This package provides functions to implement K Nearest Neighbor forecasting using a weighted similarity metric tailored to the problem of forecasting univariate time series where recent observations, seasonal patterns, and exogenous predictors are all relevant in predicting future observations of the series in question. For more information on the formulation of this similarity metric please see Trupiano (2021) <arXiv:2112.06266>.

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.

Calculate nine types of coefficients of determination (R-squared) based on the classification by Kvalseth (1985) <doi:10.1080/00031305.1985.10479448>. This package is designed for educational purposes to demonstrate how R-squared values can fluctuate depending on the choice of formula, particularly in power regression models or linear models without an intercept. By providing a comprehensive list of definitions, it helps users understand the mathematical sensitivity of goodness-of-fit indices.

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

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.

The kernelized version of principal component analysis (KPCA) has proven to be a valid nonlinear alternative for tackling the nonlinearity of biological sample spaces. However, it poses new challenges in terms of the interpretability of the original variables. kpcaIG aims to provide a tool to select the most relevant variables based on the kernel PCA representation of the data as in Briscik et al. (2023) <doi:10.1186/s12859-023-05404-y>. It also includes functions for 2D and 3D visualization of the original variables (as arrows) into the kernel principal components axes, highlighting the contribution of the most important ones.

Real-time quantitative polymerase chain reaction (qPCR) data sets by Karlen et al. (2007) <doi:10.1186/1471-2105-8-131>. Provides one single tabular tidy data set in long format, encompassing 32 dilution series, for seven PCR targets and four biological samples. The targeted amplicons are within the murine genes: Cav1, Ccn2, Eln, Fn1, Rpl27, Hspg2, and Serpine1, respectively. Dilution series: scheme 1 (Cav1, Eln, Hspg2, Serpine1): 1-fold, 10-fold, 50-fold, and 100-fold; scheme 2 (Ccn2, Rpl27, Fn1): 1-fold, 10-fold, 50-fold, 100-fold and 1000-fold. For each concentration there are five replicates, except for the 1000-fold concentration, where only two replicates were performed. Each amplification curve is 40 cycles long. Original raw data file is Additional file 2 from "Statistical significance of quantitative PCR" by Y. Karlen, A. McNair, S. Perseguers, C. Mazza, and N. Mermod (2007) <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-8-131/MediaObjects/12859_2006_1503_MOESM2_ESM.ZIP>.

This package provides a shiny app to visualize the knowledge networks for the code concepts. Using co-occurrence matrices of EHR codes from Veterans Affairs (VA) and Massachusetts General Brigham (MGB), the knowledge extraction via sparse embedding regression (KESER) algorithm was used to construct knowledge networks for the code concepts. Background and details about the method can be found at Chuan et al. (2021) <doi:10.1038/s41746-021-00519-z>.

Density, distribution function, quantile function and random generation for the K-distribution. A plotting function that plots data on Weibull paper and another function to draw additional lines. See results from package in T Lamont-Smith (2018), submitted J. R. Stat. Soc.

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.

Data on houses in and around Seattle WA are included. Basic characteristics are given along with sale prices.

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.

Attempts to remove vocals from a stereo .wav recording of a song.

Computes the Kantorovich distance between two probability measures on a finite set. The Kantorovich distance is also known as the Monge-Kantorovich distance or the first Wasserstein distance.

The King's Health Questionnaire (KHQ) is a disease-specific, self-administered questionnaire designed specific to assess the impact of Urinary Incontinence (UI) on Quality of Life. The questionnaire was developed by Kelleher and collaborators (1997) <doi:10.1111/j.1471-0528.1997.tb11006.x>. It is a simple, acceptable and reliable measure to use in the clinical setting and a research tool that is useful in evaluating UI treatment outcomes. The KHQ five dimensions (KHQ5D) is a condition-specific preference-based measure developed by Brazier and collaborators (2008) <doi:10.1177/0272989X07301820>. Although not as popular as the SF6D <doi:10.1016/S0895-4356(98)00103-6> and EQ-5D <https://euroqol.org/>, the KHQ5D measures health-related quality of life (HRQoL) specifically for UI, not general conditions like the others two instruments mentioned. The KHQ5D ca be used in the clinical and economic evaluation of health care. The subject self-rates their health in terms of five dimensions: Role Limitation (RL), Physical Limitations (PL), Social Limitations (SL), Emotions (E), and Sleep (S). Frequently the states on these five dimensions are converted to a single utility index using country specific value sets, which can be used in the clinical and economic evaluation of health care as well as in population health surveys. This package provides methods to calculate scores for each dimension of the KHQ; converts KHQ item scores to KHQ5D scores; and also calculates the utility index of the KHQ5D.

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

Analysis of DNA copy number in single cells using custom genome-wide targeted DNA sequencing panels for the Mission Bio Tapestri platform. Users can easily parse, manipulate, and visualize datasets produced from the automated Tapestri Pipeline', with support for normalization, clustering, and copy number calling. Functions are also available to deconvolute multiplexed samples by genotype and parsing barcoded reads from exogenous lentiviral constructs.

The developed function is designed to facilitate the seamless conversion of KML (Keyhole Markup Language) files to Shapefiles while preserving attribute values. It provides a straightforward interface for users to effortlessly import KML data, extract relevant attributes, and export them into the widely compatible Shapefile format. The package ensures accurate representation of spatial data while maintaining the integrity of associated attribute information. For details see, Flores, G. (2021). <DOI:10.1007/978-3-030-63665-4_15>. Whether for spatial analysis, visualization, or data interoperability, it simplifies the conversion process and empowers users to seamlessly work with geospatial datasets.

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

This package provides functions to search, retrieve, apply and update classification standards and code lists using Statistics Norway's API <https://www.ssb.no/klass> from the system KLASS'. Retrieves classifications by date with options to choose language, hierarchical level and formatting.

This package contains kidney care oriented functions. Current version contains functions for calculation of: - Estimated glomerular filtration rate by CKD-EPI (2021 and 2009), MDRD, CKiD, FAS, EKFC, etc. - Kidney Donor Risk Index and Kidney Donor Profile Index for kidney transplant donors. - Citation: Bikbov B. kidney.epi: Kidney-Related Functions for Clinical and Epidemiological Research. Scientific-Tools.Org, <https://Scientific-Tools.Org>. <doi:10.32614/CRAN.package.kidney.epi>.

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>.

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>.

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.