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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-ksnn 0.1.2
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=ksNN
Licenses: GPL 2+
Build system: r
Synopsis: K* Nearest Neighbors Algorithm
Description:

Prediction with k* nearest neighbor algorithm based on a publication by Anava and Levy (2016) <arXiv:1701.07266>.

r-ksa 0.1.0
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=KSA
Licenses: GPL 2
Build system: r
Synopsis: Retained Component Criterion for Principal Component Analysis
Description:

The Retained Component Criterion for Principal Component Analysis (RCC_PCA) is a tool to determine the optimal number of components to retain in PCA.

r-kernelknn 1.1.6
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://github.com/mlampros/KernelKnn
Licenses: Expat
Build system: r
Synopsis: Kernel k Nearest Neighbors
Description:

Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of RcppArmadillo to speed up the calculation of distances between observations.

r-kpiwidget 0.1.1
Propagated dependencies: r-htmlwidgets@1.6.4 r-crosstalk@1.2.2
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://arnold-kakas.github.io/kpiwidget/
Licenses: Expat
Build system: r
Synopsis: KPI Widgets for Quarto Dashboards with Crosstalk
Description:

This package provides an easy way to create interactive KPI (key performance indicator) widgets for Quarto dashboards using Crosstalk'. The package enables visualization of key metrics in a structured format, supporting interactive filtering and linking with other Crosstalk'-enabled components. Designed for use in Quarto Dashboards.

r-knnvs 0.1.0
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=kNNvs
Licenses: GPL 3
Build system: r
Synopsis: k Nearest Neighbors with Grid Search Variable Selection
Description:

k Nearest Neighbors with variable selection, combine grid search and forward selection to achieve variable selection in order to improve k Nearest Neighbors predictive performance.

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-kedd 1.0.4
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://gitlab.com/iagogv/kedd
Licenses: GPL 2+
Build system: r
Synopsis: Kernel Estimator and Bandwidth Selection for Density and Its Derivatives
Description:

Smoothing techniques and computing bandwidth selectors of the nth derivative of a probability density for one-dimensional data (described in Arsalane Chouaib Guidoum (2020) <arXiv:2012.06102> [stat.CO]).

r-kmi 0.5.5
Propagated dependencies: r-survival@3.8-3 r-mitools@2.4
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://github.com/aallignol/kmi
Licenses: GPL 2+
Build system: r
Synopsis: Kaplan-Meier Multiple Imputation for the Analysis of Cumulative Incidence Functions in the Competing Risks Setting
Description:

This package performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions (Allignol and Beyersmann, 2010 <doi:10.1093/biostatistics/kxq018>).

r-keyperm 0.1.1
Propagated dependencies: r-tm@0.7-16 r-slam@0.1-55 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=keyperm
Licenses: GPL 2+
Build system: r
Synopsis: Keyword Analysis Using Permutation Tests
Description:

Efficient implementation of permutation tests for keyword analysis in corpus linguistics as described in Mildenberger (2023) <arXiv:2308.13383>.

r-knowbr 2.2
Propagated dependencies: r-vegan@2.7-2 r-sp@2.2-0 r-plotrix@3.8-13 r-mgcv@1.9-4 r-fossil@0.4.0
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=KnowBR
Licenses: GPL 2+
Build system: r
Synopsis: Discriminating Well Surveyed Spatial Units from Exhaustive Biodiversity Databases
Description:

It uses species accumulation curves and diverse estimators to assess, at the same time, the levels of survey coverage in multiple geographic cells of a size defined by the user or polygons. It also enables the geographical depiction of observed species richness, survey effort and completeness values including a background with administrative areas.

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-ktweedie 1.0.3
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=ktweedie
Licenses: GPL 3
Build system: r
Synopsis: 'Tweedie' Compound Poisson Model in the Reproducing Kernel Hilbert Space
Description:

Kernel-based Tweedie compound Poisson gamma model using high-dimensional predictors for the analyses of zero-inflated response variables. The package features built-in estimation, prediction and cross-validation tools and supports choice of different kernel functions. For more details, please see Yi Lian, Archer Yi Yang, Boxiang Wang, Peng Shi & Robert William Platt (2023) <doi:10.1080/00401706.2022.2156615>.

r-kcop 1.0.0
Propagated dependencies: r-orthopolynom@1.0-6.1 r-gtools@3.9.5 r-dplyr@1.1.4 r-copula@1.1-7
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=Kcop
Licenses: GPL 3+
Build system: r
Synopsis: Smooth Test for Equality of Copulas and Clustering Multivariate
Description:

This package implements approaches of non-parametric smooth test to compare simultaneously K(K>1) copulas and non-parametric clustering of multivariate populations with arbitrary sizes. See Yves I. Ngounou Bakam and Denys Pommeret (2022) <arXiv:2112.05623> and Yves I. Ngounou Bakam and Denys Pommeret (2022) <arXiv:2211.06338>.

r-knockoff 0.3.6
Propagated dependencies: r-rspectra@0.16-2 r-rdsdp@1.0.6 r-matrix@1.7-4 r-gtools@3.9.5 r-glmnet@4.1-10 r-corpcor@1.6.10
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://web.stanford.edu/group/candes/knockoffs/index.html
Licenses: GPL 3
Build system: r
Synopsis: The Knockoff Filter for Controlled Variable Selection
Description:

The knockoff filter is a general procedure for controlling the false discovery rate (FDR) when performing variable selection. For more information, see the website below and the accompanying paper: Candes et al., "Panning for gold: model-X knockoffs for high-dimensional controlled variable selection", J. R. Statist. Soc. B (2018) 80, 3, pp. 551-577.

