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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-neuralsens 1.1.3
Propagated dependencies: r-stringr@1.6.0 r-scales@1.4.0 r-reshape2@1.4.5 r-neuralnettools@1.5.3 r-magrittr@2.0.4 r-hmisc@5.2-4 r-gridextra@2.3 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-ggnewscale@0.5.2 r-ggforce@0.5.0 r-ggbreak@0.1.6 r-fastdummies@1.7.5 r-dplyr@1.1.4 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/JaiPizGon/NeuralSens
Licenses: GPL 2+
Build system: r
Synopsis: Sensitivity Analysis of Neural Networks
Description:

Analysis functions to quantify inputs importance in neural network models. Functions are available for calculating and plotting the inputs importance and obtaining the activation function of each neuron layer and its derivatives. The importance of a given input is defined as the distribution of the derivatives of the output with respect to that input in each training data point <doi:10.18637/jss.v102.i07>.

r-ncf 1.3-3
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://ento.psu.edu/directory/onb1
Licenses: GPL 3
Build system: r
Synopsis: Spatial Covariance Functions
Description:

Spatial (cross-)covariance and related geostatistical tools: the nonparametric (cross-)covariance function , the spline correlogram, the nonparametric phase coherence function, local indicators of spatial association (LISA), (Mantel) correlogram, (Partial) Mantel test.

r-nlshrink 1.0.1
Propagated dependencies: r-nloptr@2.2.1 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nlshrink
Licenses: GPL 3
Build system: r
Synopsis: Non-Linear Shrinkage Estimation of Population Eigenvalues and Covariance Matrices
Description:

Non-linear shrinkage estimation of population eigenvalues and covariance matrices, based on publications by Ledoit and Wolf (2004, 2015, 2016).

r-networkcomparisontest 2.2.2
Propagated dependencies: r-reshape2@1.4.5 r-qgraph@1.9.8 r-networktools@1.6.0 r-matrix@1.7-4 r-isingfit@0.4
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NetworkComparisonTest
Licenses: GPL 2
Build system: r
Synopsis: Statistical Comparison of Two Networks Based on Several Invariance Measures
Description:

This permutation based hypothesis test, suited for several types of data supported by the estimateNetwork function of the bootnet package (Epskamp & Fried, 2018), assesses the difference between two networks based on several invariance measures (network structure invariance, global strength invariance, edge invariance, several centrality measures, etc.). Network structures are estimated with l1-regularization. The Network Comparison Test is suited for comparison of independent (e.g., two different groups) and dependent samples (e.g., one group that is measured twice). See van Borkulo et al. (2021), available from <doi:10.1037/met0000476>.

r-nhldata 1.0.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NHLData
Licenses: CC0
Build system: r
Synopsis: Scores for Every Season Since the Founding of the NHL in 1917
Description:

Each dataset contains scores for every game during a specific season of the NHL.

r-nntbiomarker 0.29.11
Propagated dependencies: r-xtable@1.8-4 r-stringr@1.6.0 r-shiny@1.11.1 r-mvbutils@2.8.232 r-magrittr@2.0.4
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=NNTbiomarker
Licenses: GPL 3
Build system: r
Synopsis: Calculate Design Parameters for Biomarker Validation Studies
Description:

Helps a clinical trial team discuss the clinical goals of a well-defined biomarker with a diagnostic, staging, prognostic, or predictive purpose. From this discussion will come a statistical plan for a (non-randomized) validation trial. Both prospective and retrospective trials are supported. In a specific focused discussion, investigators should determine the range of "discomfort" for the NNT, number needed to treat. The meaning of the discomfort range, [NNTlower, NNTupper], is that within this range most physicians would feel discomfort either in treating or withholding treatment. A pair of NNT values bracketing that range, NNTpos and NNTneg, become the targets of the study's design. If the trial can demonstrate that a positive biomarker test yields an NNT less than NNTlower, and that a negative biomarker test yields an NNT less than NNTlower, then the biomarker may be useful for patients. A highlight of the package is visualization of a "contra-Bayes" theorem, which produces criteria for retrospective case-controls studies.

r-niledam 0.4
Propagated dependencies: r-tidyr@1.3.1 r-thematic@0.1.8 r-shinythemes@1.2.0 r-shinyjs@2.1.0 r-shiny@1.11.1 r-scales@1.4.0 r-rlang@1.1.6 r-nleqslv@3.3.5 r-magrittr@2.0.4 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=NiLeDAM
Licenses: GPL 2+
Build system: r
Synopsis: Monazite Dating for the NiLeDAM Team
Description:

Th-U-Pb electron microprobe age dating of monazite, as originally described in <doi:10.1016/0009-2541(96)00024-1>.

r-netutils 0.8.4
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/schochastics/netUtils/
Licenses: Expat
Build system: r
Synopsis: Collection of Tools for Network Analysis
Description:

This package provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes <doi:10.1016/j.socnet.2019.04.003>, core-periphery models <doi:10.1016/S0378-8733(99)00019-2>, and several graph generators. Most functions are build upon igraph'.

