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.

This package provides a set of functions designed to quickly generate results of a multiple choice test. Generates detailed global results, lists for anonymous feedback and personalised result feedback (in LaTeX and/or PDF format), as well as item statistics like Cronbach's alpha or disciminatory power. klausuR also includes a plugin for the R GUI and IDE RKWard, providing graphical dialogs for its basic features. The respective R package rkward cannot be installed directly from a repository, as it is a part of RKWard. To make full use of this feature, please install RKWard from <https://rkward.kde.org> (plugins are detected automatically). Due to some restrictions on CRAN, the full package sources are only available from the project homepage.

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.

This package provides a phenotype-aware algorithm for resolving cryptic relatedness in genetic studies. It removes related individuals based on kinship or identity-by-descent (IBD) scores while prioritizing subjects with phenotypes of interest. This approach helps maximize the retention of informative subjects, particularly for rare or valuable traits, and improves statistical power in genetic and epidemiological studies. KDPS supports both categorical and quantitative phenotypes, composite scoring, and customizable pruning strategies using a fuzziness parameter. Benchmark results show improved phenotype retention and high computational efficiency on large-scale datasets like the UK Biobank. Methods used include Manichaikul et al. (2010) <doi:10.1093/bioinformatics/btq559> for kinship estimation, Purcell et al. (2007) <doi:10.1086/519795> for IBD estimation, and Bycroft et al. (2018) <doi:10.1038/s41586-018-0579-z> for UK Biobank data reference.

This package provides a streamlined cross-referencing system for R Markdown documents generated with knitr'. R Markdown is an authoring format for generating dynamic content from R. kfigr provides a hook for anchoring code chunks and a function to cross-reference document elements generated from said chunks, e.g. figures and tables.

Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, deterministic or stochastic migration model. KISSMig runs on binary or quantitative suitability maps, which are pre-calculated with niche-based habitat suitability models (also called ecological niche models (ENMs) or species distribution models (SDMs)). Nobis & Normand (2014), <doi:10.1111/ecog.00930>.

This package implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence. This package is described in Fong et al. (2015) <DOI:10.1093/biostatistics/kxu056>.

The goal of kronos is to provide an easy-to-use framework to analyse circadian or otherwise rhythmic data using the familiar R linear modelling syntax, while taking care of the trigonometry under the hood.

This package provides a user-friendly interface for interacting with the District Health Information Software 2 ('DHIS2', <https://dhis2.org>) instance. It streamlines data retrieval, empowering researchers, analysts, and healthcare professionals to obtain and utilize data efficiently.

This package provides a weighting approach that employs kernels to make one group have a similar distribution to another group on covariates. This method matches not only means or marginal distributions but also higher-order transformations implied by the choice of kernel. kbal is applicable to both treatment effect estimation and survey reweighting problems. Based on Hazlett, C. (2020) "Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects." Statistica Sinica. <https://www.researchgate.net/publication/299013953_Kernel_Balancing_A_flexible_non-parametric_weighting_procedure_for_estimating_causal_effects>.

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.

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.

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>.

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 obtained in the 1990s by the research group around Cornelia Dowling through querying experts.

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.

This package provides a clustering algorithm similar to K-Means is implemented, it has two main advantages, namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when there are atypical values in the sample and (b) The estimator is efficient, roughly speaking, if there are no outliers in the sample, results will be similar to those obtained by a classic algorithm (K-Means). Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale. (see Gonzalez, Yohai and Zamar (2019) <arxiv:1906.08198>).

Predicts any variable in any categorical dataset for given values of predictor variables. If a dataset contains 4 variables, then any variable can be predicted based on the values of the other three variables given by the user. The user can upload their own datasets and select what variable they want to predict. A handsontable is provided to enter the predictor values and also accuracy of the prediction is also shown.

Implementations of the kernel measure of multi-sample dissimilarity (KMD) between several samples using K-nearest neighbor graphs and minimum spanning trees. The KMD measures the dissimilarity between multiple samples, based on the observations from them. It converges to the population quantity (depending on the kernel) which is between 0 and 1. A small value indicates the multiple samples are from the same distribution, and a large value indicates the corresponding distributions are different. The population quantity is 0 if and only if all distributions are the same, and 1 if and only if all distributions are mutually singular. The package also implements the tests based on KMD for H0: the M distributions are equal against H1: not all the distributions are equal. Both permutation test and asymptotic test are available. These tests are consistent against all alternatives where at least two samples have different distributions. For more details on KMD and the associated tests, see Huang, Z. and B. Sen (2024) <doi:10.1080/01621459.2023.2298036>.

Algorithms of distance-based k-medoids clustering: simple and fast k-medoids, ranked k-medoids, and increasing number of clusters in k-medoids. Calculate distances for mixed variable data such as Gower, Podani, Wishart, Huang, Harikumar-PV, and Ahmad-Dey. Cluster validation applies internal and relative criteria. The internal criteria includes silhouette index and shadow values. The relative criterium applies bootstrap procedure producing a heatmap with a flexible reordering matrix algorithm such as complete, ward, or average linkages. The cluster result can be plotted in a marked barplot or pca biplot.

This package provides a seamless bridge between keras and the tidymodels frameworks. It allows for the dynamic creation of parsnip model specifications for keras models.

Collection of utility functions used in the KEHRA project (see http://www.brunel.ac.uk/ife/britishcouncil). It refers to the multidimensional analysis of air pollution, weather and health data.

Comparative evaluation of families and candidate variants in rare-variant association studies. The package can be used for two methodologically overlapping but distinct purposes. First, the prior to any genetic or genomic evaluation, evaluation of relative detection power of pedigrees, can direct recruitment efforts by showing which individuals not yet sampled would be the most meaningful additions to a study. Second, after sequencing and analysis, variants based on association with disease status and familial relationships of individuals, aids in variant prioritization. Methodology is described in Nugent (2025) <doi:10.1101/2025.10.06.25337426>.

This package provides tools for applying Krippendorff's Alpha methodology <DOI:10.1080/19312450709336664>. Both the customary methodology and Hughes methodology <DOI:10.48550/arXiv.2210.13265> are supported, the former being preferred for larger datasets, the latter for smaller datasets. The framework supports common and user-defined distance functions, and can accommodate any number of units, any number of coders, and missingness. Interval estimation can be done in parallel for either methodology.

This package implements methods for clustering mixed-type data, specifically combinations of continuous and nominal data. Special attention is paid to the often-overlooked problem of equitably balancing the contribution of the continuous and categorical variables. This package implements KAMILA clustering, a novel method for clustering mixed-type data in the spirit of k-means clustering. It does not require dummy coding of variables, and is efficient enough to scale to rather large data sets. Also implemented is Modha-Spangler clustering, which uses a brute-force strategy to maximize the cluster separation simultaneously in the continuous and categorical variables. For more information, see Foss, Markatou, Ray, & Heching (2016) <doi:10.1007/s10994-016-5575-7> and Foss & Markatou (2018) <doi:10.18637/jss.v083.i13>.