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

This package provides tools to calculate the theoretical hydrodynamic response of an aquifer undergoing harmonic straining or pressurization, or analyze measured responses. There are two classes of models here, designed for use with confined aquifers: (1) for sealed wells, based on the model of Kitagawa et al (2011, <doi:10.1029/2010JB007794>), and (2) for open wells, based on the models of Cooper et al (1965, <doi:10.1029/JZ070i016p03915>), Hsieh et al (1987, <doi:10.1029/WR023i010p01824>), Rojstaczer (1988, <doi:10.1029/JB093iB11p13619>), Liu et al (1989, <doi:10.1029/JB094iB07p09453>), and Wang et al (2018, <doi:10.1029/2018WR022793>). Wang's solution is a special exception which allows for leakage out of the aquifer (semi-confined); it is equivalent to Hsieh's model when there is no leakage (the confined case). These models treat strain (or aquifer head) as an input to the physical system, and fluid-pressure (or water height) as the output. The applicable frequency band of these models is characteristic of seismic waves, atmospheric pressure fluctuations, and solid earth tides.

This is a C++ mutual information (MI) library based on the k-nearest neighbor (KNN) algorithm. There are three functions provided for computing MI for continuous values, mixed continuous and discrete values, and conditional MI for continuous values. They are based on algorithms by A. Kraskov, et. al. (2004) <doi:10.1103/PhysRevE.69.066138>, BC Ross (2014)<doi:10.1371/journal.pone.0087357>, and A. Tsimpiris (2012) <doi:10.1016/j.eswa.2012.05.014>, respectively.

Knowledge graphs enable to efficiently visualize and gain insights into large-scale data analysis results, as p-values from multiple studies or embedding data matrices. The usual workflow is a user providing a data frame of association studies results and specifying target nodes, e.g. phenotypes, to visualize. The knowledge graph then shows all the features which are significantly associated with the phenotype, with the edges being proportional to the association scores. As the user adds several target nodes and grouping information about the nodes such as biological pathways, the construction of such graphs soon becomes complex. The kgraph package aims to enable users to easily build such knowledge graphs, and provides two main features: first, to enable building a knowledge graph based on a data frame of concepts relationships, be it p-values or cosine similarities; second, to enable determining an appropriate cut-off on cosine similarities from a complete embedding matrix, to enable the building of a knowledge graph directly from an embedding matrix. The kgraph package provides several display, layout and cut-off options, and has already proven useful to researchers to enable them to visualize large sets of p-value associations with various phenotypes, and to quickly be able to visualize embedding results. Two example datasets are provided to demonstrate these behaviors, and several live shiny applications are hosted by the CELEHS laboratory and Parse Health, as the KESER Mental Health application <https://keser-mental-health.parse-health.org/> based on Hong C. (2021) <doi:10.1038/s41746-021-00519-z>.

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>.

Producing kernel estimates of the unconditional and conditional hazard function for right-censored data including methods of bandwidth selection.

Offers a graphical user interface for the evaluation of inter-rater agreement with Cohen's and Fleiss Kappa. The calculation of kappa statistics is done using the R package irr', so that KappaGUI is essentially a Shiny front-end for irr'.

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>).

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.

This package provides a method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. kfino is a robust sequential algorithm allowing to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These are data points that differ significantly from other observations. ML (Maximization Likelihood) and EM (Expectation-Maximization algorithm) algorithms were implemented in kfino'. The method is described in full details in the following arXiv e-Print: <arXiv:2208.00961>.

Quality of life functions for interactive programming. Shortcuts for common combinations of functions or different default arguments. Not to be used in production level scripts, but useful for exploring and quickly manipulating data for easy analysis. Also imports a variety of packages to facilitate the installation of those imported packages on the host machine.

Estimation algorithms for Kullback-Leibler divergence between two probability distributions, based on one or two samples, and including uncertainty quantification. Distributions can be uni- or multivariate and continuous, discrete or mixed.

Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA() and KFPCA_reg(). Moreover, least square estimates of functional principal component scores are also provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <arXiv:2102.01286>. Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <doi:10.1016/j.jmva.2021.104864>.

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>.

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.

An R code with a GUI for microclimate time series, with an emphasis on underground environments. KarsTS provides linear and nonlinear methods, including recurrence analysis (Marwan et al. (2007) <doi:10.1016/j.physrep.2006.11.001>) and filling methods (Moffat et al. (2007) <doi:10.1016/j.agrformet.2007.08.011>), as well as tools to manipulate easily time series and gap sets.

Rcpp implementation of the multivariate Kalman filter for state space models that can handle missing values and exogenous data in the observation and state equations. There is also a function to handle time varying parameters. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.

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>.

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.

This package provides a multi-purpose and flexible k-meric enrichment analysis software. kmeRtone measures the enrichment of k-mers by comparing the population of k-mers in the case loci with a carefully devised internal negative control group, consisting of k-mers from regions close to, yet sufficiently distant from, the case loci to mitigate any potential sequencing bias. This method effectively captures both the local sequencing variations and broader sequence influences, while also correcting for potential biases, thereby ensuring more accurate analysis. The core functionality of kmeRtone is the SCORE() function, which calculates the susceptibility scores for k-mers in case and control regions. Case regions are defined by the genomic coordinates provided in a file by the user and the control regions can be constructed relative to the case regions or provided directly. The k-meric susceptibility scores are calculated by using a one-proportion z-statistic. kmeRtone is highly flexible by allowing users to also specify their target k-mer patterns and quantify the corresponding k-mer enrichment scores in the context of these patterns, allowing for a more comprehensive approach to understanding the functional implications of specific DNA sequences on a genomic scale (e.g., CT motifs upon UV radiation damage). Adib A. Abdullah, Patrick Pflughaupt, Claudia Feng, Aleksandr B. Sahakyan (2024) Bioinformatics (submitted).

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>.

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>.

Selection of k in k-means clustering based on Pham et al. paper ``Selection of k in k-means clustering''.

Used to create dynamic, interactive D3.js based parallel coordinates and principal component plots in R'. The plots make visualizing k-means or other clusters simple and informative.