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-neo4r 0.1.1
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-shiny@1.11.1 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-r6@2.6.1 r-purrr@1.2.0 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-igraph@2.2.1 r-httr@1.4.7 r-glue@1.8.0 r-data-table@1.17.8 r-attempt@0.3.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/neo4j-rstats/neo4r
Licenses: Expat
Build system: r
Synopsis: 'Neo4J' Driver
Description:

This package provides a Modern and Flexible Neo4J Driver, allowing you to query data on a Neo4J server and handle the results in R. It's modern in the sense it provides a driver that can be easily integrated in a data analysis workflow, especially by providing an API working smoothly with other data analysis and graph packages. It's flexible in the way it returns the results, by trying to stay as close as possible to the way Neo4J returns data. That way, you have the control over the way you will compute the results. At the same time, the result is not too complex, so that the "heavy lifting" of data wrangling is not left to the user.

r-npordtests 1.2
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=npordtests
Licenses: GPL 2+
Build system: r
Synopsis: Nonparametric Tests for Equality of Location Against Ordered Alternatives
Description:

This package performs nonparametric tests for equality of location against ordered alternatives.

r-nparmd 0.2.3
Propagated dependencies: r-matrixstats@1.5.0 r-matrixcalc@1.0-6 r-mass@7.3-65 r-gtools@3.9.5 r-formula@1.2-5
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nparMD
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Nonparametric Analysis of Multivariate Data in Factorial Designs
Description:

Analysis of multivariate data with two-way completely randomized factorial design. The analysis is based on fully nonparametric, rank-based methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda, Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate response is allowed to be ordinal, quantitative, binary or a mixture of the different variable types. The package offers two functions performing the analysis, one for small and the other for large sample sizes. The underlying methodology is largely described in Bathke and Harrar (2016) <doi:10.1007/978-3-319-39065-9_7> and in Munzel and Brunner (2000) <doi:10.1016/S0378-3758(99)00212-8> and in Kiefel and Bathke (2022) <doi:10.1515/stat-2022-0112>.

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-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
Build system: r
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-nearfar 1.3
Propagated dependencies: r-nbpmatching@1.5.6 r-mass@7.3-65 r-gensa@1.1.15 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=nearfar
Licenses: GPL 3
Build system: r
Synopsis: Near-Far Matching
Description:

Near-far matching is a study design technique for preprocessing observational data to mimic a pair-randomized trial. Individuals are matched to be near on measured confounders and far on levels of an instrumental variable. Methods outlined in further detail in Rigdon, Baiocchi, and Basu (2018) <doi:10.18637/jss.v086.c05>.

r-naspaclust 0.2.2
Propagated dependencies: r-stabledist@0.7-2 r-rdpack@2.6.4 r-rdist@0.0.5 r-beepr@2.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=naspaclust
Licenses: GPL 3
Build system: r
Synopsis: Nature-Inspired Spatial Clustering
Description:

Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geographically Weighted Clustering with various optimization algorithms, mainly from Xin She Yang (2014) <ISBN:9780124167438> with book entitled Nature-Inspired Optimization Algorithms. The optimization algorithm is useful to tackle the disadvantages of clustering inconsistency when using the traditional approach. The distance measurements option is also provided in order to increase the quality of clustering results. The Fuzzy Geographically Weighted Clustering with nature inspired optimisation algorithm was firstly developed by Arie Wahyu Wijayanto and Ayu Purwarianti (2014) <doi:10.1109/CITSM.2014.7042178> using Artificial Bee Colony algorithm.

r-notebookutils 1.6.2
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=notebookutils
Licenses: Expat
Build system: r
Synopsis: Dummy R APIs Used in 'Azure Synapse Analytics' for Local Developments
Description:

This is a pure dummy interfaces package which mirrors MsSparkUtils APIs <https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/microsoft-spark-utilities?pivots=programming-language-r> of Azure Synapse Analytics <https://learn.microsoft.com/en-us/azure/synapse-analytics/> for R users, customer of Azure Synapse can download this package from CRAN for local development.

