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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-neighboot 1.0.1
Propagated dependencies: r-rdstreeboot@1.0 r-rds@0.9-10 r-igraph@2.2.1 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=Neighboot
Licenses: GPL 3
Synopsis: Neighborhood Bootstrap Method for RDS
Description:

This package provides a bootstrap method for Respondent-Driven Sampling (RDS) that relies on the underlying structure of the RDS network to estimate uncertainty.

r-nutrinetwork 0.2.0
Propagated dependencies: r-tmvtnorm@1.7 r-matrix@1.7-4 r-igraph@2.2.1 r-huge@1.3.5 r-glasso@1.11
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nutriNetwork
Licenses: GPL 3
Synopsis: Structure Learning with Copula Graphical Model
Description:

Statistical tool for learning the structure of direct associations among variables for continuous data, discrete data and mixed discrete-continuous data. The package is based on the copula graphical model in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287>.

r-nn2poly 0.1.3
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pracma@2.4.6 r-matrixstats@1.5.0 r-generics@0.1.4
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://ibidat.github.io/nn2poly/
Licenses: Expat
Synopsis: Neural Network Weights Transformation into Polynomial Coefficients
Description:

This package implements a method that builds the coefficients of a polynomial model that performs almost equivalently as a given neural network (densely connected). This is achieved using Taylor expansion at the activation functions. The obtained polynomial coefficients can be used to explain features (and their interactions) importance in the neural network, therefore working as a tool for interpretability or eXplainable Artificial Intelligence (XAI). See Morala et al. 2021 <doi:10.1016/j.neunet.2021.04.036>, and 2023 <doi:10.1109/TNNLS.2023.3330328>.

r-nbody 1.41
Propagated dependencies: r-rcpp@1.1.0 r-magicaxis@2.5.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nbody
Licenses: GPL 3
Synopsis: Gravitational N-Body Simulation
Description:

Run simple direct gravitational N-body simulations. The package can access different external N-body simulators (e.g. GADGET-4 by Springel et al. (2021) <doi:10.48550/arXiv.2010.03567>), but also has a simple built-in simulator. This default simulator uses a variable block time step and lets the user choose between a range of integrators, including 4th and 6th order integrators for high-accuracy simulations. Basic top-hat smoothing is available as an option. The code also allows the definition of background particles that are fixed or in uniform motion, not subject to acceleration by other particles.

r-nosleepr 0.2.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/hetalang/NoSleepR
Licenses: Expat
Synopsis: Prevent System Sleep During Long R Tasks
Description:

This package provides a cross-platform interface to prevent the operating system from going to sleep while long-running R tasks are executing.

r-nprobust 0.5.0
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 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=nprobust
Licenses: GPL 2
Synopsis: Nonparametric Robust Estimation and Inference Methods using Local Polynomial Regression and Kernel Density Estimation
Description:

This package provides tools for data-driven statistical analysis using local polynomial regression and kernel density estimation methods as described in Calonico, Cattaneo and Farrell (2018, <doi:10.1080/01621459.2017.1285776>): lprobust() for local polynomial point estimation and robust bias-corrected inference, lpbwselect() for local polynomial bandwidth selection, kdrobust() for kernel density point estimation and robust bias-corrected inference, kdbwselect() for kernel density bandwidth selection, and nprobust.plot() for plotting results. The main methodological and numerical features of this package are described in Calonico, Cattaneo and Farrell (2019, <doi:10.18637/jss.v091.i08>).

r-nnmis 1.0.1
Propagated dependencies: r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NNMIS
Licenses: LGPL 2.0+
Synopsis: Nearest Neighbor Based Multiple Imputation for Survival Data with Missing Covariates
Description:

Imputation for both missing covariates and censored observations (optional) for survival data with missing covariates by the nearest neighbor based multiple imputation algorithm as described in Hsu et al. (2006) <doi:10.1002/sim.2452>, and Hsu and Yu (2018) <doi: 10.1177/0962280218772592>. Note that the current version can only impute for a situation with one missing covariate.

r-nswgeo 0.5.0
Propagated dependencies: r-sf@1.0-23 r-cartographer@0.2.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/cidm-ph/nswgeo
Licenses: Expat
Synopsis: Geospatial Data and Maps for New South Wales, Australia
Description:

Geospatial data for creating maps of New South Wales (NSW), Australia, and some helpers to work with common problems like normalising postcodes. Registers its data with cartographer'.

r-ncsampling 1.0
Propagated dependencies: r-yaimpute@1.0-34.1 r-randomforest@4.7-1.2 r-lattice@0.22-7
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NCSampling
Licenses: GPL 2
Synopsis: Nearest Centroid (NC) Sampling
Description:

This package provides functionality for performing Nearest Centroid (NC) Sampling. The NC sampling procedure was developed for forestry applications and selects plots for ground measurement so as to maximize the efficiency of imputation estimates. It uses multiple auxiliary variables and multivariate clustering to search for an optimal sample. Further details are given in Melville G. & Stone C. (2016) <doi:10.1080/00049158.2016.1218265>.

