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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-joinspy 0.7.3
Propagated dependencies: r-rlang@1.1.6 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://gillescolling.com/joinspy/
Licenses: Expat
Build system: r
Synopsis: Diagnostic Tools for Data Frame Joins
Description:

This package provides diagnostic tools for understanding and debugging data frame joins. Analyzes key columns before joining to detect duplicates, mismatches, encoding issues, and other common problems. Explains unexpected row count changes and provides safe join wrappers with cardinality enforcement. Concepts and diagnostics build on tidy data principles as described in Wickham (2014) <doi:10.18637/jss.v059.i10>.

r-jenga 1.3.0
Propagated dependencies: r-tictoc@1.2.1 r-scales@1.4.0 r-rfast@2.1.5.2 r-readr@2.1.6 r-purrr@1.2.0 r-philentropy@0.10.0 r-narray@0.5.2 r-moments@0.14.1 r-modeest@2.4.0 r-lubridate@1.9.4 r-imputets@3.4 r-greybox@2.0.8 r-ggplot2@4.0.1 r-fastdummies@1.7.5 r-fancova@0.6-1 r-entropy@1.3.2 r-dplyr@1.1.4 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://rpubs.com/giancarlo_vercellino/jenga
Licenses: GPL 3
Build system: r
Synopsis: Fast Extrapolation of Time Features using K-Nearest Neighbors
Description:

Fast extrapolation of univariate and multivariate time features using K-Nearest Neighbors. The compact set of hyper-parameters is tuned via grid or random search.

r-jointseg 1.0.3
Propagated dependencies: r-matrixstats@1.5.0 r-dnacopy@1.84.0 r-acnr@1.0.0
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/mpierrejean/jointseg
Licenses: LGPL 2.1+
Build system: r
Synopsis: Joint Segmentation of Multivariate (Copy Number) Signals
Description:

This package provides methods for fast segmentation of multivariate signals into piecewise constant profiles and for generating realistic copy-number profiles. A typical application is the joint segmentation of total DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism (SNP) microarrays in cancer studies. The methods are described in Pierre-Jean, Rigaill and Neuvial (2015) <doi:10.1093/bib/bbu026>.

r-jsonstat 0.0.2
Propagated dependencies: r-rlang@1.1.6 r-jsonlite@2.0.0 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/zedoul/jsonstat
Licenses: Expat
Build system: r
Synopsis: Interface to 'JSON-stat'
Description:

Interface to JSON-stat <https://json-stat.org/>, a simple lightweight JSON format for data dissemination.

r-jobqueue 1.7.0
Propagated dependencies: r-rlang@1.1.6 r-r6@2.6.1 r-ps@1.9.1 r-promises@1.5.0 r-parallelly@1.45.1 r-magrittr@2.0.4 r-later@1.4.4 r-interprocess@1.3.0 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cmmr.github.io/jobqueue/
Licenses: Expat
Build system: r
Synopsis: Run Interruptible Code Asynchronously
Description:

Takes an R expression and returns a job object with a $stop() method which can be called to terminate the background job. Also provides timeouts and other mechanisms for automatically terminating a background job. The result of the expression is available synchronously via $result or asynchronously with callbacks or through the promises package framework.

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-japanapis 0.1.1
Propagated dependencies: r-tibble@3.3.0 r-scales@1.4.0 r-jsonlite@2.0.0 r-httr@1.4.7 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/lightbluetitan/japanapis
Licenses: Expat
Build system: r
Synopsis: Access Japanese Data via Public APIs and Curated Datasets
Description:

This package provides functions to access data from public RESTful APIs including Nager.Date', World Bank API', and REST Countries API', retrieving real-time or historical data related to Japan, such as holidays, economic indicators, and international demographic and geopolitical indicators. Additionally, the package includes one of the largest curated collections of open datasets focused on Japan, covering topics such as natural disasters, economic production, vehicle industry, air quality, demographics, and administrative divisions. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: Nager.Date <https://date.nager.at/Api>, World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, and REST Countries API <https://restcountries.com/>.

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-jvcoords 1.0.3
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/seehuhn/jvcoords
Licenses: GPL 3
Build system: r
Synopsis: Principal Component Analysis (PCA) and Whitening
Description:

This package provides functions to standardize and whiten data, and to perform Principal Component Analysis (PCA). The main advantage of this package over alternatives like prcomp() is, that jvcoords makes it easy to convert (additional) data between the original and the transformed coordinates. The package also provides a class coords, which can represent affine coordinate transformations. This class forms the basis of the transformations provided by the package, but can also be used independently. The implementation has been optimized to be of comparable speed (and sometimes even faster) than existing alternatives.

r-jrich 0.60-35
Propagated dependencies: r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/Dmirandae/jrich
Licenses: GPL 3
Build system: r
Synopsis: Jack-Knife Support for Evolutionary Distinctiveness Indices I and W
Description:

These functions calculate the taxonomic measures presented in Miranda-Esquivel (2016). The package introduces Jack-knife resampling in evolutionary distinctiveness prioritization analysis, as a way to evaluate the support of the ranking in area prioritization, and the persistence of a given area in a conservation analysis. The algorithm is described in: Miranda-Esquivel, D (2016) <DOI:10.1007/978-3-319-22461-9_11>.

r-jointcalib 0.1.0
Propagated dependencies: r-survey@4.4-8 r-sampling@2.11 r-mathjaxr@1.8-0 r-mass@7.3-65 r-laeken@0.5.3 r-ebal@0.1-8
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/ncn-foreigners/jointCalib
Licenses: GPL 3
Build system: r
Synopsis: Joint Calibration of Totals and Quantiles
Description:

This package provides a small package containing functions to perform a joint calibration of totals and quantiles. The calibration for totals is based on Deville and Särndal (1992) <doi:10.1080/01621459.1992.10475217>, the calibration for quantiles is based on Harms and Duchesne (2006) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X20060019255>. The package uses standard calibration via the survey', sampling or laeken packages. In addition, entropy balancing via the ebal package and empirical likelihood based on codes from Wu (2005) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2005002/article/9051-eng.pdf> can be used. See the paper by BerÄ sewicz and Szymkowiak (2023) for details <arXiv:2308.13281>.

r-jollofr 0.6.5
Propagated dependencies: r-tidyr@1.3.1 r-terra@1.8-86 r-sf@1.0-23 r-reshape2@1.4.5 r-raster@3.6-32 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/wpgp/jollofR/
Licenses: Expat
Build system: r
Synopsis: Small Area Population Estimation by Demographics
Description:

Automatic disaggregation of small-area population estimates by demographic groups (e.g., age, sex, race, marital status, educational level, etc) along with the estimates of uncertainty, using advanced Bayesian statistical modelling approaches based on integrated nested Laplace approximation (INLA) Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x> and stochastic partial differential equation (SPDE) methods Lindgren et al. (2011) <doi:10.1111/j.1467-9868.2011.00777.x>. The package implements hierarchical Bayesian modeling frameworks for small area estimation as described in Leasure et al. (2020) <doi:10.1073/pnas.1913050117> and Nnanatu et al. (2025) <doi:10.1038/s41467-025-59862-4>.

r-jdmbs 1.4
Propagated dependencies: r-png@0.1-8 r-igraph@2.2.1 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=Jdmbs
Licenses: GPL 2+
Build system: r
Synopsis: Monte Carlo Option Pricing Algorithms for Jump Diffusion Models with Correlational Companies
Description:

Option is a one of the financial derivatives and its pricing is an important problem in practice. The process of stock prices are represented as Geometric Brownian motion [Black (1973) <doi:10.1086/260062>] or jump diffusion processes [Kou (2002) <doi:10.1287/mnsc.48.8.1086.166>]. In this package, algorithms and visualizations are implemented by Monte Carlo method in order to calculate European option price for three equations by Geometric Brownian motion and jump diffusion processes and furthermore a model that presents jumps among companies affect each other.

r-josae 0.3.0
Propagated dependencies: r-nlme@3.1-168
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=JoSAE
Licenses: GPL 2
Build system: r
Synopsis: Unit-Level and Area-Level Small Area Estimation
Description:

Implementation of some unit and area level EBLUP estimators as well as the estimators of their MSE also under heteroscedasticity. The package further documents the publications Breidenbach and Astrup (2012) <DOI:10.1007/s10342-012-0596-7>, Breidenbach et al. (2016) <DOI:10.1016/j.rse.2015.07.026> and Breidenbach et al. (2018 in press). The vignette further explains the use of the implemented functions.

r-joinerml 0.4.7
Propagated dependencies: r-tibble@3.3.0 r-survival@3.8-3 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-randtoolbox@2.0.5 r-nlme@3.1-168 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-mass@7.3-65 r-lme4@1.1-37 r-ggplot2@4.0.1 r-generics@0.1.4 r-foreach@1.5.2 r-doparallel@1.0.17 r-cobs@1.3-9-1
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/graemeleehickey/joineRML
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes
Description:

Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).

r-jsonld 2.2.1
Propagated dependencies: r-v8@8.0.1 r-jsonlite@2.0.0 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://docs.ropensci.org/jsonld/
Licenses: Modified BSD
Build system: r
Synopsis: JSON for Linking Data
Description:

JSON-LD <https://www.w3.org/TR/json-ld/> is a light-weight syntax for expressing linked data. It is primarily intended for web-based programming environments, interoperable web services and for storing linked data in JSON-based databases. This package provides bindings to the JavaScript library for converting, expanding and compacting JSON-LD documents.

