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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-naturesounds 1.0.5
Propagated dependencies: r-tuner@1.4.7 r-knitr@1.50
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/maRce10/NatureSounds
Licenses: GPL 2+
Build system: r
Synopsis: Animal Sounds for Bioacustic Analysis
Description:

Collection of example animal sounds for bioacoustic analysis.

r-nbshiny 0.1.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NBShiny
Licenses: GPL 2
Build system: r
Synopsis: Interactive Document for Working with Naive Bayes Classification
Description:

An interactive document on the topic of naive Bayes classification analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://kartikeyab.shinyapps.io/NBShiny/>.

r-newmanomics 1.0.14
Propagated dependencies: r-oompabase@3.2.10
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: http://oompa.r-forge.r-project.org/
Licenses: ASL 2.0
Build system: r
Synopsis: Extending the Newman Studentized Range Statistic to Transcriptomics
Description:

Extends the classical Newman studentized range statistic in various ways that can be applied to genome-scale transcriptomic or other expression data.

r-nlraa 1.9.10
Propagated dependencies: r-nlme@3.1-168 r-mgcv@1.9-4 r-matrix@1.7-4 r-mass@7.3-65 r-knitr@1.50 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nlraa
Licenses: GPL 3
Build system: r
Synopsis: Nonlinear Regression for Agricultural Applications
Description:

Additional nonlinear regression functions using self-start (SS) algorithms. One of the functions is the Beta growth function proposed by Yin et al. (2003) <doi:10.1093/aob/mcg029>. There are several other functions with breakpoints (e.g. linear-plateau, plateau-linear, exponential-plateau, plateau-exponential, quadratic-plateau, plateau-quadratic and bilinear), a non-rectangular hyperbola and a bell-shaped curve. Twenty eight (28) new self-start (SS) functions in total. This package also supports the publication Nonlinear regression Models and applications in agricultural research by Archontoulis and Miguez (2015) <doi:10.2134/agronj2012.0506>, a book chapter with similar material <doi:10.2134/appliedstatistics.2016.0003.c15> and a publication by Oddi et. al. (2019) in Ecology and Evolution <doi:10.1002/ece3.5543>. The function nlsLMList uses nlsLM for fitting, but it is otherwise almost identical to nlme::nlsList'.In addition, this release of the package provides functions for conducting simulations for nlme and gnls objects as well as bootstrapping. These functions are intended to work with the modeling framework of the nlme package. It also provides four vignettes with extended examples.

r-neurohcp 0.11.0
Propagated dependencies: r-xml2@1.5.0 r-httr@1.4.7 r-digest@0.6.39 r-base64enc@0.1-3 r-aws-s3@0.3.22
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://db.humanconnectome.org
Licenses: GPL 2
Build system: r
Synopsis: Human 'Connectome' Project Interface
Description:

Downloads and reads data from Human Connectome Project <https://db.humanconnectome.org> using Amazon Web Services ('AWS') S3 buckets.

r-nmfn 2.0.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NMFN
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Non-Negative Matrix Factorization
Description:

Non-negative Matrix Factorization.

r-nph 2.1
Propagated dependencies: r-survival@3.8-3 r-mvtnorm@1.3-3 r-multcomp@1.4-29 r-muhaz@1.2.6.4 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nph
Licenses: GPL 3
Build system: r
Synopsis: Planning and Analysing Survival Studies under Non-Proportional Hazards
Description:

Piecewise constant hazard functions are used to flexibly model survival distributions with non-proportional hazards and to simulate data from the specified distributions. A function to calculate weighted log-rank tests for the comparison of two hazard functions is included. Also, a function to calculate a test using the maximum of a set of test statistics from weighted log-rank tests (MaxCombo test) is provided. This test utilizes the asymptotic multivariate normal joint distribution of the separate test statistics. The correlation is estimated from the data. These methods are described in Ristl et al. (2021) <doi:10.1002/pst.2062>. Finally, a function is provided for the estimation and inferential statistics of various parameters that quantify the difference between two survival curves. Eligible parameters are differences in survival probabilities, log survival probabilities, complementary log log (cloglog) transformed survival probabilities, quantiles of the survival functions, log transformed quantiles, restricted mean survival times, as well as an average hazard ratio, the Cox model score statistic (logrank statistic), and the Cox-model hazard ratio. Adjustments for multiple testing and simultaneous confidence intervals are calculated using a multivariate normal approximation to the set of selected parameters.

r-nc 2026.2.20
Propagated dependencies: r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/tdhock/nc
Licenses: GPL 3
Build system: r
Synopsis: Named Capture to Data Tables
Description:

User-friendly functions for extracting a data table (row for each match, column for each group) from non-tabular text data using regular expressions, and for melting columns that match a regular expression. Patterns are defined using a readable syntax that makes it easy to build complex patterns in terms of simpler, re-usable sub-patterns. Named R arguments are translated to column names in the output; capture groups without names are used internally in order to provide a standard interface to three regular expression C libraries ('PCRE', RE2', ICU'). Output can also include numeric columns via user-specified type conversion functions.

