propagated dependencies: r-metafor@4.8-0 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=PINMA
Licenses: GPL 3
Build system: r
Synopsis: Improved Methods for Constructing Prediction Intervals for Network Meta-Analysis
Description:

Improved methods to construct prediction intervals for network meta-analysis. The parametric bootstrap and Kenward-Roger-type adjustment by Noma et al. (2022) <forthcoming> are implementable.

r-prototest 1.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mass@7.3-65 r-intervals@0.15.5 r-glmnet@4.1-10
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: http://arxiv.org/abs/1511.07839
Licenses: GPL 2+
Build system: r
Synopsis: Inference on Prototypes from Clusters of Features
Description:

Procedures for testing for group-wide signal in clusters of variables. Tests can be performed for single groups in isolation (univariate) or multiple groups together (multivariate). Specific tests include the exact and approximate (un)selective likelihood ratio tests described in Reid et al (2015), the selective F test and marginal screening prototype test of Reid and Tibshirani (2015). User may pre-specify columns to be included in prototype formation, or allow the function to select them itself. A mixture of these two is also possible. Any variable selection is accounted for using the selective inference framework. Options for non-sampling and hit-and-run null reference distributions.

r-pureseqtmr 1.4
Propagated dependencies: r-tibble@3.3.0 r-stringr@1.6.0 r-readr@2.1.6 r-rcpp@1.1.0 r-rappdirs@0.3.3 r-plyr@1.8.9 r-peptides@2.4.6 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-devtools@2.4.6 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/richelbilderbeek/pureseqtmr/
Licenses: GPL 3
Build system: r
Synopsis: Predict Transmembrane Protein Topology
Description:

Proteins reside in either the cell plasma or in the cell membrane. A membrane protein goes through the membrane at least once. Given the amino acid sequence of a membrane protein, the tool PureseqTM (<https://github.com/PureseqTM/pureseqTM_package>, as described in "Efficient And Accurate Prediction Of Transmembrane Topology From Amino acid sequence only.", Wang, Qing, et al (2019), <doi:10.1101/627307>), can predict the topology of a membrane protein. This package allows one to use PureseqTM from R.

r-pgm2 1.2
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://mlaib.net
Licenses: GPL 3
Build system: r
Synopsis: Recursive Construction of Nested Resolvable Designs and Associated Uniform Designs
Description:

This package implements recursive construction methods for balanced incomplete block designs (BIBDs), their second generation, resolvable BIBDs (RBIBDs), and uniform designs (UDs) derived from projective geometries over GF(2). It enables extraction of nested structures in multiple stages and supports recursive resolution processes, as introduced in Boudraa et al. (2013).

r-pomp 6.4
Propagated dependencies: r-mvtnorm@1.3-3 r-digest@0.6.39 r-desolve@1.40 r-data-table@1.17.8 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://kingaa.github.io/pomp/
Licenses: GPL 3
Build system: r
Synopsis: Statistical Inference for Partially Observed Markov Processes
Description:

This package provides tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.

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