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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-jointcomprisk 0.1.1
Propagated dependencies: r-survival@3.8-3 r-rlang@1.1.6 r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/cathyzzzhang/jointCompRisk
Licenses: Expat
Build system: r
Synopsis: Joint Inference for Competing Risks Data Using Multiple Endpoints
Description:

This package provides tools for competing risks trials that allow simultaneous inference on recovery and mortality endpoints. Provides data preparation helpers, standard cumulative incidence estimators (restricted mean time gained/lost), and severity weighted extensions that integrate longitudinal ordinal outcomes to summarise treatment benefit. Methods follow Wen, Hu, and Wang (2023) Biometrics 79(3):1635-1645 <doi:10.1111/biom.13752>.

r-jamba 1.0.4
Propagated dependencies: r-withr@3.0.2 r-rcolorbrewer@1.1-3 r-kernsmooth@2.23-26 r-colorspace@2.1-2
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://jmw86069.github.io/jamba/
Licenses: Expat
Build system: r
Synopsis: Just Analysis Methods Base
Description:

Just analysis methods ('jam') base functions focused on bioinformatics. Version- and gene-centric alphanumeric sort, unique name and version assignment, colorized console and HTML output, color ramp and palette manipulation, Rmarkdown cache import, styled Excel worksheet import and export, interpolated raster output from smooth scatter and image plots, list to delimited vector, efficient list tools.

r-joint-cox 3.16
Propagated dependencies: r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=joint.Cox
Licenses: GPL 2
Build system: r
Synopsis: Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis
Description:

Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death. Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model. The methods are applicable for meta-analytic data containing individual-patient information from several studies. Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). Methodologies were published in Emura et al. (2017) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>, Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>, Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>. See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>. Survival data from ovarian cancer patients are also available.

r-jointpm 2.3.2
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=jointPm
Licenses: GPL 2+
Build system: r
Synopsis: Risk Estimation Using the Joint Probability Method
Description:

Estimate risk caused by two extreme and dependent forcing variables using bivariate extreme value models as described in Zheng, Westra, and Sisson (2013) <doi:10.1016/j.jhydrol.2013.09.054>; Zheng, Westra and Leonard (2014) <doi:10.1002/2013WR014616>; Zheng, Leonard and Westra (2015) <doi:10.2166/hydro.2015.052>.

r-jointdiag 0.4
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/gouypailler/jointDiag
Licenses: GPL 2+
Build system: r
Synopsis: Joint Approximate Diagonalization of a Set of Square Matrices
Description:

Different algorithms to perform approximate joint diagonalization of a finite set of square matrices. Depending on the algorithm, orthogonal or non-orthogonal diagonalizer is found. These algorithms are particularly useful in the context of blind source separation. Original publications of the algorithms can be found in Ziehe et al. (2004), Pham and Cardoso (2001) <doi:10.1109/78.942614>, Souloumiac (2009) <doi:10.1109/TSP.2009.2016997>, Vollgraff and Obermayer <doi:10.1109/TSP.2006.877673>. An example of application in the context of Brain-Computer Interfaces EEG denoising can be found in Gouy-Pailler et al (2010) <doi:10.1109/TBME.2009.2032162>.

r-jmvconnect 2.5.7
Propagated dependencies: r-rcpp@1.1.0 r-rappdirs@0.3.3 r-jmvcore@2.7.7 r-httr@1.4.7 r-evaluate@1.0.5 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=jmvconnect
Licenses: GPL 2+
Build system: r
Synopsis: Connect to the 'jamovi' Statistical Spreadsheet
Description:

This package provides methods to access data sets from the jamovi statistical spreadsheet (see <https://www.jamovi.org> for more information) from R.

r-joyn 0.3.0
Propagated dependencies: r-rlang@1.1.6 r-lifecycle@1.0.4 r-glue@1.8.0 r-data-table@1.17.8 r-collapse@2.1.5 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/randrescastaneda/joyn
Licenses: Expat
Build system: r
Synopsis: Tool for Diagnosis of Tables Joins and Complementary Join Features
Description:

Tool for diagnosing table joins. It combines the speed of `collapse` and `data.table`, the flexibility of `dplyr`, and the diagnosis and features of the `merge` command in `Stata`.

r-jointnmix 1.0
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=jointNmix
Licenses: GPL 2+
Build system: r
Synopsis: Joint N-Mixture Models for Site-Associated Species
Description:

Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods.

