Implementation of the KCMeans regression estimator studied by Wiemann (2023) <arXiv:2311.17021> for expectation function estimation conditional on categorical variables. Computation leverages the unconditional KMeans implementation in one dimension using dynamic programming algorithm of Wang and Song (2011) <doi:10.32614/RJ-2011-015>, allowing for global solutions in time polynomial in the number of observed categories.

Interface to Keras <https://keras.io>, a high-level neural networks API. Keras was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.

This package implements the Kidney Failure Risk Equation (KFRE; Tangri and colleagues (2011) <doi:10.1001/jama.2011.451>; Tangri and colleagues (2016) <doi:10.1001/jama.2015.18202>) to compute 2- and 5-year kidney failure risk using 4-, 6-, and 8-variable models. Includes helpers to append risk columns to data frames, classify chronic kidney disease (CKD) stages and end-stage renal disease (ESRD) outcomes, and evaluate and plot model performance.

Tests the homogeneity of intraclass kappa statistics obtained from independent studies or a stratified study with binary results. It is desired to compare the kappa statistics obtained in multi-center studies or in a single stratified study to give a common or summary kappa using all available information. If the homogeneity test of these kappa statistics is not rejected, then it is possible to make inferences over a single kappa statistic that summarizes all the studies. Muammer Albayrak, Kemal Turhan, Yasemin Yavuz, Zeliha Aydin Kasap (2019) <doi:10.1080/03610918.2018.1538457> Jun-mo Nam (2003) <doi:10.1111/j.0006-341X.2003.00118.x> Jun-mo Nam (2005) <doi:10.1002/sim.2321>Mousumi Banerjee, Michelle Capozzoli, Laura McSweeney,Debajyoti Sinha (1999) <doi:10.2307/3315487> Allan Donner, Michael Eliasziw, Neil Klar (1996) <doi:10.2307/2533154>.

Sequences encoding by using the chaos game representation. Löchel et al. (2019) <doi:10.1093/bioinformatics/btz493>.

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 (2022) <arXiv:2210.00634>.

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.

Kernel Machine Score Test for Pathway Analysis in the Presence of Semi-Competing Risks. Method is detailed in: Neykov, Hejblum & Sinnott (2018) <doi: 10.1177/0962280216653427>.

New kernel-based test and fast tests for detecting change-points or changed-intervals where the distributions abruptly change. They work well particularly for high-dimensional data. Song, H. and Chen, H. (2022) <arXiv:2206.01853>.

This package provides methods for inference about/under complex relationships using peak height data from DNA mixtures: the most basic example would be testing whether a contributor to a mixture is the father of a child of known genotype. This provides most of the functionality of the KinMix package, but with some loss of efficiency and restriction on problem size, as the latter uses RHugin as the Bayes net engine, while this package uses gRain'. The package implements the methods introduced in Green, P. J. and Mortera, J. (2017) <doi:10.1016/j.fsigen.2017.02.001> and Green, P. J. and Mortera, J. (2021) <doi:10.1111/rssc.12498>.

This package implements the kernel method of test equating as defined in von Davier, A. A., Holland, P. W. and Thayer, D. T. (2004) <doi:10.1007/b97446> and Andersson, B. and Wiberg, M. (2017) <doi:10.1007/s11336-016-9528-7> using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting Gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.

Kernel smoothing for Wishart random matrices described in Daayeb, Khardani and Ouimet (2025) <doi:10.48550/arXiv.2506.08816>, Gaussian and log-Gaussian models using least square or likelihood cross validation criteria for optimal bandwidth selection.

Read Swiss time series data from the KOF Data API, <https://datenservice.kof.ethz.ch>. The API provides macro economic time series data mostly about Switzerland. The package itself is a set of wrappers around the KOF Datenservice API. The kofdata package is able to consume public information as well as data that requires an API token.

Implementations several algorithms for kernel k-means. The default OTQT algorithm is a fast alternative to standard implementations of kernel k-means, particularly in cases with many clusters. For a small number of clusters, the implemented MacQueen method typically performs the fastest. For more details and performance evaluations, see Berlinski and Maitra (2025) <doi:10.1002/sam.70032>.

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.

Assists researchers in choosing Key Opinion Leaders (KOLs) in a network to help disseminate or encourage adoption of an innovation by other network members. Potential KOL teams are evaluated using the ABCDE framework (Neal et al., 2025 <doi:10.31219/osf.io/3vxy9_v1>). This framework which considers: (1) the team members Availability, (2) the Breadth of the team's network coverage, (3) the Cost of recruiting a team of a given size, and (4) the Diversity of the team's members, (5) which are pooled into a single Evaluation score.

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.

The number of clusters (k) is needed to start all the partitioning clustering algorithms. An optimal value of this input argument is widely determined by using some internal validity indices. Since most of the existing internal indices suggest a k value which is computed from the clustering results after several runs of a clustering algorithm they are computationally expensive. On the contrary, the package kpeaks enables to estimate k before running any clustering algorithm. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in a data set.

This package implements k-means like blockmodeling of one-mode and linked networks as presented in Žiberna (2020) <doi:10.1016/j.socnet.2019.10.006>. The development of this package is financially supported by the Slovenian Research Agency (<https://www.arrs.si/>) within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).

This package provides useful functions which are needed for bioinformatic analysis such as calculating linear principal components from numeric data and Single-nucleotide polymorphism (SNP) dataset, calculating fixation index (Fst) using Hudson method, creating scatter plots in 3 views, handling with PLINK binary file format, detecting rough structures and outliers using unsupervised clustering, and calculating matrix multiplication in the faster way for big data.

This package provides data for Kaya identity variables (population, gross domestic product, primary energy consumption, and energy-related CO2 emissions) for the world and for individual nations, and utility functions for looking up data, plotting trends of Kaya variables, and plotting the fuel mix for a given country or region. The Kaya identity (Yoichi Kaya and Keiichi Yokobori, "Environment, Energy, and Economy: Strategies for Sustainability" (United Nations University Press, 1998) and <https://en.wikipedia.org/wiki/Kaya_identity>) expresses a nation's or region's greenhouse gas emissions in terms of its population, per-capita Gross Domestic Product, the energy intensity of its economy, and the carbon-intensity of its energy supply.

This package provides functions for analysing eye tracking data, including event detection, visualizations and area of interest (AOI) based analyses. The package includes implementations of the IV-T, I-DT, adaptive velocity threshold, and Identification by two means clustering (I2MC) algorithms. See separate documentation for each function. The principles underlying I-VT and I-DT algorithms are described in Salvucci & Goldberg (2000) <doi:10.1145/355017.355028>. Two-means clustering is described in Hessels et al. (2017), <doi: 10.3758/s13428-016-0822-1>. The adaptive velocity threshold algorithm is described in Nyström & Holmqvist (2010),<doi:10.3758/BRM.42.1.188>. A documentation of the kollaR can be found in Kleberg et al (2026) <doi:10.3758/s13428-025-02903-z>. Cite this paper when using kollaR See a demonstration in the URL.

An implementation of a simple and highly optimized ordinary kriging algorithm to plot geographical data.

Computes Khattree-Bahuguna's univariate and multivariate skewness, principal-component-based Khattree-Bahuguna's multivariate skewness. It also provides several measures of univariate or multivariate skewnesses including, Pearsonâ s coefficient of skewness, Bowleyâ s univariate skewness and Mardia's multivariate skewness. See Khattree, R. and Bahuguna, M. (2019) <doi: 10.1007/s41060-018-0106-1>.