r-nonparametric-bayes 0.0.1
Propagated dependencies: r-progress@1.2.3 r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nonparametric.bayes
Licenses: Expat
Build system: r
Synopsis: Project Code - Nonparametric Bayes
Description:

Basic implementation of a Gibbs sampler for a Chinese Restaurant Process along with some visual aids to help understand how the sampling works. This is developed as part of a postgraduate school project for an Advanced Bayesian Nonparametric course. It is inspired by Tamara Broderick's presentation on Nonparametric Bayesian statistics given at the Simons institute.

r-nndiagram 1.0.0
Propagated dependencies: r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/ccfang2/nndiagram
Licenses: Expat
Build system: r
Synopsis: Generator of 'LaTeX' Code for Drawing Neural Network Diagrams with 'TikZ'
Description:

Generates LaTeX code for drawing well-formatted neural network diagrams with TikZ'. Users have to define number of neurons on each layer, and optionally define neuron connections they would like to keep or omit, layers they consider to be oversized and neurons they would like to draw with lighter color. They can also specify the title of diagram, color, opacity of figure, labels of layers, input and output neurons. In addition, this package helps to produce LaTeX code for drawing activation functions which are crucial in neural network analysis. To make the code work in a LaTeX editor, users need to install and import some TeX packages including TikZ in the setting of TeX file.

r-nat-utils 0.6.1
Propagated dependencies: r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/natverse/nat.utils
Licenses: GPL 3+
Build system: r
Synopsis: File System Utility Functions for 'NeuroAnatomy Toolbox'
Description:

Utility functions that may be of general interest but are specifically required by the NeuroAnatomy Toolbox ('nat'). Includes functions to provide a basic make style system to update files based on timestamp information, file locking and touch utility. Convenience functions for working with file paths include abs2rel', split_path and common_path'. Finally there are utility functions for working with zip and gzip files including integrity tests.

r-ngspatial 1.2-2
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=ngspatial
Licenses: GPL 2+
Build system: r
Synopsis: Fitting the Centered Autologistic and Sparse Spatial Generalized Linear Mixed Models for Areal Data
Description:

This package provides tools for analyzing spatial data, especially non- Gaussian areal data. The current version supports the sparse restricted spatial regression model of Hughes and Haran (2013) <DOI:10.1111/j.1467-9868.2012.01041.x>, the centered autologistic model of Caragea and Kaiser (2009) <DOI:10.1198/jabes.2009.07032>, and the Bayesian spatial filtering model of Hughes (2017) <arXiv:1706.04651>.

r-noweb 1.1-4
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=noweb
Licenses: LGPL 2.0
Build system: r
Synopsis: The 'noweb' System for R
Description:

The noweb system for source code, implemented in R.

r-numgen 0.1.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=numGen
Licenses: GPL 3
Build system: r
Synopsis: Number Series Generator
Description:

This package provides a number series generator that creates number series items based on cognitive models.

r-node2vec 0.1.0
Propagated dependencies: r-word2vec@0.4.1 r-vegan@2.7-2 r-vctrs@0.6.5 r-rlist@0.4.6.2 r-igraph@2.2.1 r-dplyr@1.1.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=node2vec
Licenses: GPL 3+
Build system: r
Synopsis: Algorithmic Framework for Representational Learning on Graphs
Description:

Given any graph, the node2vec algorithm can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks.The techniques are detailed in the paper "node2vec: Scalable Feature Learning for Networks" by Aditya Grover, Jure Leskovec(2016),available at <arXiv:1607.00653>.

r-nifti-io 1.0.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nifti.io
Licenses: Expat
Build system: r
Synopsis: Read and Write NIfTI Files
Description:

This package provides tools for reading and writing NIfTI-1.1 (NII) files, including optimized voxelwise read/write operations and a simplified method to write dataframes to NII. Specification of the NIfTI-1.1 format can be found here <https://nifti.nimh.nih.gov/nifti-1>. Scientific publication first using these tools Koscik TR, Man V, Jahn A, Lee CH, Cunningham WA (2020) <doi:10.1016/j.neuroimage.2020.116764> "Decomposing the neural pathways in a simple, value-based choice." Neuroimage, 214, 116764.

r-nonmem2r 0.2.5
Propagated dependencies: r-splines2@0.5.4 r-reshape2@1.4.5 r-mvtnorm@1.3-3 r-mass@7.3-65 r-latticeextra@0.6-31 r-lattice@0.22-7 r-gridextra@2.3 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nonmem2R
Licenses: FSDG-compatible
Build system: r
Synopsis: Loading NONMEM Output Files with Functions for Visual Predictive Checks (VPC) and Goodness of Fit (GOF) Plots
Description:

Loading NONMEM (NONlinear Mixed-Effect Modeling, <https://www.iconplc.com/solutions/technologies/nonmem/>) and PSN (Perl-speaks-NONMEM, <https://uupharmacometrics.github.io/PsN/>) output files to extract parameter estimates, provide visual predictive check (VPC) and goodness of fit (GOF) plots, and simulate with parameter uncertainty.

