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

Implementation of Discrete Symmetric Optimal Kernel for estimating count data distributions, as described by T. Senga Kiessé and G. Durrieu (2024) <doi:10.1016/j.spl.2024.110078>.The nonparametric estimator using the discrete symmetric optimal kernel was illustrated on simulated data sets and a real-word data set included in the package, in comparison with two other discrete symmetric kernels.

The format KVH is a lightweight format that can be read/written both by humans and machines. It can be useful in situations where XML or alike formats seem to be an overkill. We provide an ability to parse KVH files in R pretty fast due to Rcpp use.

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

Online, Semi-online, and Offline K-medians algorithms are given. For both methods, the algorithms can be initialized randomly or with the help of a robust hierarchical clustering. The number of clusters can be selected with the help of a penalized criterion. We provide functions to provide robust clustering. Function gen_K() enables to generate a sample of data following a contaminated Gaussian mixture. Functions Kmedians() and Kmeans() consists in a K-median and a K-means algorithms while Kplot() enables to produce graph for both methods. Cardot, H., Cenac, P. and Zitt, P-A. (2013). "Efficient and fast estimation of the geometric median in Hilbert spaces with an averaged stochastic gradient algorithm". Bernoulli, 19, 18-43. <doi:10.3150/11-BEJ390>. Cardot, H. and Godichon-Baggioni, A. (2017). "Fast Estimation of the Median Covariation Matrix with Application to Online Robust Principal Components Analysis". Test, 26(3), 461-480 <doi:10.1007/s11749-016-0519-x>. Godichon-Baggioni, A. and Surendran, S. "A penalized criterion for selecting the number of clusters for K-medians" <arXiv:2209.03597> Vardi, Y. and Zhang, C.-H. (2000). "The multivariate L1-median and associated data depth". Proc. Natl. Acad. Sci. USA, 97(4):1423-1426. <doi:10.1073/pnas.97.4.1423>.

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 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>.

An implementation of the k-means-- algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means-- : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", <doi:10.1137/1.9781611972832.21> and using ordering described by Howe, 2013 in the thesis, Clustering and anomaly detection in tropical cyclones". Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space.

Wait for a single key press at the R prompt. This works in terminals, but does not currently work in the Windows GUI', the OS X GUI ('R.app'), in Emacs ESS', in an Emacs shell buffer or in R Studio'. In these cases keypress stops with an error message.

Miscellaneous functions for classification and visualization, e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, an interface to svmlight and stepclass() wrapper variable selection for supervised classification, partimat() visualization of classification rules and shardsplot() of cluster results as well as kmodes() clustering for categorical data, corclust() variable clustering, variable extraction from different variable clustering models and weight of evidence preprocessing.

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.

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>.

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 is a stochastic framework that combines biochemical reaction networks with extended Kalman filter and Rauch-Tung-Striebel smoothing. This framework allows to investigate the dynamics of cell differentiation from high-dimensional clonal tracking data subject to measurement noise, false negative errors, and systematically unobserved cell types. Our tool can provide statistical support to biologists in gene therapy clonal tracking studies for a deeper understanding of clonal reconstitution dynamics. Further details on the methods can be found in L. Del Core et al., (2022) <doi:10.1101/2022.07.08.499353>.

This package provides a fast and computationally efficient algorithm designed to enable researchers to efficiently and quickly extract semantically-related keywords using a fitted embedding model. For more details about the methods applied, see Chester (2025). <doi:10.17605/OSF.IO/5B7RQ>.

Convert an R Markdown documents into an .xlsx spreadsheet reports with the knitxl() function, which works similarly to knit() from the knitr package. The generated report can be opened in Excel or similar software for further analysis and presentation.

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.

Software for k-means clustering of partially observed data from Chi, Chi, and Baraniuk (2016) <doi:10.1080/00031305.2015.1086685>.

This package provides a wrapper for querying WISKI databases via the KiWIS REST API. WISKI is an SQL relational database used for the collection and storage of water data developed by KISTERS and KiWIS is a REST service that provides access to WISKI databases via HTTP requests (<https://www.kisters.eu/water-weather-and-environment/>). Contains a list of default databases (called hubs') and also allows users to provide their own KiWIS URL. Supports the entire query process- from metadata to specific time series values. All data is returned as tidy tibbles.

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 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.

This package provides the function to calculate the kernel-lasso expansion, Z-score, and max-min-scale standardization.It can increase the dimension of existed dataset and remove abundant features by lasso. Z Dai, L Jiayi, T Gong, C Wang (2021) <doi:10.1088/1742-6596/1955/1/012047>.

This package performs variable selection for many types of L1-regularised regressions using the revisited knockoffs procedure. This procedure uses a matrix of knockoffs of the covariates independent from the response variable Y. The idea is to determine if a covariate belongs to the model depending on whether it enters the model before or after its knockoff. The procedure suits for a wide range of regressions with various types of response variables. Regression models available are exported from the R packages glmnet and ordinalNet'. Based on the paper linked to via the URL below: Gegout A., Gueudin A., Karmann C. (2019) <arXiv:1907.03153>.

Miscellaneous functions and data used in psychological research and teaching. Keng currently has a built-in dataset depress, and could (1) scale a vector; (2) compute the cut-off values of Pearson's r with known sample size; (3) test the significance and compute the post-hoc power for Pearson's r with known sample size; (4) conduct a priori power analysis and plan the sample size for Pearson's r; (5) compare lm()'s fitted outputs using R-squared, f_squared, post-hoc power, and PRE (Proportional Reduction in Error, also called partial R-squared or partial Eta-squared); (6) calculate PRE from partial correlation, Cohen's f, or f_squared; (7) conduct a priori power analysis and plan the sample size for one or a set of predictors in regression analysis; (8) conduct post-hoc power analysis for one or a set of predictors in regression analysis with known sample size; (9) randomly pick numbers for Chinese Super Lotto and Double Color Balls; (10) assess course objective achievement in Outcome-Based Education.