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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-nycflights23 0.2.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://moderndive.github.io/nycflights23/
Licenses: CC0
Build system: r
Synopsis: Flights and Other Useful Metadata for NYC Outbound Flights in 2023
Description:

Updating the now 10-year-old nycflights13 data package. It contains information about all flights that departed from the three main New York City airports in 2023 and metadata on airlines, airports, weather, and planes.

r-nsp 1.0.0
Propagated dependencies: r-lpsolve@5.6.23
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nsp
Licenses: GPL 3+
Build system: r
Synopsis: Inference for Multiple Change-Points in Linear Models
Description:

Implementation of Narrowest Significance Pursuit, a general and flexible methodology for automatically detecting localised regions in data sequences which each must contain a change-point (understood as an abrupt change in the parameters of an underlying linear model), at a prescribed global significance level. Narrowest Significance Pursuit works with a wide range of distributional assumptions on the errors, and yields exact desired finite-sample coverage probabilities, regardless of the form or number of the covariates. For details, see P. Fryzlewicz (2021) <https://stats.lse.ac.uk/fryzlewicz/nsp/nsp.pdf>.

r-netint 1.0.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NetInt
Licenses: GPL 2+
Build system: r
Synopsis: Methods for Unweighted and Weighted Network Integration
Description:

Implementation of network integration approaches comprising unweighted and weighted integration methods. Unweighted integration is performed considering the average, per-edge average, maximum and minimum of networks edges. Weighted integration takes into account a weight for each network during the fusion process, where the weights express the predictiveness strength of each network considering a specific predictive task. Weights can be learned using a machine learning algorithm able to associate the weights to the assessment of the accuracy of the learning algorithm trained on the network itself. The implemented methods can be applied to effectively integrate different biological networks modelling a wide range of problems in bioinformatics (e.g. disease gene prioritization, protein function prediction, drug repurposing, clinical outcome prediction).

r-nbconv 1.0.1
Propagated dependencies: r-matrixstats@1.5.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/gbedwell/nbconv
Licenses: GPL 3+
Build system: r
Synopsis: Evaluate Arbitrary Negative Binomial Convolutions
Description:

Three distinct methods are implemented for evaluating the sums of arbitrary negative binomial distributions. These methods are: Furman's exact probability mass function (Furman (2007) <doi:10.1016/j.spl.2006.06.007>), saddlepoint approximation, and a method of moments approximation. Functions are provided to calculate the density function, the distribution function and the quantile function of the convolutions in question given said evaluation methods. Functions for generating random deviates from negative binomial convolutions and for directly calculating the mean, variance, skewness, and excess kurtosis of said convolutions are also provided.

r-negligible 0.1.11
Propagated dependencies: r-wrs2@1.1-7 r-rockchalk@1.8.157 r-nptest@1.2 r-mbess@4.9.41 r-lavaan@0.6-20 r-ggplot2@4.0.1 r-effectsize@1.0.1 r-e1071@1.7-16 r-dplyr@1.1.4 r-desctools@0.99.60
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=negligible
Licenses: GPL 3
Build system: r
Synopsis: Collection of Functions for Negligible Effect/Equivalence Testing
Description:

Researchers often want to evaluate whether there is a negligible relationship among variables. The negligible package provides functions that are useful for conducting negligible effect testing (also called equivalence testing). For example, there are functions for evaluating the equivalence of means or the presence of a negligible association (correlation or regression). Beribisky, N., Mara, C., & Cribbie, R. A. (2020) <doi:10.20982/tqmp.16.4.p424>. Beribisky, N., Davidson, H., Cribbie, R. A. (2019) <doi:10.7717/peerj.6853>. Shiskina, T., Farmus, L., & Cribbie, R. A. (2018) <doi:10.20982/tqmp.14.3.p167>. Mara, C. & Cribbie, R. A. (2017) <doi:10.1080/00220973.2017.1301356>. Counsell, A. & Cribbie, R. A. (2015) <doi:10.1111/bmsp.12045>. van Wieringen, K. & Cribbie, R. A. (2014) <doi:10.1111/bmsp.12015>. Goertzen, J. R. & Cribbie, R. A. (2010) <doi:10.1348/000711009x475853>. Cribbie, R. A., Gruman, J. & Arpin-Cribbie, C. (2004) <doi:10.1002/jclp.10217>.