r-nimblewomble 0.1.0
Propagated dependencies: r-terra@1.8-86 r-sp@2.2-0 r-sf@1.0-23 r-nimble@1.4.1 r-metr@0.18.3 r-mba@0.1-2 r-ggspatial@1.1.10 r-ggplot2@4.0.1 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nimblewomble
Licenses: Expat
Build system: r
Synopsis: Bayesian Wombling using 'nimble'
Description:

This package provides a software package to perform Wombling, or boundary analysis, using the nimble Bayesian hierarchical modeling environment. Wombling is used widely to track regions of rapid change within the spatial reference domain. Specific functions in the package implement Gaussian process models for point-referenced spatial data followed by predictive inference on rates of change over curves using line integrals. We demonstrate model based Bayesian inference using posterior distributions featuring simple analytic forms while offering uncertainty quantification over curves. For more details on wombling please see, Banerjee and Gelfand (2006) <doi:10.1198/016214506000000041> and Halder, Banerjee and Dey (2024) <doi:10.1080/01621459.2023.2177166>.

r-newscatcher 0.1.2
Propagated dependencies: r-tidyrss@2.0.7
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/discindo/newscatcheR/
Licenses: Expat
Build system: r
Synopsis: Programmatically Collect Normalized News from (Almost) Any Website
Description:

Programmatically collect normalized news from (almost) any website. An R clone of the <https://github.com/kotartemiy/newscatcher> Python module.

r-nbr 0.1.5
Propagated dependencies: r-nlme@3.1-168
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NBR
Licenses: GPL 3+
Build system: r
Synopsis: Network-Based R-Statistics using Mixed Effects Models
Description:

An implementation of network-based statistics in R using mixed effects models. Theoretical background for Network-Based Statistics can be found in Zalesky et al. (2010) <doi:10.1016/j.neuroimage.2010.06.041>. For Mixed Effects Models check the R package <https://CRAN.R-project.org/package=nlme>.

r-neuralestimators 0.2.0
Dependencies: julia@1.8.5
Propagated dependencies: r-magrittr@2.0.4 r-juliaconnector@1.1.5
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/msainsburydale/NeuralEstimators
Licenses: GPL 2+
Build system: r
Synopsis: Likelihood-Free Parameter Estimation using Neural Networks
Description:

An R interface to the Julia package NeuralEstimators.jl'. The package facilitates the user-friendly development of neural Bayes estimators, which are neural networks that map data to a point summary of the posterior distribution (Sainsbury-Dale et al., 2024, <doi:10.1080/00031305.2023.2249522>). These estimators are likelihood-free and amortised, in the sense that, once the neural networks are trained on simulated data, inference from observed data can be made in a fraction of the time required by conventional approaches. The package also supports amortised Bayesian or frequentist inference using neural networks that approximate the posterior or likelihood-to-evidence ratio (Zammit-Mangion et al., 2025, Sec. 3.2, 5.2, <doi:10.48550/arXiv.2404.12484>). The package accommodates any model for which simulation is feasible by allowing users to define models implicitly through simulated data.

r-nonprobsvy 0.2.3
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/ncn-foreigners/nonprobsvy
Licenses: Expat
Build system: r
Synopsis: Inference Based on Non-Probability Samples
Description:

Statistical inference with non-probability samples when auxiliary information from external sources such as probability samples or population totals or means is available. The package implements various methods such as inverse probability (propensity score) weighting, mass imputation and doubly robust approach. Details can be found in: Chen et al. (2020) <doi:10.1080/01621459.2019.1677241>, Yang et al. (2020) <doi:10.1111/rssb.12354>, Kim et al. (2021) <doi:10.1111/rssa.12696>, Yang et al. (2021) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021001/article/00004-eng.htm> and Wu (2022) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2022002/article/00002-eng.htm>. For details on the package and its functionalities see <doi:10.48550/arXiv.2504.04255>.

r-networktoolbox 1.4.4
Propagated dependencies: r-r-matlab@3.7.0 r-qgraph@1.9.8 r-pwr@1.3-0 r-psych@2.5.6 r-ppcor@1.1 r-pbapply@1.7-4 r-mass@7.3-65 r-isingfit@0.4 r-igraph@2.2.1 r-foreach@1.5.2 r-fdrtool@1.2.18 r-doparallel@1.0.17 r-corrplot@0.95
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NetworkToolbox
Licenses: GPL 3+
Build system: r
Synopsis: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis
Description:

This package implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.

r-networkdynamicdata 0.3.0
Propagated dependencies: r-networkdynamic@0.11.5 r-network@1.19.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://statnet.org
Licenses: FSDG-compatible
Build system: r
Synopsis: Dynamic (Longitudinal) Network Datasets
Description:

This package provides a collection of dynamic network data sets from various sources and multiple authors represented as networkDynamic'-formatted objects.

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