r-nebula 1.5.6
Propagated dependencies: r-trust@0.1-8 r-singlecellexperiment@1.32.0 r-seurat@5.3.1 r-rfast@2.1.5.2 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-parallelly@1.45.1 r-nloptr@2.2.1 r-matrix@1.7-4 r-future@1.68.0 r-foreach@1.5.2 r-dorng@1.8.6.2 r-dofuture@1.1.2
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/lhe17/nebula
Licenses: GPL 3
Build system: r
Synopsis: Negative Binomial Mixed Models Using Large-Sample Approximation for Differential Expression Analysis of ScRNA-Seq Data
Description:

This package provides a fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.

r-nutrition 1.1.0
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://wleoncio.github.io/nutrition/
Licenses: GPL 3+
Build system: r
Synopsis: Useful Functions for People on a Diet
Description:

This package contains a collection of functions for performing different kinds of calculation that are of interest to someone following a diet plan. Calculators for the Basal Metabolic Rate are based on Mifflin et al. (1990) <doi:10.1093/ajcn/51.2.241> and McArdle, W. D., Katch, F. I., & Katch, V. L. (2010, ISBN:9780812109917).

r-namer 0.1.9
Propagated dependencies: r-tibble@3.3.0 r-rstudioapi@0.17.1 r-purrr@1.2.0 r-magrittr@2.0.4 r-glue@1.8.0 r-fs@1.6.6 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/jumpingrivers/namer
Licenses: Expat
Build system: r
Synopsis: Names Your 'R Markdown' Chunks
Description:

It names the R Markdown chunks of files based on the filename.

r-neuromplex 1.0-1
Propagated dependencies: r-tidyr@1.3.1 r-magrittr@2.0.4 r-gridextra@2.3 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-bayeslogit@2.1
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://cran.r-project.org/package=neuromplex
Licenses: GPL 2
Build system: r
Synopsis: Neural Multiplexing Analysis
Description:

Statistical methods for whole-trial and time-domain analysis of single cell neural response to multiple stimuli presented simultaneously. The package is based on the paper by C Glynn, ST Tokdar, A Zaman, VC Caruso, JT Mohl, SM Willett, and JM Groh (2021) "Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure", is in press for publication by the Annals of Applied Statistics. A preprint may be found at <arXiv:1911.04387>.

r-nlgm 1.0
Propagated dependencies: r-rfast2@0.1.5.5 r-rfast@2.1.5.2 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=nlgm
Licenses: GPL 2+
Build system: r
Synopsis: Non Linear Growth Models
Description:

Six growth models are fitted using non-linear least squares. These are the Richards, the 3, 4 and 5 parameter logistic, the Gompetz and the Weibull growth models. Reference: Reddy T., Shkedy Z., van Rensburg C. J., Mwambi H., Debba P., Zuma K. and Manda, S. (2021). "Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach". BMC medical research methodology, 21(1), 1-11. <doi:10.1186/s12874-020-01165-x>.

r-netmhc2pan 1.3.2
Propagated dependencies: r-tibble@3.3.0 r-testit@0.13 r-stringr@1.6.0 r-seqinr@4.2-36 r-readr@2.1.6 r-rappdirs@0.3.3 r-dplyr@1.1.4 r-devtools@2.4.6
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://github.com/richelbilderbeek/netmhc2pan/
Licenses: GPL 3
Build system: r
Synopsis: Interface to 'NetMHCIIpan'
Description:

The field of immunology benefits from software that can predict which peptide sequences trigger an immune response. NetMHCIIpan is a such a tool: it predicts the binding strength of a short peptide to a Major Histocompatibility Complex class II (MHC-II) molecule. NetMHCIIpan can be used from a web server at <https://services.healthtech.dtu.dk/services/NetMHCIIpan-3.2/> or from the command-line, using a local installation. This package allows to call NetMHCIIpan from R.

r-nbtransmission 1.2.0
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-poisbinom@1.0.2 r-lubridate@1.9.4 r-dplyr@1.1.4 r-caret@7.0-1 r-broom@1.0.10
Channel: guix-cran
Location: guix-cran/packages/n.scm (guix-cran packages n)
Home page: https://sarahleavitt.github.io/nbTransmission/
Licenses: Expat
Build system: r
Synopsis: Naive Bayes Transmission Analysis
Description:

Estimates the relative transmission probabilities between cases in an infectious disease outbreak or cluster using naive Bayes. Included are various functions to use these probabilities to estimate transmission parameters such as the generation/serial interval and reproductive number as well as finding the contribution of covariates to the probabilities and visualizing results. The ideal use is for an infectious disease dataset with metadata on the majority of cases but more informative data such as contact tracing or pathogen whole genome sequencing on only a subset of cases. For a detailed description of the methods see Leavitt et al. (2020) <doi:10.1093/ije/dyaa031>.

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