r-netassoc 0.7.2
Propagated dependencies: r-vegan@2.7-2 r-infotheo@1.2.0.1 r-igraph@2.2.1 r-corpcor@1.6.10
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=netassoc
Licenses: GPL 3
Build system: r
Synopsis: Inference of Species Associations from Co-Occurrence Data
Description:

This package infers species associations from community matrices. Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.

r-ntlkwiex 0.1.0
Propagated dependencies: r-ggplot2@4.0.1 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=NTLKwIEx
Licenses: GPL 2
Build system: r
Synopsis: Computation of NTLKwIEx Distribution Properties
Description:

This package implements statistical tools for analyzing, simulating, and computing properties of the New Topp-Leone Kumaraswamy Inverse Exponential (NTLKwIEx) distribution. See Atchadé M, Otodji T, and Djibril A (2024) <doi:10.1063/5.0179458> and Atchadé M, Otodji T, Djibril A, and N'bouké M (2023) <doi:10.1515/phys-2023-0151> for details.

r-naive 1.2.3
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://rpubs.com/giancarlo_vercellino/naive
Licenses: GPL 3
Build system: r
Synopsis: Empirical Extrapolation of Time Feature Patterns
Description:

An application for the empirical extrapolation of time features selecting and summarizing the most relevant patterns in time sequences.

r-nimbleapt 1.0.7
Propagated dependencies: r-nimble@1.4.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/DRJP/nimbleAPT
Licenses: Modified BSD
Build system: r
Synopsis: Adaptive Parallel Tempering for 'NIMBLE'
Description:

This package provides functions for adaptive parallel tempering (APT) with NIMBLE models. Adapted from Lacki & Miasojedow (2016) <DOI:10.1007/s11222-015-9579-0> and Miasojedow, Moulines and Vihola (2013) <DOI:10.1080/10618600.2013.778779>.

r-nettskjemar 1.0.4
Propagated dependencies: r-jsonlite@2.0.0 r-httr2@1.2.1 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/CAPRO-UiO/nettskjemar
Licenses: Expat
Build system: r
Synopsis: Connect to the 'nettskjema.no' API of the University of Oslo
Description:

Enables users to retrieve data, meta-data, and codebooks from <https://nettskjema.no/>. The data from the API is richer than from the online data portal. This package is not developed by the University of Oslo IT. Mowinckel (2021) <doi:10.5281/zenodo.4745481>.

r-naaccr 3.1.1
Propagated dependencies: r-xml@3.99-0.20 r-stringi@1.8.7 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/WerthPADOH/naaccr
Licenses: Expat
Build system: r
Synopsis: Read Cancer Records in the NAACCR Format
Description:

This package provides functions for reading cancer record files which follow a format defined by the North American Association of Central Cancer Registries (NAACCR).

r-npsurvss 1.1.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/godwinyyung/npsurvSS
Licenses: GPL 2
Build system: r
Synopsis: Sample Size and Power Calculation for Common Non-Parametric Tests in Survival Analysis
Description:

This package provides a number of statistical tests have been proposed to compare two survival curves, including the difference in (or ratio of) t-year survival, difference in (or ratio of) p-th percentile survival, difference in (or ratio of) restricted mean survival time, and the weighted log-rank test. Despite the multitude of options, the convention in survival studies is to assume proportional hazards and to use the unweighted log-rank test for design and analysis. This package provides sample size and power calculation for all of the above statistical tests with allowance for flexible accrual, censoring, and survival (eg. Weibull, piecewise-exponential, mixture cure). It is the companion R package to the paper by Yung and Liu (2020) <doi:10.1111/biom.13196>. Specific to the weighted log-rank test, users may specify which approximations they wish to use to estimate the large-sample mean and variance. The default option has been shown to provide substantial improvement over the conventional sample size and power equations based on Schoenfeld (1981) <doi:10.1093/biomet/68.1.316>.

r-netdiffuser 1.24.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/USCCANA/netdiffuseR
Licenses: Expat
Build system: r
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-nice 0.4-2
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nice
Licenses: Expat
Build system: r
Synopsis: Get or Set UNIX Niceness
Description:

Get or set UNIX priority (niceness) of running R process.

r-netmeta 3.3-1
Propagated dependencies: r-mvtnorm@1.3-3 r-metafor@4.8-0 r-meta@8.2-1 r-matrix@1.7-4 r-mass@7.3-65 r-magrittr@2.0.4 r-magic@1.6-1 r-igraph@2.2.1 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://github.com/guido-s/netmeta
Licenses: GPL 2+ FSDG-compatible
Build system: r
Synopsis: Network Meta-Analysis using Frequentist Methods
Description:

This package provides a comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) <doi:10.18637/jss.v106.i02> and supporting Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) <doi:10.1002/jrsm.1058>; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <doi:10.1002/bimj.201800167>; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <doi:10.1002/sim.8158>, or penalised logistic regression (Evrenoglou et al., 2022) <doi:10.1002/sim.9562>; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) <doi:10.1016/j.jclinepi.2010.03.016>; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) <doi:10.1186/s12874-015-0060-8>; - split direct and indirect evidence to check consistency (Dias et al., 2010) <doi:10.1002/sim.3767>, (Efthimiou et al., 2019) <doi:10.1002/sim.8158>; - league table with network meta-analysis results; - comparison-adjusted funnel plot (Chaimani & Salanti, 2012) <doi:10.1002/jrsm.57>; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <doi:10.1186/1471-2288-13-35>; - measures characterizing the flow of evidence between two treatments by König et al. (2013) <doi:10.1002/sim.6001>; - automated drawing of network graphs described in Rücker & Schwarzer (2016) <doi:10.1002/jrsm.1143>; - partial order of treatment rankings ('poset') and Hasse diagram for poset (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <doi:10.1002/jrsm.1270>; - contribution matrix as described in Papakonstantinou et al. (2018) <doi:10.12688/f1000research.14770.3> and Davies et al. (2022) <doi:10.1002/sim.9346>; - network meta-regression with a single continuous or binary covariate; - subgroup network meta-analysis.

r-nonlineardotplot 0.5.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nonLinearDotPlot
Licenses: Expat
Build system: r
Synopsis: Non Linear Dot Plots
Description:

Non linear dot plots are diagrams that allow dots of varying size to be constructed, so that columns with a large number of samples are reduced in height. Implementation of algorithm described in: Nils Rodrigues and Daniel Weiskopf, "Nonlinear Dot Plots", IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 616-625, 2018. <doi:10.1109/TVCG.2017.2744018>.

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
Build system: r
Synopsis: Natural Language Processing Utilities
Description:

Utilities for Natural Language Processing.

r-npboottprm 0.3.2
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/mightymetrika/npboottprm
Licenses: Expat
Build system: r
Synopsis: Nonparametric Bootstrap Test with Pooled Resampling
Description:

Addressing crucial research questions often necessitates a small sample size due to factors such as distinctive target populations, rarity of the event under study, time and cost constraints, ethical concerns, or group-level unit of analysis. Many readily available analytic methods, however, do not accommodate small sample sizes, and the choice of the best method can be unclear. The npboottprm package enables the execution of nonparametric bootstrap tests with pooled resampling to help fill this gap. Grounded in the statistical methods for small sample size studies detailed in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, the package facilitates a range of statistical tests, encompassing independent t-tests, paired t-tests, and one-way Analysis of Variance (ANOVA) F-tests. The nonparboot() function undertakes essential computations, yielding detailed outputs which include test statistics, effect sizes, confidence intervals, and bootstrap distributions. Further, npboottprm incorporates an interactive shiny web application, nonparboot_app(), offering intuitive, user-friendly data exploration.

r-networkinference 1.2.5
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NetworkInference
Licenses: Expat
Build system: r
Synopsis: Inferring Latent Diffusion Networks
Description:

This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010)<doi:10.1145/1835804.1835933>. Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process.

r-npred 1.1.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/zejiang-unsw/NPRED#readme
Licenses: GPL 3
Build system: r
Synopsis: Predictor Identifier: Nonparametric Prediction
Description:

Partial informational correlation (PIC) is used to identify the meaningful predictors to the response from a large set of potential predictors. Details of methodologies used in the package can be found in Sharma, A., Mehrotra, R. (2014). <doi:10.1002/2013WR013845>, Sharma, A., Mehrotra, R., Li, J., & Jha, S. (2016). <doi:10.1016/j.envsoft.2016.05.021>, and Mehrotra, R., & Sharma, A. (2006). <doi:10.1016/j.advwatres.2005.08.007>.

r-neonutilities 3.0.3
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/NEONScience/NEON-utilities
Licenses: AGPL 3
Build system: r
Synopsis: Utilities for Working with NEON Data
Description:

NEON data packages can be accessed through the NEON Data Portal <https://www.neonscience.org> or through the NEON Data API (see <https://data.neonscience.org/data-api> for documentation). Data delivered from the Data Portal are provided as monthly zip files packaged within a parent zip file, while individual files can be accessed from the API. This package provides tools that aid in discovering, downloading, and reformatting data prior to use in analyses. This includes downloading data via the API, merging data tables by type, and converting formats. For more information, see the readme file at <https://github.com/NEONScience/NEON-utilities>.

r-nitrogenuptake2016 0.2.3
Propagated dependencies: r-zoo@1.8-14 r-mass@7.3-65 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/troyhill/NitrogenUptake2016
Licenses: GPL 3
Build system: r
Synopsis: Data and Source Code From: Nitrogen Uptake and Allocation Estimates for Spartina Alterniflora and Distichlis Spicata
Description:

This package contains data, code, and figures from Hill et al. 2018a (Journal of Experimental Marine Biology and Ecology; <DOI: 10.1016/j.jembe.2018.07.006>) and Hill et al. 2018b (Data In Brief <DOI: 10.1016/j.dib.2018.09.133>). Datasets document plant allometry, stem heights, nutrient and stable isotope content, and sediment denitrification enzyme assays. The data and analysis offer an examination of nitrogen uptake and allocation in two salt marsh plant species.

r-npcurepk 1.0-2
Propagated dependencies: r-npcure@0.1-5 r-foreach@1.5.2 r-doparallel@1.0.17 r-desctools@0.99.60 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=npcurePK
Licenses: GPL 2+
Build system: r
Synopsis: Mixture Cure Model Estimation with Cure Status Partially Known
Description:

This package performs nonparametric estimation in mixture cure models when the cure status is partially known. For details, see Safari et al (2021) <doi:10.1002/bimj.202100156>, Safari et al (2022) <doi:10.1177/09622802221115880> and Safari et al (2023) <doi:10.1007/s10985-023-09591-x>.

r-netdose 0.7-4
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/petropouloumaria/netdose
Licenses: GPL 2+
Build system: r
Synopsis: Dose-Response Network Meta-Analysis in a Frequentist Way
Description:

This package provides a set of functions providing the implementation of the network meta-analysis model with dose-response relationships, predicted values of the fitted model and dose-response plots in a frequentist way.

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