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-kernstadapt 0.4.0
Propagated dependencies: r-spatstat-utils@3.2-0 r-spatstat-univar@3.1-5 r-spatstat-random@3.4-3 r-spatstat-linnet@3.3-2 r-spatstat-geom@3.6-1 r-spatstat-explore@3.6-0 r-sparr@2.3-16 r-misc3d@0.9-1
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=kernstadapt
Licenses: Expat
Build system: r
Synopsis: Adaptive Kernel Estimators for Point Process Intensities on Linear Networks
Description:

Adaptive estimation of the first-order intensity function of a spatio-temporal point process using kernels and variable bandwidths. The methodology used for estimation is presented in González and Moraga (2022). <doi:10.48550/arXiv.2208.12026>.

r-knfi 1.0.2
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://github.com/SYOUNG9836/knfi
Licenses: GPL 3
Build system: r
Synopsis: Analysis of Korean National Forest Inventory Database
Description:

Understanding the current status of forest resources is essential for monitoring changes in forest ecosystems and generating related statistics. In South Korea, the National Forest Inventory (NFI) surveys over 4,500 sample plots nationwide every five years and records 70 items, including forest stand, forest resource, and forest vegetation surveys. Many researchers use NFI as the primary data for research, such as biomass estimation or analyzing the importance value of each species over time and space, depending on the research purpose. However, the large volume of accumulated forest survey data from across the country can make it challenging to manage and utilize such a vast dataset. To address this issue, we developed an R package that efficiently handles large-scale NFI data across time and space. The package offers a comprehensive workflow for NFI data analysis. It starts with data processing, where read_nfi() function reconstructs NFI data according to the researcher's needs while performing basic integrity checks for data quality.Following this, the package provides analytical tools that operate on the verified data. These include functions like summary_nfi() for summary statistics, diversity_nfi() for biodiversity analysis, iv_nfi() for calculating species importance value, and biomass_nfi() and cwd_biomass_nfi() for biomass estimation. Finally, for visualization, the tsvis_nfi() function generates graphs and maps, allowing users to visualize forest ecosystem changes across various spatial and temporal scales. This integrated approach and its specialized functions can enhance the efficiency of processing and analyzing NFI data, providing researchers with insights into forest ecosystems. The NFI Excel files (.xlsx) are not included in the R package and must be downloaded separately. Users can access these NFI Excel files by visiting the Korea Forest Service Forestry Statistics Platform <https://kfss.forest.go.kr/stat/ptl/article/articleList.do?curMenu=11694&bbsId=microdataboard> to download the annual NFI Excel files, which are bundled in .zip archives. Please note that this website is only available in Korean, and direct download links can be found in the notes section of the read_nfi() function.

r-kstmatrix 2.3-0
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-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-kindisperse 0.10.2
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://github.com/moshejasper/kindisperse
Licenses: Expat
Build system: r
Synopsis: Simulate and Estimate Close-Kin Dispersal Kernels
Description:

This package provides functions for simulating and estimating kinship-related dispersal. Based on the methods described in M. Jasper, T.L. Schmidt., N.W. Ahmad, S.P. Sinkins & A.A. Hoffmann (2019) <doi:10.1111/1755-0998.13043> "A genomic approach to inferring kinship reveals limited intergenerational dispersal in the yellow fever mosquito". Assumes an additive variance model of dispersal in two dimensions, compatible with Wright's neighbourhood area. Simple and composite dispersal simulations are supplied, as well as the functions needed to estimate parent-offspring dispersal for simulated or empirical data, and to undertake sampling design for future field studies of dispersal. For ease of use an integrated Shiny app is also included.

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-kcprs 1.1.1
Propagated dependencies: r-roll@1.2.0 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=kcpRS
Licenses: GPL 2+
Build system: r
Synopsis: Kernel Change Point Detection on the Running Statistics
Description:

The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) <arXiv:1202.3878> is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, <doi:10.1016/j.ins.2018.03.010>). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0.

r-kqm 1.1.1
Propagated dependencies: r-mass@7.3-65 r-gtools@3.9.5 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=KQM
Licenses: GPL 2
Build system: r
Synopsis: K Quantiles Medoids (KQM) Clustering
Description:

K Quantiles Medoids (KQM) clustering applies quantiles to divide data of each dimension into K mean intervals. Combining quantiles of all the dimensions of the data and fully permuting quantiles on each dimension is the strategy to determine a pool of candidate initial cluster centers. To find the best initial cluster centers from the pool of candidate initial cluster centers, two methods based on quantile strategy and PAM strategy respectively are proposed. During a clustering process, medoids of clusters are used to update cluster centers in each iteration. Comparison between KQM and the method of randomly selecting initial cluster centers shows that KQM is almost always getting clustering results with smaller total sum squares of distances.

r-kappagold 0.4.0
Channel: guix-cran
Location: guix-cran/packages/k.scm (guix-cran packages k)
Home page: https://cran.r-project.org/package=kappaGold
Licenses: Expat
Build system: r
Synopsis: Agreement of Nominal Scale Raters (with a Gold Standard)
Description:

Estimate agreement of a group of raters with a gold standard rating on a nominal scale. For a single gold standard rater the average pairwise agreement of raters with this gold standard is provided. For a group of (gold standard) raters the approach of S. Vanbelle, A. Albert (2009) <doi:10.1007/s11336-009-9116-1> is implemented. Bias and standard error are estimated via delete-1 jackknife.

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Total results: 69112