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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-kernopt 1.0.0
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://thomasfillon.github.io/kernopt/
Licenses: GPL 3+
Build system: r
Synopsis: Estimating Count Data Distributions with Discrete Optimal Symmetric Kernel
Description:

Implementation of Discrete Symmetric Optimal Kernel for estimating count data distributions, as described by T. Senga Kiessé and G. Durrieu (2024) <doi:10.1016/j.spl.2024.110078>.The nonparametric estimator using the discrete symmetric optimal kernel was illustrated on simulated data sets and a real-word data set included in the package, in comparison with two other discrete symmetric kernels.

r-kstmatrix 2.2-1
Propagated dependencies: r-tidyr@1.3.1 r-sets@1.0-25 r-rsvg@2.7.0 r-pks@0.6-1 r-diagrammersvg@0.1 r-diagrammer@1.0.11
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=kstMatrix
Licenses: GPL 3
Build system: r
Synopsis: Basic Functions in Knowledge Space Theory Using Matrix Representation
Description:

Knowledge space theory by Doignon and Falmagne (1999) <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical framework, which proposes mathematical formalisms to operationalize knowledge structures in a particular domain. The kstMatrix package provides basic functionalities to generate, handle, and manipulate knowledge structures and knowledge spaces. Opposed to the kst package, kstMatrix uses matrix representations for knowledge structures. Furthermore, kstMatrix contains several knowledge spaces developed by the research group around Cornelia Dowling through querying experts.

r-kzs 1.4.1
Propagated dependencies: r-lattice@0.22-7
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=kzs
Licenses: GPL 2+
Build system: r
Synopsis: Kolmogorov-Zurbenko Spatial Smoothing and Applications
Description:

This package provides a spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise.

r-kirby21-t1 1.8.0
Propagated dependencies: r-kirby21-base@1.7.3
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://www.nitrc.org/projects/multimodal/
Licenses: GPL 2
Build system: r
Synopsis: Example T1 Structural Data from the Multi-Modal MRI 'Reproducibility' Resource
Description:

Structural T1 magnetic resonance imaging ('MRI') data from the Kirby21 reproducibility study <doi:10.1016/j.neuroimage.2010.11.047>.

r-kuzco 0.1.0
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-rlang@1.1.6 r-ollamar@1.2.2 r-magick@2.9.0 r-jsonlite@2.0.0 r-imager@1.0.5 r-gtextras@0.6.2 r-gt@1.3.0 r-ellmer@0.4.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://frankiethull.github.io/kuzco/
Licenses: Expat
Build system: r
Synopsis: Computer Vision with Large Language Models
Description:

Make computer vision tasks approachable in R by leveraging Large Language Models. Providing fine-tuned prompts, boilerplate functions, and input/output helpers for common computer vision workflows, such as classifying and describing images. Functions are designed to take images as input and return structured data, helping users build practical applications with minimal code.

r-kitesquare 0.0.2
Propagated dependencies: r-tidyr@1.3.1 r-scales@1.4.0 r-rlang@1.1.6 r-ggplot2@4.0.1 r-ggh4x@0.3.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://github.com/HUGLeipzig/kitesquare
Licenses: LGPL 3+
Build system: r
Synopsis: Visualize Contingency Tables Using Kite-Square Plots
Description:

Create a kite-square plot for contingency tables using ggplot2', to display their relevant quantities in a single figure (marginal, conditional, expected, observed, chi-squared). The plot resembles a flying kite inside a square if the variables are independent, and deviates from this the more dependence exists.

r-kinematics 1.0.0
Propagated dependencies: r-numderiv@2016.8-1.1
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=kinematics
Licenses: Expat
Build system: r
Synopsis: Studying Sampled Trajectories
Description:

Allows analyzing time series representing two-dimensional movements. It accepts a data frame with a time (t), horizontal (x) and vertical (y) coordinate as columns, and returns several dynamical properties such as speed, acceleration or curvature.

r-kspm 0.2.1
Propagated dependencies: r-expm@1.0-0 r-deoptim@2.2-8 r-compquadform@1.4.4
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=KSPM
Licenses: GPL 3
Build system: r
Synopsis: Kernel Semi-Parametric Models
Description:

To fit the kernel semi-parametric model and its extensions. It allows multiple kernels and unlimited interactions in the same model. Coefficients are estimated by maximizing a penalized log-likelihood; penalization terms and hyperparameters are estimated by minimizing leave-one-out error. It includes predictions with confidence/prediction intervals, statistical tests for the significance of each kernel, a procedure for variable selection and graphical tools for diagnostics and interpretation of covariate effects. Currently it is implemented for continuous dependent variables. The package is based on the paper of Liu et al. (2007), <doi:10.1111/j.1541-0420.2007.00799.x>.

r-kgschart 1.3.5
Propagated dependencies: r-stringr@1.6.0 r-shiny@1.11.1 r-png@0.1-8 r-nnet@7.3-20 r-matrixstats@1.5.0 r-magrittr@2.0.4 r-gridextra@2.3 r-ggplot2@4.0.1 r-deepnet@0.2.1 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://github.com/kota7/kgschart
Licenses: Expat
Build system: r
Synopsis: KGS Rank Graph Parser
Description:

Restore underlining numeric data from rating history graph of KGS (an online platform of the game of go, <http://www.gokgs.com/>). A shiny application is also provided.

r-kindling 0.2.0
Propagated dependencies: r-vip@0.4.5 r-vctrs@0.6.5 r-tune@2.0.1 r-torch@0.16.3 r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-purrr@1.2.0 r-parsnip@1.3.3 r-neuralnettools@1.5.3 r-hardhat@1.4.2 r-glue@1.8.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-dials@1.4.2 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://kindling.joshuamarie.com
Licenses: Expat
Build system: r
Synopsis: Higher-Level Interface of 'torch' Package to Auto-Train Neural Networks
Description:

This package provides a higher-level interface to the torch package for defining, training, and fine-tuning neural networks, including its depth, powered by code generation. This package currently supports few to several architectures, namely feedforward (multi-layer perceptron) and recurrent neural networks (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)), while also reduces boilerplate torch code while enabling seamless integration with torch'. The model methods to train neural networks from this package also bridges to titanic ML frameworks in R, namely tidymodels ecosystem, which enables the parsnip model specifications, workflows, recipes, and tuning tools.

r-kere 1.0.0
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=KERE
Licenses: GPL 2
Build system: r
Synopsis: Expectile Regression in Reproducing Kernel Hilbert Space
Description:

An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space.

r-kgc 1.0.0.2
Propagated dependencies: r-shinythemes@1.2.0 r-shiny@1.11.1 r-plyr@1.8.9
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=kgc
Licenses: FreeBSD
Build system: r
Synopsis: Koeppen-Geiger Climatic Zones
Description:

Aids in identifying the Koeppen-Geiger (KG) climatic zone for a given location. The Koeppen-Geiger climate zones were first published in 1884, as a system to classify regions of the earth by their relative heat and humidity through the year, for the benefit of human health, plant and agriculture and other human activity [1]. This climate zone classification system, applicable to all of the earths surface, has continued to be developed by scientists up to the present day. Recently one of use (FZ) has published updated, higher accuracy KG climate zone definitions [2]. In this package we use these updated high-resolution maps as the data source [3]. We provide functions that return the KG climate zone for a given longitude and lattitude, or for a given United States zip code. In addition the CZUncertainty() function will check climate zones nearby to check if the given location is near a climate zone boundary. In addition an interactive shiny app is provided to define the KG climate zone for a given longitude and lattitude, or United States zip code. Digital data, as well as animated maps, showing the shift of the climate zones are provided on the following website <http://koeppen-geiger.vu-wien.ac.at>. This work was supported by the DOE-EERE SunShot award DE-EE-0007140. [1] W. Koeppen, (2011) <doi:10.1127/0941-2948/2011/105>. [2] F. Rubel and M. Kottek, (2010) <doi:10.1127/0941-2948/2010/0430>. [3] F. Rubel, K. Brugger, K. Haslinger, and I. Auer, (2016) <doi:10.1127/metz/2016/0816>.

r-kstatistics 2.1.1
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=kStatistics
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Unbiased Estimators for Cumulant Products and Faa Di Bruno's Formula
Description:

This package provides tools for estimate (joint) cumulants and (joint) products of cumulants of a random sample using (multivariate) k-statistics and (multivariate) polykays, unbiased estimators with minimum variance. Tools for generating univariate and multivariate Faa di Bruno's formula and related polynomials, such as Bell polynomials, generalized complete Bell polynomials, partition polynomials and generalized partition polynomials. For more details see Di Nardo E., Guarino G., Senato D. (2009) <arXiv:0807.5008>, <arXiv:1012.6008>.

r-kaigiroku 0.5
Propagated dependencies: r-tidyr@1.3.1 r-r-utils@2.13.0 r-jsonlite@2.0.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://github.com/amatsuo/kaigiroku
Licenses: GPL 3
Build system: r
Synopsis: Programmatic Access to the API for Japanese Diet Proceedings
Description:

Search and download data from the API for Japanese Diet Proceedings (see the reference at <https://kokkai.ndl.go.jp/api.html>).

r-krakenr 1.0.0
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-dplyr@1.1.4 r-anytime@0.3.12
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://github.com/nathanael-g-durst/KrakenR
Licenses: GPL 3+
Build system: r
Synopsis: Comprehensive R Interface for Accessing Kraken Cryptocurrency Exchange REST API
Description:

This package provides a comprehensive R interface to access data from the Kraken cryptocurrency exchange REST API <https://docs.kraken.com/api/>. It allows users to retrieve various market data, such as asset information, trading pairs, and price data. The package is designed to facilitate efficient data access for analysis, strategy development, and monitoring of cryptocurrency market trends.

r-kriginv 1.4.2
Propagated dependencies: r-rgenoud@5.9-0.11 r-randtoolbox@2.0.5 r-pbivnorm@0.6.0 r-mvtnorm@1.3-3 r-dicekriging@1.6.1 r-anmc@0.2.5
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://doi.org/10.1016/j.csda.2013.03.008
Licenses: GPL 3
Build system: r
Synopsis: Kriging-Based Inversion for Deterministic and Noisy Computer Experiments
Description:

Criteria and algorithms for sequentially estimating level sets of a multivariate numerical function, possibly observed with noise.

r-knotr 1.0-4
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=knotR
Licenses: GPL 2
Build system: r
Synopsis: Knot Diagrams using Bezier Curves
Description:

Makes visually pleasing diagrams of knot projections using optimized Bezier curves.

r-kdensity 1.1.1
Propagated dependencies: r-univariateml@1.5.0 r-eql@1.0-1 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://github.com/JonasMoss/kdensity
Licenses: Expat
Build system: r
Synopsis: Kernel Density Estimation with Parametric Starts and Asymmetric Kernels
Description:

Handles univariate non-parametric density estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density estimation with parametric starts involves fitting a parametric density to the data before making a correction with kernel density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>. Asymmetric kernels make kernel density estimation more efficient on bounded intervals such as (0, 1) and the positive half-line. Supported asymmetric kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>, the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>. User-supplied kernels, parametric starts, and bandwidths are supported.

r-kifidi 0.1.0
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=Kifidi
Licenses: GPL 3
Build system: r
Synopsis: Summary Table and Means Plots
Description:

Optimized for handling complex datasets in environmental and ecological research, this package offers functionality that is not fully met by general-purpose packages. It provides two key functions, summarize_data()', which summarizes datasets, and plot_means()', which creates plots with error bars. The plot_means() function incorporates error bars by default, allowing quick visualization of uncertainties, crucial in ecological studies. It also streamlines workflows for grouped datasets (e.g., by species or treatment), making it particularly user-friendly and reducing the complexity and time required for data summarization and visualization.

r-kyotil 2024.11-01
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=kyotil
Licenses: GPL 2+
Build system: r
Synopsis: Utility Functions for Statistical Analysis Report Generation and Monte Carlo Studies
Description:

Helper functions for creating formatted summary of regression models, writing publication-ready tables to latex files, and running Monte Carlo experiments.

r-kin-cohort 0.7
Propagated dependencies: r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=kin.cohort
Licenses: GPL 2+
Build system: r
Synopsis: Analysis of Kin-Cohort Studies
Description:

Analysis of kin-cohort studies. kin.cohort provides estimates of age-specific cumulative risk of a disease for carriers and noncarriers of a mutation. The cohorts are retrospectively built from relatives of probands for whom the genotype is known. Currently the method of moments and marginal maximum likelihood are implemented. Confidence intervals are calculated from bootstrap samples. Most of the code is a translation from previous MATLAB code by N. Chatterjee.

r-kssa 0.0.5
Propagated dependencies: r-zoo@1.8-14 r-rlang@1.1.6 r-missmethods@0.4.0 r-metrics@0.1.4 r-magrittr@2.0.4 r-imputets@3.4 r-ggplot2@4.0.1 r-forecast@8.24.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://github.com/steffenmoritz/kssa
Licenses: AGPL 3+
Build system: r
Synopsis: Known Sub-Sequence Algorithm
Description:

This package implements the Known Sub-Sequence Algorithm <doi:10.1016/j.aaf.2021.12.013>, which helps to automatically identify and validate the best method for missing data imputation in a time series. Supports the comparison of multiple state-of-the-art algorithms.

r-kendallknight 1.0.1
Propagated dependencies: r-cpp4r@0.4.0
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://pacha.dev/kendallknight/
Licenses: FSDG-compatible
Build system: r
Synopsis: Efficient Implementation of Kendall's Correlation Coefficient Computation
Description:

The computational complexity of the implemented algorithm for Kendall's correlation is O(n log(n)), which is faster than the base R implementation with a computational complexity of O(n^2). For small vectors (i.e., less than 100 observations), the time difference is negligible. However, for larger vectors, the speed difference can be substantial and the numerical difference is minimal. The references are Knight (1966) <doi:10.2307/2282833>, Abrevaya (1999) <doi:10.1016/S0165-1765(98)00255-9>, Christensen (2005) <doi:10.1007/BF02736122> and Emara (2024) <https://learningcpp.org/>. This implementation is described in Vargas Sepulveda (2025) <doi:10.1371/journal.pone.0326090>.

r-kuzur 0.2.3
Propagated dependencies: r-tidygraph@1.3.1 r-tibble@3.3.0 r-reticulate@1.44.1 r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://github.com/WickM/kuzuR
Licenses: Expat
Build system: r
Synopsis: Interface to 'kuzu' Graph Database
Description:

This package provides a high-performance R interface to the kuzu graph database. It uses the reticulate package to wrap the official Python client ('kuzu', pandas', and networkx'), allowing users to interact with kuzu seamlessly from within R'. Key features include managing database connections, executing Cypher queries, and efficiently loading data from R data frames. It also provides seamless integration with the R ecosystem by converting query results directly into popular R data structures, including tibble', igraph', tidygraph', and g6R objects, making kuzu's powerful graph computation capabilities readily available for data analysis and visualization workflows in R'. The kuzu documentation can be found at <https://kuzudb.github.io/docs/>.

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