r-justifier 0.2.8
Propagated dependencies: r-yum@0.1.0 r-yaml@2.3.10 r-purrr@1.2.0 r-diagrammersvg@0.1 r-diagrammer@1.0.11 r-data-tree@1.2.0
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://justifier.opens.science
Licenses: GPL 2+
Build system: r
Synopsis: Human and Machine-Readable Justifications and Justified Decisions Based on 'YAML'
Description:

Leverages the yum package to implement a YAML ('YAML Ain't Markup Language', a human friendly standard for data serialization; see <https://yaml.org>) standard for documenting justifications, such as for decisions taken during the planning, execution and analysis of a study or during the development of a behavior change intervention as illustrated by Marques & Peters (2019) <doi:10.17605/osf.io/ndxha>. These justifications are both human- and machine-readable, facilitating efficient extraction and organisation.

r-jetpack 0.5.5
Propagated dependencies: r-renv@1.1.5 r-remotes@2.5.0 r-docopt@0.7.2 r-desc@1.4.3
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/ankane/jetpack
Licenses: Expat
Build system: r
Synopsis: Friendly Package Manager
Description:

Manage project dependencies from your DESCRIPTION file. Create a reproducible virtual environment with minimal additional files in your project. Provides tools to add, remove, and update dependencies as well as install existing dependencies with a single function.

r-jagstargets 1.2.2
Propagated dependencies: r-withr@3.0.2 r-tidyselect@1.2.1 r-tibble@3.3.0 r-targets@1.11.4 r-tarchetypes@0.13.2 r-secretbase@1.0.5 r-rlang@1.1.6 r-rjags@4-17 r-r2jags@0.8-9 r-qs2@0.1.6 r-purrr@1.2.0 r-posterior@1.6.1 r-fst@0.9.8 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://docs.ropensci.org/jagstargets/
Licenses: Expat
Build system: r
Synopsis: Targets for JAGS Pipelines
Description:

Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the jagstargets R package is leverages targets and R2jags to ease this burden. jagstargets makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than targets alone. For the underlying methodology, please refer to the documentation of targets <doi:10.21105/joss.02959> and JAGS (Plummer 2003) <https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf>.

r-jaccard 0.1.2
Propagated dependencies: r-shiny@1.11.1 r-rcpp@1.1.0 r-qvalue@2.42.0
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=jaccard
Licenses: GPL 2
Build system: r
Synopsis: Testing Similarity Between Binary Datasets using Jaccard/Tanimoto Coefficients
Description:

Calculate statistical significance of Jaccard/Tanimoto similarity coefficients.

r-jmotif 1.2.1
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/jMotif/jmotif-R
Licenses: GPL 2
Build system: r
Synopsis: Time Series Analysis Toolkit Based on Symbolic Aggregate Discretization, i.e. SAX
Description:

This package implements time series z-normalization, SAX, HOT-SAX, VSM, SAX-VSM, RePair, and RRA algorithms facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly), and characteristic pattern discovery along with interpretable time series classification.

r-jacpop 0.6
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=jacpop
Licenses: GPL 3
Build system: r
Synopsis: Jaccard Index for Population Structure Identification
Description:

Uses the Jaccard similarity index to account for population structure in sequencing studies. This method was specifically designed to detect population stratification based on rare variants, hence it will be especially useful in rare variant analysis.

r-jocre 0.3.3
Propagated dependencies: r-tsp@1.2.6 r-plyr@1.8.9 r-kernsmooth@2.23-26 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=jocre
Licenses: GPL 2
Build system: r
Synopsis: Joint Confidence Regions
Description:

Computing and plotting joint confidence regions and intervals. Regions include classical ellipsoids, minimum-volume or minimum-length regions, and an empirical Bayes region. Intervals include the TOST procedure with ordinary or expanded intervals and a fixed-sequence procedure. Such regions and intervals are useful e.g., for the assessment of multi-parameter (bio-)equivalence. Joint confidence regions for the mean and variance of a normal distribution are available as well.

r-jwileymisc 1.4.4
Propagated dependencies: r-vgam@1.1-13 r-scales@1.4.0 r-robustbase@0.99-6 r-rlang@1.1.6 r-psych@2.5.6 r-multcompview@0.1-10 r-mice@3.18.0 r-mgcv@1.9-4 r-mass@7.3-65 r-lme4@1.1-37 r-lavaan@0.6-20 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-gamlss@5.5-0 r-fst@0.9.8 r-extraoperators@0.3.0 r-emmeans@2.0.0 r-digest@0.6.39 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://joshuawiley.com/JWileymisc/
Licenses: GPL 3+
Build system: r
Synopsis: Miscellaneous Utilities and Functions
Description:

Miscellaneous tools and functions, including: generate descriptive statistics tables, format output, visualize relations among variables or check distributions, and generic functions for residual and model diagnostics.

r-jointest 1.0
Propagated dependencies: r-flipscores@1.3.2 r-flip@2.5.1
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=jointest
Licenses: GPL 2
Build system: r
Synopsis: Multivariate Testing Through Joint Resampling-Based Tests
Description:

Runs resampling-based tests jointly, e.g., sign-flip score tests from Hemerik et al., (2020) <doi:10.1111/rssb.12369>, to allow for multivariate testing, i.e., weak and strong control of the Familywise Error Rate or True Discovery Proportion.

r-jcvrisk 0.1.3
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=Jcvrisk
Licenses: Expat
Build system: r
Synopsis: Risk Calculator for Cardiovascular Disease in Japan
Description:

This package provides a calculation tool to obtain the 5-year or 10-year risk of cardiovascular disease from various risk models.

r-javagd 0.6-6
Propagated dependencies: r-rjava@1.0-11
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://www.rforge.net/JavaGD/
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Java Graphics Device
Description:

Graphics device routing all graphics commands to a Java program. The actual functionality of the JavaGD depends on the Java-side implementation. Simple AWT and Swing implementations are included.

r-junctions 2.1.4
Propagated dependencies: r-tibble@3.3.0 r-rcppparallel@5.1.11-1 r-rcpp@1.1.0 r-nloptr@2.2.1
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://thijsjanzen.github.io/junctions/
Licenses: GPL 2+
Build system: r
Synopsis: The Breakdown of Genomic Ancestry Blocks in Hybrid Lineages
Description:

Individual based simulations of hybridizing populations, where the accumulation of junctions is tracked. Furthermore, mathematical equations are provided to verify simulation outcomes. Both simulations and mathematical equations are based on Janzen (2018, <doi:10.1101/058107>) and Janzen (2022, <doi:10.1111/1755-0998.13519>).

r-jcp 1.2
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=jcp
Licenses: GPL 3
Build system: r
Synopsis: Joint Change Point Detection
Description:

Procedures for joint detection of changes in both expectation and variance in univariate sequences. Performs a statistical test of the null hypothesis of the absence of change points. In case of rejection performs an algorithm for change point detection. Reference - Bivariate change point detection - joint detection of changes in expectation and variance, Scandinavian Journal of Statistics, DOI 10.1111/sjos.12547.

r-jmastats 0.3.0
Propagated dependencies: r-xml2@1.5.0 r-units@1.0-0 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-sf@1.0-23 r-rvest@1.0.5 r-rlang@1.1.6 r-readr@2.1.6 r-rappdirs@0.3.3 r-purrr@1.2.0 r-lubridate@1.9.4 r-lifecycle@1.0.4 r-ggplot2@4.0.1 r-forcats@1.0.1 r-dplyr@1.1.4 r-crayon@1.5.3 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://uribo.github.io/jmastats/
Licenses: Expat
Build system: r
Synopsis: Download Weather Data from Japan Meteorological Agency Website
Description:

This package provides features that allow users to download weather data published by the Japan Meteorological Agency (JMA) website (<https://www.jma.go.jp/jma/index.html>). The data includes information dating back to 1976 and aligns with the categories available on the website. Additionally, users can process the best track data of typhoons and easily handle earthquake record files.

r-jaya 1.0.3
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/neerajdhanraj/Jaya
Licenses: Expat
Build system: r
Synopsis: Gradient-Free Optimization Algorithm for Single and Multi-Objective Problems
Description:

An implementation of the Jaya optimization algorithm for both single-objective and multi-objective problems. Jaya is a population-based, gradient-free optimization algorithm capable of solving constrained and unconstrained optimization problems without hyperparameters. This package includes features such as multi-objective Pareto optimization, adaptive population adjustment, and early stopping. For further details, see R.V. Rao (2016) <doi:10.5267/j.ijiec.2015.8.004>.

r-jackstraw 1.3.17
Propagated dependencies: r-rsvd@1.0.5 r-irlba@2.3.5.1 r-genio@1.1.2 r-corpcor@1.6.10 r-clusterr@1.3.5 r-cluster@2.1.8.1 r-bedmatrix@2.0.4
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=jackstraw
Licenses: GPL 2
Build system: r
Synopsis: Statistical Inference for Unsupervised Learning
Description:

Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq).

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