r-jetty 0.2.2
Propagated dependencies: r-rlang@1.1.6 r-renv@1.1.5
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/dmolitor/jetty
Licenses: GPL 3+
Build system: r
Synopsis: Execute R in a 'Docker' Context
Description:

The goal of jetty is to execute R functions and code snippets in an isolated R subprocess within a Docker container and return the evaluated results to the local R session. jetty can install necessary packages at runtime and seamlessly propagates errors and outputs from the Docker subprocess back to the main session. jetty is primarily designed for sandboxed testing and quick execution of example code.

r-jointvip 1.0.1
Propagated dependencies: r-ggrepel@0.9.6 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/ldliao/jointVIP
Licenses: Expat
Build system: r
Synopsis: Prioritize Variables with Joint Variable Importance Plot in Observational Study Design
Description:

In the observational study design stage, matching/weighting methods are conducted. However, when many background variables are present, the decision as to which variables to prioritize for matching/weighting is not trivial. Thus, the joint treatment-outcome variable importance plots are created to guide variable selection. The joint variable importance plots enhance variable comparisons via unadjusted bias curves derived under the omitted variable bias framework. The plots translate variable importance into recommended values for tuning parameters in existing methods. Post-matching and/or weighting plots can also be used to visualize and assess the quality of the observational study design. The method motivation and derivation is presented in "Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot" by Liao et al. (2024) <doi:10.1080/00031305.2024.2303419>. See the package paper by Liao and Pimentel (2024) <doi:10.21105/joss.06093> for a beginner friendly user introduction.

r-jagsui 1.6.3
Dependencies: jags@4.3.1
Propagated dependencies: r-rjags@4-17 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://kenkellner.com/jagsUI/
Licenses: GPL 3
Build system: r
Synopsis: Wrapper Around 'rjags' to Streamline 'JAGS' Analyses
Description:

This package provides a set of wrappers around rjags functions to run Bayesian analyses in JAGS (specifically, via libjags'). A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the R2WinBUGS'/'R2OpenBUGS packages to allow easy switching between MCMC samplers.

r-juliacall 0.17.6
Propagated dependencies: r-rjson@0.2.23 r-rcpp@1.1.0 r-knitr@1.50
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/JuliaInterop/JuliaCall
Licenses: Expat
Build system: r
Synopsis: Seamless Integration Between R and 'Julia'
Description:

This package provides an R interface to Julia', which is a high-level, high-performance dynamic programming language for numerical computing, see <https://julialang.org/> for more information. It provides a high-level interface as well as a low-level interface. Using the high level interface, you could call any Julia function just like any R function with automatic type conversion. Using the low level interface, you could deal with C-level SEXP directly while enjoying the convenience of using a high-level programming language like Julia'.

r-joinet 1.0.0
Propagated dependencies: r-palasso@1.0.0 r-glmnet@4.1-10 r-cornet@1.0.0
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/rauschenberger/joinet
Licenses: GPL 3
Build system: r
Synopsis: Penalised Multivariate Regression ('Multi-Target Learning')
Description:

This package implements penalised multivariate regression (i.e., for multiple outcomes and many features) by stacked generalisation (<doi:10.1093/bioinformatics/btab576>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. For optional comparisons, install remMap from GitHub (<https://github.com/cran/remMap>).

r-josaplay 0.1.3
Propagated dependencies: r-utf8@1.2.6 r-magrittr@2.0.4
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/mrchypark/josaplay
Licenses: Expat
Build system: r
Synopsis: Add Josa Based on Previous Letter in Korean
Description:

Josa in Korean is often determined by judging the previous word. When writing reports using Rmd, a function that prints the appropriate investigation for each case is helpful. The josaplay package then evaluates the previous word to determine which josa is appropriate.

r-jousboost 2.1.0
Propagated dependencies: r-rpart@4.1.24 r-rcpp@1.1.0 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://cran.r-project.org/package=JOUSBoost
Licenses: Expat
Build system: r
Synopsis: Implements Under/Oversampling for Probability Estimation
Description:

This package implements under/oversampling for probability estimation. To be used with machine learning methods such as AdaBoost, random forests, etc.

r-jointfpm 1.3.0
Propagated dependencies: r-survival@3.8-3 r-statmod@1.5.1 r-rstpm2@1.7.1 r-rmutil@1.1.10 r-rlang@1.1.6 r-matrixstats@1.5.0 r-lifecycle@1.0.4 r-data-table@1.17.8 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/entjos/JointFPM
Licenses: FSDG-compatible
Build system: r
Synopsis: Parametric Model for Estimating the Mean Number of Events
Description:

Implementation of a parametric joint model for modelling recurrent and competing event processes using generalised survival models as described in Entrop et al., (2025) <doi:10.1002/bimj.70038>. The joint model can subsequently be used to predict the mean number of events in the presence of competing risks at different time points. Comparisons of the mean number of event functions, e.g. the differences in mean number of events between two exposure groups, are also available.

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