r-nlist 0.4.0
Propagated dependencies: r-universals@0.0.5 r-tibble@3.3.0 r-term@0.3.6 r-rlang@1.1.6 r-purrr@1.2.0 r-lifecycle@1.0.4 r-generics@0.1.4 r-extras@0.8.0 r-coda@0.19-4.1 r-chk@0.10.0 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/poissonconsulting/nlist
Licenses: Expat
Synopsis: Lists of Numeric Atomic Objects
Description:

Create and manipulate numeric list ('nlist') objects. An nlist is an S3 list of uniquely named numeric objects. An numeric object is an integer or double vector, matrix or array. An nlists object is a S3 class list of nlist objects with the same names, dimensionalities and typeofs. Numeric list objects are of interest because they are the raw data inputs for analytic engines such as JAGS', STAN and TMB'. Numeric lists objects, which are useful for storing multiple realizations of of simulated data sets, can be converted to coda::mcmc and coda::mcmc.list objects.

r-nomisdata 0.1.1
Propagated dependencies: 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 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/cherylisabella/nomisdata
Licenses: Expat
Synopsis: Access 'Nomis' UK Labour Market Data and Statistics
Description:

Interface to the Nomis database (<https://www.nomisweb.co.uk>), a comprehensive resource of United Kingdom labour market statistics provided by the Office for National Statistics (ONS). Facilitates programmatic access to census data, labour force surveys, benefit statistics, and socioeconomic indicators through a modern HTTP client with intelligent caching, automatic query pagination, and tidy data principles. Includes spatial data integration, interactive helpers, and visualization utilities. Independent implementation unaffiliated with ONS or Durham University.

r-nls2 0.3-4
Propagated dependencies: r-proto@1.0.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/ggrothendieck/nls2
Licenses: GPL 2
Synopsis: Non-Linear Regression with Brute Force
Description:

Adds brute force and multiple starting values to nls.

r-nlputils 0.0-5.1
Propagated dependencies: r-snowballc@0.7.1 r-qdap@2.4.6.1 r-nlp@0.3-2
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NLPutils
Licenses: GPL 2
Synopsis: Natural Language Processing Utilities
Description:

Utilities for Natural Language Processing.

r-nimblehmc 0.2.4
Propagated dependencies: r-nimble@1.4.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nimbleHMC
Licenses: Modified BSD GPL 2+
Synopsis: Hamiltonian Monte Carlo and Other Gradient-Based MCMC Sampling Algorithms for 'nimble'
Description:

This package provides gradient-based MCMC sampling algorithms for use with the MCMC engine provided by the nimble package. This includes two versions of Hamiltonian Monte Carlo (HMC) No-U-Turn (NUTS) sampling, and (under development) Langevin samplers. The `NUTS_classic` sampler implements the original HMC-NUTS algorithm as described in Hoffman and Gelman (2014) <doi:10.48550/arXiv.1111.4246>. The `NUTS` sampler is a modern version of HMC-NUTS sampling matching the HMC sampler available in version 2.32.2 of Stan (Stan Development Team, 2023). In addition, convenience functions are provided for generating and modifying MCMC configuration objects which employ HMC sampling. Functionality of the nimbleHMC package is described further in Turek, et al (2024) <doi: 10.21105/joss.06745>.

r-nngarrote 1.0.4
Propagated dependencies: r-glmnet@4.1-10
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nnGarrote
Licenses: GPL 2+
Synopsis: Non-Negative Garrote Estimation with Penalized Initial Estimators
Description:

This package provides functions to compute the non-negative garrote estimator as proposed by Breiman (1995) <https://www.jstor.org/stable/1269730> with the penalized initial estimators extension as proposed by Yuan and Lin (2007) <https://www.jstor.org/stable/4623260>.

r-nrlr 0.1.2
Propagated dependencies: r-xml2@1.5.0 r-tibble@3.3.0 r-stringr@1.6.0 r-rvest@1.0.5 r-purrr@1.2.0 r-lubridate@1.9.4 r-jsonlite@2.0.0 r-httr@1.4.7 r-glue@1.8.0 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://github.com/DanielTomaro13/nrlR
Licenses: Expat
Synopsis: Functions to Scrape Rugby Data
Description:

This package provides a set of functions to scrape and analyze rugby data. Supports competitions including the National Rugby League, New South Wales Cup, Queensland Cup, Super League, and various representative and women's competitions. Includes functions to fetch player statistics, match results, ladders, venues, and coaching data. Designed to assist analysts, fans, and researchers in exploring historical and current rugby league data. See Woods et al. (2017) <doi:10.1123/ijspp.2016-0187> for an example of rugby league performance analysis methodology.

r-ncar 0.5.0
Propagated dependencies: r-rtf@0.4-14.1 r-noncompart@0.7.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=ncar
Licenses: GPL 3
Synopsis: Noncompartmental Analysis for Pharmacokinetic Report
Description:

Conduct a noncompartmental analysis with industrial strength. Some features are 1) CDISC SDTM terms 2) Automatic or manual slope selection 3) Supporting both linear-up linear-down and linear-up log-down method 4) Interval(partial) AUCs with linear or log interpolation method 5) Produce pdf, rtf, text report files. * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).

r-newimvc 0.1.0
Propagated dependencies: r-quantreg@6.1 r-limma@3.66.0 r-ggmridge@1.5 r-expm@1.0-0 r-compquadform@1.4.4
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=newIMVC
Licenses: GPL 3
Synopsis: Robust Integrated Mean Variance Correlation
Description:

Measure the dependence structure between two random variables with a new correlation coefficient and extend it to hypothesis test, feature screening and false discovery rate control.

r-nonmem2rx 0.1.9
Propagated dependencies: r-xml2@1.5.0 r-rxode2@5.0.1 r-rcpp@1.1.0 r-qs2@0.1.6 r-magrittr@2.0.4 r-lotri@1.0.2 r-ggplot2@4.0.1 r-ggforce@0.5.0 r-dparser@1.3.1-13 r-digest@0.6.39 r-data-table@1.17.8 r-crayon@1.5.3 r-cli@3.6.5 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://nlmixr2.github.io/nonmem2rx/
Licenses: GPL 3+
Synopsis: Converts 'NONMEM' Models to 'rxode2'
Description:

NONMEM has been a tool for running nonlinear mixed effects models since the 80s and is still used today (Bauer 2019 <doi:10.1002/psp4.12404>). This tool allows you to convert NONMEM models to rxode2 (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) and with simple models nlmixr2 syntax (Fidler et al (2019) <doi:10.1002/psp4.12445>). The nlmixr2 syntax requires the residual specification to be included and it is not always translated. If available, the rxode2 model will read in the NONMEM data and compare the simulation for the population model ('PRED') individual model ('IPRED') and residual model ('IWRES') to immediately show how well the translation is performing. This saves the model development time for people who are creating an rxode2 model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a rxode2 model. This is complementary to the babelmixr2 package that translates nlmixr2 models to NONMEM and can convert the objects converted from nonmem2rx to a full nlmixr2 fit.

r-nixmass 1.3.1
Propagated dependencies: r-zoo@1.8-14 r-tidyr@1.3.1 r-lubridate@1.9.4 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-colorspace@2.1-2
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://haraldschellander.github.io/nixmass/
Licenses: GPL 3
Synopsis: Snow Water Equivalent Modeling with the 'Delta.snow' and 'HS2SWE' Models and Empirical Regression Models
Description:

Snow water equivalent is modeled with the process based models delta.snow and HS2SWE and empirical regression, which use relationships between density and diverse at-site parameters. The methods are described in Winkler et al. (2021) <doi:10.5194/hess-25-1165-2021>, Magnusson et al. (2025) <doi:10.1016/j.coldregions.2025.104435>, Guyennon et al. (2019) <doi:10.1016/j.coldregions.2019.102859>, Pistocchi (2016) <doi:10.1016/j.ejrh.2016.03.004>, Jonas et al. (2009) <doi:10.1016/j.jhydrol.2009.09.021> and Sturm et al. (2010) <doi:10.1175/2010JHM1202.1>.

r-nseq 0.1.1
Propagated dependencies: r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://rfsaldanha.github.io/nseq/
Licenses: Expat
Synopsis: Count of Sequential Events
Description:

Count the occurrence of sequences of values in a vector that meets certain conditions of length and magnitude. The method is based on the Run Length Encoding algorithm, available with base R, inspired by A. H. Robinson and C. Cherry (1967) <doi:10.1109/PROC.1967.5493>.

r-netcoin 2.1.9
Propagated dependencies: r-rd3plot@1.1.37 r-matrix@1.7-4 r-mass@7.3-65 r-igraph@2.2.1 r-haven@2.5.5 r-gparotation@2025.3-1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://modesto-escobar.github.io/netCoin-2.x/
Licenses: GPL 2 GPL 3
Synopsis: Interactive Analytic Networks
Description:

Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of JavaScript in one package.

r-ntranova 0.0.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=ntranova
Licenses: GPL 3
Synopsis: Two Way Neutrosophic ANOVA
Description:

Dealing with neutrosophic data of the form N=D+I(where N is a Neutrosophic number ,D is the determinant part of the number and I is the indeterminacy part) using the neutrosophic two way anova test keeps the type I error low. This algorithm calculates the fisher statistics when we have a neutrosophic data, also tests two hypothesizes, first is to test differences between treatments, and second is to test differences between sectors. For more information see Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022) <https://www.americaspg.com/articleinfo/21/show/1058>.

r-npreg 1.1.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=npreg
Licenses: GPL 2+
Synopsis: Nonparametric Regression via Smoothing Splines
Description:

Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors (including random intercepts), interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for diagnostics, inference, and prediction.

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