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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-neonstore 0.5.1
Propagated dependencies: r-zip@2.3.3 r-vroom@1.6.6 r-thor@1.2.0 r-r-utils@2.13.0 r-progress@1.2.3 r-memoise@2.0.1 r-httr@1.4.7 r-glue@1.8.0 r-duckdbfs@0.1.2 r-duckdb@1.4.2 r-dbi@1.2.3 r-cachem@1.1.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=neonstore
Licenses: Expat
Synopsis: NEON Data Store
Description:

The National Ecological Observatory Network (NEON) provides access to its numerous data products through its REST API, <https://data.neonscience.org/data-api/>. This package provides a high-level user interface for downloading and storing NEON data products. Unlike neonUtilities', this package will avoid repeated downloading, provides persistent storage, and improves performance. neonstore can also construct a local duckdb database of stacked tables, making it possible to work with tables that are far to big to fit into memory.

r-nonparquantilecausality 0.1.0
Propagated dependencies: r-quantreg@6.1 r-kernsmooth@2.23-26 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://www.mbalcilar.net
Licenses: Expat
Synopsis: Nonparametric Causality in Quantiles Test
Description:

This package implements the nonparametric causality-in-quantiles test (in mean or variance), returning a test object with an S3 plot() method. The current implementation uses one lag of each series (first-order Granger causality setup). Methodology is based on Balcilar, Gupta, and Pierdzioch (2016a) <doi:10.1016/j.resourpol.2016.04.004> and Balcilar et al. (2016) <doi:10.1007/s11079-016-9388-x>.

r-nimblemacros 0.1.1
Propagated dependencies: r-reformulas@0.4.2 r-nimble@1.4.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://r-nimble.org
Licenses: Modified BSD GPL 2+
Synopsis: Macros Generating 'nimble' Code
Description:

Macros to generate nimble code from a concise syntax. Included are macros for generating linear modeling code using a formula-based syntax and for building for() loops. For more details review the nimble manual: <https://r-nimble.org/html_manual/cha-writing-models.html#subsec:macros>.

r-npmlecmprsk 3.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NPMLEcmprsk
Licenses: Artistic License 2.0
Synopsis: Type-Specific Failure Rate and Hazard Rate on Competing Risks Data
Description:

Given a failure type, the function computes covariate-specific probability of failure over time and covariate-specific conditional hazard rate based on possibly right-censored competing risk data. Specifically, it computes the non-parametric maximum-likelihood estimates of these quantities and their asymptotic variances in a semi-parametric mixture model for competing-risks data, as described in Chang et al. (2007a).

r-netgreg 0.0.2
Propagated dependencies: r-plsgenomics@1.5-3 r-huge@1.3.5 r-glmnet@4.1-10 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NetGreg
Licenses: GPL 3
Synopsis: Network-Guided Penalized Regression (NetGreg)
Description:

This package provides a network-guided penalized regression framework that integrates network characteristics from Gaussian graphical models with partial penalization, accounting for both network structure (hubs and non-hubs) and clinical covariates in high-dimensional omics data, including transcriptomics and proteomics. The full methodological details can be found in our recent preprint by Ahn S and Oh EJ (2025) <doi:10.48550/arXiv.2505.22986>.

r-nestcolor 0.1.3
Propagated dependencies: r-lifecycle@1.0.4 r-ggplot2@4.0.1 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://insightsengineering.github.io/nestcolor/
Licenses: ASL 2.0
Synopsis: Colors for NEST Graphs
Description:

Clinical reporting figures require to use consistent colors and configurations. As a part of the Roche open-source clinical reporting project, namely the NEST project, the nestcolor package specifies the color code and default theme with specifying ggplot2 theme parameters. Users can easily customize color and theme settings before using the reset of NEST packages to ensure consistent settings in both static and interactive output at the downstream.

r-needs4bigdata 1.0.1
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-rfast@2.1.5.2 r-rdpack@2.6.4 r-psych@2.5.6 r-mvnfast@0.2.8 r-matrixstats@1.5.0 r-ggridges@0.5.7 r-ggplot2@4.0.1 r-ggh4x@0.3.1 r-gam@1.22-6 r-foreach@1.5.2 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/Amalan-ConStat/NeEDS4BigData
Licenses: Expat
Synopsis: New Experimental Design Based Subsampling Methods for Big Data
Description:

Subsampling methods for big data under different models and assumptions. Starting with linear regression and leading to Generalised Linear Models, softmax regression, and quantile regression. Specifically, the model-robust subsampling method proposed in Mahendran, A., Thompson, H., and McGree, J. M. (2023) <doi:10.1007/s00362-023-01446-9>, where multiple models can describe the big data, and the subsampling framework for potentially misspecified Generalised Linear Models in Mahendran, A., Thompson, H., and McGree, J. M. (2025) <doi:10.48550/arXiv.2510.05902>.

r-naflex 0.1.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/dannyparsons/naflex
Licenses: LGPL 3+
Synopsis: Flexible Options for Handling Missing Values
Description:

For use in summary functions to omit missing values conditionally using specified checks.

r-nlcv 0.3.6
Propagated dependencies: r-xtable@1.8-4 r-rocr@1.0-11 r-rcolorbrewer@1.1-3 r-randomforest@4.7-1.2 r-pamr@1.57 r-multtest@2.66.0 r-mlinterfaces@1.90.0 r-mass@7.3-65 r-limma@3.66.0 r-kernlab@0.9-33 r-ipred@0.9-15 r-e1071@1.7-16 r-biobase@2.70.0 r-a4core@1.58.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nlcv
Licenses: GPL 3
Synopsis: Nested Loop Cross Validation
Description:

Nested loop cross validation for classification purposes for misclassification error rate estimation. The package supports several methodologies for feature selection: random forest, Student t-test, limma, and provides an interface to the following classification methods in the MLInterfaces package: linear, quadratic discriminant analyses, random forest, bagging, prediction analysis for microarray, generalized linear model, support vector machine (svm and ksvm). Visualizations to assess the quality of the classifier are included: plot of the ranks of the features, scores plot for a specific classification algorithm and number of features, misclassification rate for the different number of features and classification algorithms tested and ROC plot. For further details about the methodology, please check: Markus Ruschhaupt, Wolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004) <doi:10.2202/1544-6115.1078>.

r-nivm 0.6
Propagated dependencies: r-ssanv@1.1 r-bpcp@1.4.2
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nivm
Licenses: GPL 3+
Synopsis: Noninferiority Tests with Variable Margins
Description:

Noninferiority tests for difference in failure rates at a prespecified control rate or prespecified time. For details, see Fay and Follmann, 2016 <DOI:10.1177/1740774516654861>.

r-normalr 1.0.0
Propagated dependencies: r-shiny@1.11.1 r-rlang@1.1.6 r-purrr@1.2.0 r-mass@7.3-65 r-magrittr@2.0.4
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/kcha193/normalr
Licenses: GPL 2+ GPL 3+
Synopsis: Normalisation of Multiple Variables in Large-Scale Datasets
Description:

The robustness of many of the statistical techniques, such as factor analysis, applied in the social sciences rests upon the assumption of item-level normality. However, when dealing with real data, these assumptions are often not met. The Box-Cox transformation (Box & Cox, 1964) <http://www.jstor.org/stable/2984418> provides an optimal transformation for non-normal variables. Yet, for large datasets of continuous variables, its application in current software programs is cumbersome with analysts having to take several steps to normalise each variable. We present an R package normalr that enables researchers to make convenient optimal transformations of multiple variables in datasets. This R package enables users to quickly and accurately: (1) anchor all of their variables at 1.00, (2) select the desired precision with which the optimal lambda is estimated, (3) apply each unique exponent to its variable, (4) rescale resultant values to within their original X1 and X(n) ranges, and (5) provide original and transformed estimates of skewness, kurtosis, and other inferential assessments of normality.

r-nhsrwaitinglist 0.1.2
Propagated dependencies: r-rlang@1.1.6 r-randomnames@1.6-0.0 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://nhs-r-community.github.io/NHSRwaitinglist/
Licenses: Expat
Synopsis: Waiting List Metrics Using Queuing Theory
Description:

Waiting list management using queuing theory to analyse, predict and manage queues, based on the approach described in Fong et al. (2022) <doi:10.1101/2022.08.23.22279117>. Aimed at UK National Health Service (NHS) applications, waiting list summary statistics, target-value calculations, waiting list simulation, and scheduling functions are included.

r-nirstat 1.1
Propagated dependencies: r-mgcv@1.9-4 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=NIRStat
Licenses: GPL 2
Synopsis: Novel Statistical Methods for Studying Near-Infrared Spectroscopy (NIRS) Time Series Data
Description:

This package provides transfusion-related differential tests on Near-infrared spectroscopy (NIRS) time series with detection limit, which contains two testing statistics: Mean Area Under the Curve (MAUC) and slope statistic. This package applied a penalized spline method within imputation setting. Testing is conducted by a nested permutation approach within imputation. Refer to Guo et al (2018) <doi:10.1177/0962280218786302> for further details.

r-nlmrt 2016.3.2
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nlmrt
Licenses: GPL 2
Synopsis: Functions for Nonlinear Least Squares Solutions
Description:

Replacement for nls() tools for working with nonlinear least squares problems. The calling structure is similar to, but much simpler than, that of the nls() function. Moreover, where nls() specifically does NOT deal with small or zero residual problems, nlmrt is quite happy to solve them. It also attempts to be more robust in finding solutions, thereby avoiding singular gradient messages that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach in nlmrt generally works more reliably to get a solution, though this may be one of a set of possibilities, and may also be statistically unsatisfactory. Added print and summary as of August 28, 2012.

r-networkr 0.1.5
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrix@1.7-4 r-fastmatch@1.1-6 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=networkR
Licenses: GPL 2+
Synopsis: Network Analysis and Visualization
Description:

Collection of functions for fast manipulation, handling, and analysis of large-scale networks based on family and social data. Functions are utility functions used to manipulate data in three "formats": sparse adjacency matrices, pedigree trio family data, and pedigree family data. When possible, the functions should be able to handle millions of data points quickly for use in combination with data from large public national registers and databases. Kenneth Lange (2003, ISBN:978-8181281135).

r-netdiffuser 1.24.0
Propagated dependencies: r-viridislite@0.4.2 r-sparsem@1.84-2 r-sna@2.8 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-networkdynamic@0.11.5 r-network@1.19.0 r-matrix@1.7-4 r-matchit@4.7.2 r-mass@7.3-65 r-igraph@2.2.1 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/USCCANA/netdiffuseR
Licenses: Expat
Synopsis: Analysis of Diffusion and Contagion Processes on Networks
Description:

Empirical statistical analysis, visualization and simulation of diffusion and contagion processes on networks. The package implements algorithms for calculating network diffusion statistics such as transmission rate, hazard rates, exposure models, network threshold levels, infectiousness (contagion), and susceptibility. The package is inspired by work published in Valente, et al., (2015) <DOI:10.1016/j.socscimed.2015.10.001>; Valente (1995) <ISBN: 9781881303213>, Myers (2000) <DOI:10.1086/303110>, Iyengar and others (2011) <DOI:10.1287/mksc.1100.0566>, Burt (1987) <DOI:10.1086/228667>; among others.

r-nltm 1.4.6
Propagated dependencies: r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/mclements/nltm
Licenses: GPL 2
Synopsis: Non-Linear Transformation Models
Description:

Fits a non-linear transformation model ('nltm') for analyzing survival data, see Tsodikov (2003) <doi:10.1111/1467-9868.00414>. The class of nltm includes the following currently supported models: Cox proportional hazard, proportional hazard cure, proportional odds, proportional hazard - proportional hazard cure, proportional hazard - proportional odds cure, Gamma frailty, and proportional hazard - proportional odds.

r-nst 3.1.10
Propagated dependencies: r-vegan@2.7-2 r-permute@0.9-8 r-icamp@1.5.12 r-dirichletreg@0.7-2 r-bigmemory@4.6.4 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/DaliangNing/NST
Licenses: GPL 2
Synopsis: Normalized Stochasticity Ratio
Description:

To estimate ecological stochasticity in community assembly. Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. The new index, normalized stochasticity ratio (NST), is to estimate ecological stochasticity, i.e. relative importance of stochastic processes, in community assembly. With functions in this package, NST can be calculated based on different similarity metrics and/or different null model algorithms, as well as some previous indexes, e.g. previous Stochasticity Ratio (ST), Standard Effect Size (SES), modified Raup-Crick metrics (RC). Functions for permutational test and bootstrapping analysis are also included. Previous ST is published by Zhou et al (2014) <doi:10.1073/pnas.1324044111>. NST is modified from ST by considering two alternative situations and normalizing the index to range from 0 to 1 (Ning et al 2019) <doi:10.1073/pnas.1904623116>. A modified version, MST, is a special case of NST, used in some recent or upcoming publications, e.g. Liang et al (2020) <doi:10.1016/j.soilbio.2020.108023>. SES is calculated as described in Kraft et al (2011) <doi:10.1126/science.1208584>. RC is calculated as reported by Chase et al (2011) <doi:10.1890/ES10-00117.1> and Stegen et al (2013) <doi:10.1038/ismej.2013.93>. Version 3 added NST based on phylogenetic beta diversity, used by Ning et al (2020) <doi:10.1038/s41467-020-18560-z>.

r-ndl 0.2.18
Propagated dependencies: r-rcpp@1.1.0 r-mass@7.3-65 r-hmisc@5.2-4
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=ndl
Licenses: GPL 3
Synopsis: Naive Discriminative Learning
Description:

Naive discriminative learning implements learning and classification models based on the Rescorla-Wagner equations and their equilibrium equations.

r-nppbib 1.2-0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/dalling1
Licenses: GPL 3
Synopsis: Nonparametric Partially-Balanced Incomplete Block Design Analysis
Description:

This package implements a nonparametric statistical test for rank or score data from partially-balanced incomplete block-design experiments.

r-nmrphasing 1.0.7
Propagated dependencies: r-signal@1.8-1 r-massspecwavelet@1.76.0 r-baseline@1.3-7
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NMRphasing
Licenses: Expat
Synopsis: Phase Error Correction and Baseline Correction for One Dimensional ('1D') 'NMR' Data
Description:

There are three distinct approaches for phase error correction, they are: a single linear model with a choice of optimization functions, multiple linear models with optimization function choices and a shrinkage-based method. The methodology is based on our new algorithms and various references (Binczyk et al. (2015) <doi:10.1186/1475-925X-14-S2-S5>,Chen et al. (2002) <doi:10.1016/S1090-7807(02)00069-1>, de Brouwer (2009) <doi:10.1016/j.jmr.2009.09.017>, Džakula (2000) <doi:10.1006/jmre.2000.2123>, Ernst (1969) <doi:10.1016/0022-2364(69)90003-1>, Liland et al. (2010) <doi:10.1366/000370210792434350>).

r-nullcat 0.1.0
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/matthewkling/nullcat
Licenses: Expat
Synopsis: Null Models for Categorical and Continuous Community Matrices
Description:

This package provides null model algorithms for categorical and quantitative community ecology data. Extends classic binary null models (e.g., curveball', swap') to work with categorical data. Provides a stratified randomization framework for continuous data.

r-noaa 1.0.0
Propagated dependencies: r-jsonlite@2.0.0 r-httr@1.4.7
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=noaa
Licenses: GPL 3
Synopsis: Accessing NOAA Climate Data Online
Description:

Fetch data from the National Oceanic and Atmospheric Administration Climate Data Online (NOAA CDO) <https://www.ncdc.noaa.gov/cdo-web/webservices/v2> API including daily, monthly, and yearly climate summaries, radar data, climatological averages, precipitation data, annual summaries, storm events, and agricultural meteorology.

r-norstr 0.2.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/magnusdv/norSTR
Licenses: Expat
Synopsis: Allele Frequencies for 50 Forensic STR Markers
Description:

Allele frequency databases for 50 forensic short tandem repeat (STR) markers, covering Norway and several broader regional populations: Europe, Africa, South America, West Asia, Middle Asia, and East Asia. Developed and maintained for use at the Department of Forensic Sciences, Oslo, Norway.

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