Clustering typically assigns data points into discrete groups, but the clusters can sometimes be indistinct. Cluster sharpening adjusts an existing clustering to create contrast between groups. This package provides a general interface for cluster sharpening along with several implementations based on different excision criteria.

Provides tools for working with the Korea Standard Industrial Classification (KSIC). Includes datasets for the 9th, 10th, and 11th revisions. Functions include searching codes and names by keyword, converting codes across revisions, validating KSIC codes, and navigating the classification hierarchy (e.g., identifying parent or child categories). Intended for use in statistical analysis, data processing, and research involving South Korea’s industrial classification system.

Prediction with k* nearest neighbor algorithm 
    based on a publication by Anava and Levy (2016) <arXiv:1701.07266>.

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>.

Using this package you can combine known kinase substrate relationships with experimental data and determine active kinases and their substrates.

Implements the Known Sub-Sequence Algorithm <doi:10.1016/j.aaf.2021.12.013>, which helps to automatically identify and validate the best method for missing data imputation in a time series. Supports the comparison of multiple state-of-the-art algorithms.