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.

This package provides arrays with flexible control over dimension dropping when subscripting.

This package implements the vine copula based kernel density estimator of Nagler and Czado (2016) <doi:10.1016/j.jmva.2016.07.003>. The estimator does not suffer from the curse of dimensionality and is therefore well suited for high-dimensional applications.

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>.

Implementations of two empirical versions the kernel partial correlation (KPC) coefficient and the associated variable selection algorithms. KPC is a measure of the strength of conditional association between Y and Z given X, with X, Y, Z being random variables taking values in general topological spaces. As the name suggests, KPC is defined in terms of kernels on reproducing kernel Hilbert spaces (RKHSs). The population KPC is a deterministic number between 0 and 1; it is 0 if and only if Y is conditionally independent of Z given X, and it is 1 if and only if Y is a measurable function of Z and X. One empirical KPC estimator is based on geometric graphs, such as K-nearest neighbor graphs and minimum spanning trees, and is consistent under very weak conditions. The other empirical estimator, defined using conditional mean embeddings (CMEs) as used in the RKHS literature, is also consistent under suitable conditions. Using KPC, a stepwise forward variable selection algorithm KFOCI (using the graph based estimator of KPC) is provided, as well as a similar stepwise forward selection algorithm based on the RKHS based estimator. For more details on KPC, its empirical estimators and its application on variable selection, see Huang, Z., N. Deb, and B. Sen (2022). â Kernel partial correlation coefficient â a measure of conditional dependenceâ (URL listed below). When X is empty, KPC measures the unconditional dependence between Y and Z, which has been described in Deb, N., P. Ghosal, and B. Sen (2020), â Measuring association on topological spaces using kernels and geometric graphsâ <arXiv:2010.01768>, and it is implemented in the functions KMAc() and Klin() in this package. The latter can be computed in near linear time.

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 provides a toolkit for absolute and relative dating and analysis of chronological patterns. This package includes functions for chronological modeling and dating of archaeological assemblages from count data. It provides methods for matrix seriation. It also allows to compute time point estimates and density estimates of the occupation and duration of an archaeological site.

This is designed for use with an arbitrary set of equations with an arbitrary set of unknowns. The user selects "fixed" values for enough unknowns to leave as many variables as there are equations, which in most cases means the system is properly defined and a unique solution exists. The function, the fixed values and initial values for the remaining unknowns are fed to a nonlinear backsolver. The original version of "TK!Solver" , now a product of Universal Technical Systems (<https://www.uts.com>) was the inspiration for this function.

Evaluate specific panels in different aspects: i) Simulation tools related to pedigree researches; ii) calculation for systemic effectiveness indicators, such as probability of exclusion (PE).

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>.

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.

Decrypts passwords stored in the Gnome Keyring, macOS Keychain and strings encrypted with the Windows Data Protection API.

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>).

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.

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.

The Retained Component Criterion for Principal Component Analysis (RCC_PCA) is a tool to determine the optimal number of components to retain in PCA.

To fit the kernel semi-parametric model and its extensions. It allows multiple kernels and unlimited interactions in the same model. Coefficients are estimated by maximizing a penalized log-likelihood; penalization terms and hyperparameters are estimated by minimizing leave-one-out error. It includes predictions with confidence/prediction intervals, statistical tests for the significance of each kernel, a procedure for variable selection and graphical tools for diagnostics and interpretation of covariate effects. Currently it is implemented for continuous dependent variables. The package is based on the paper of Liu et al. (2007), <doi:10.1111/j.1541-0420.2007.00799.x>.

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.

Fits keyword assisted topic models (keyATM) using collapsed Gibbs samplers. The keyATM combines the latent dirichlet allocation (LDA) models with a small number of keywords selected by researchers in order to improve the interpretability and topic classification of the LDA. The keyATM can also incorporate covariates and directly model time trends. The keyATM is proposed in Eshima, Imai, and Sasaki (2024) <doi:10.1111/ajps.12779>.

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

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.

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

API Wrapper to use Korea Investment & Securities (KIS) trading system that provides various financial services like stock price check, orders and balance check <https://apiportal.koreainvestment.com/>.

Convert keys and other values to memorable phrases. Includes some methods to build lists of words.