This package provides a toolkit for creating, importing, validating, enriching, rendering, and depositing Key Resources Tables (KRTs). A KRT lists the resources used and generated in a study (antibodies, cell lines, organisms, chemicals, software, datasets, protocols, and more), each paired with a persistent identifier such as a Research Resource Identifier (RRID), a Digital Object Identifier (DOI), a repository accession, or a catalog number, so that resources are unambiguously identifiable and machine-actionable. The package models resources as typed, validated records around a neutral core schema and maps them to journal or funder output profiles, following the FAIR (Findable, Accessible, Interoperable, Reusable) principles of Wilkinson et al. (2016) <doi:10.1038/sdata.2016.18>. It normalizes and optionally resolves identifiers against public registries, extracts resources from manuscripts, and renders tables both in the STAR (Structured, Transparent, Accessible Reporting) Methods style used by Cell Press journals and in the style required by ASAP (Aligning Science Across Parkinson's), with an emphasis on transparency, reproducibility, and correct per-component licensing.

This package provides a streamlined cross-referencing system for R Markdown documents generated with knitr'. R Markdown is an authoring format for generating dynamic content from R. kfigr provides a hook for anchoring code chunks and a function to cross-reference document elements generated from said chunks, e.g. figures and tables.

Optimized for handling complex datasets in environmental and ecological research, this package offers functionality that is not fully met by general-purpose packages. It provides two key functions, summarize_data()', which summarizes datasets, and plot_means()', which creates plots with error bars. The plot_means() function incorporates error bars by default, allowing quick visualization of uncertainties, crucial in ecological studies. It also streamlines workflows for grouped datasets (e.g., by species or treatment), making it particularly user-friendly and reducing the complexity and time required for data summarization and visualization.

Retrieve data from kintone (<https://www.kintone.com/>) via its API. kintone is an enterprise application platform.

New kernel-based test and fast tests for testing whether two samples are from the same distribution. They work well particularly for high-dimensional data. Song, H. and Chen, H. (2023) <arXiv:2011.06127>.

This package implements estimation procedures for Autoregressive Distributed Lag (ARDL) and Nonlinear ARDL (NARDL) models, which allow researchers to investigate both short- and long-run relationships in time series data under mixed orders of integration. The package supports simultaneous modeling of symmetric and asymmetric regressors, flexible treatment of short-run and long-run asymmetries, and automated equation handling. It includes several cointegration testing approaches such as the Pesaran-Shin-Smith F and t bounds tests, and narayan test. Methodological foundations are provided in Pesaran, Shin, and Smith (2001) <doi:10.1016/S0304-4076(01)00049-5> and Shin, Yu, and Greenwood-Nimmo (2014, ISBN:9780123855079).

Computes and fits a heavy-tailed Student-t Naive Bayes classifier for non-stationary financial market regime analysis (Clock of Regimes, COR). The core innovation is a profile grid search over the degrees-of-freedom parameter nu that prevents numerical underflow and structural classification failures when identifying fat-tailed Stress regimes. Provides S3 methods for fitting, prediction, summarising, plotting, and parameter extraction.

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/>.

Criteria and algorithms for sequentially estimating level sets of a multivariate numerical function, possibly observed with noise.

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 <doi:10.48550/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.

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>.

The official TIMSS 2023 website provides the School Context Data Files for TIMSS 2023 Grade 4 in rdata format. However, the available data are presented solely in the form of numerical values. This package aims to transform the numerical data into categorical data, thereby enabling clearer interpretation and reducing ambiguity in statistical data analysis. Furthermore, the category labels are presented in Bahasa Indonesia. This initiative is intended as a contribution to promoting and expanding the use of Bahasa Indonesia in the field of programming, in line with its designation as one of the official languages of the United Nations General Assembly.

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>.

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.

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.

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.

This package infers relative kinase activity from phosphoproteomics data using the method described by Casado et al. (2013) <doi:10.1126/scisignal.2003573>.

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 implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y = f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014, <doi:10.1093/pan/mpt019>).

Color schemes ready for each type of data (qualitative, diverging or sequential), with colors that are distinct for all people, including color-blind readers. This package provides an implementation of Paul Tol (2018) and Fabio Crameri (2018) <doi:10.5194/gmd-11-2541-2018> color schemes for use with graphics or ggplot2'. It provides tools to simulate color-blindness and to test how well the colors of any palette are identifiable. Several scientific thematic schemes (geologic timescale, land cover, FAO soils, etc.) are also implemented.

Adds support for the English language to the koRpus package. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please consider subscribing to the koRpus-dev mailing list (<https://korpusml.reaktanz.de>).

An implementation of the blocking algorithm KLSH in Steorts, Ventura, Sadinle, Fienberg (2014) <DOI:10.1007/978-3-319-11257-2_20>, which is a k-means variant of locality sensitive hashing. The method is illustrated with examples and a vignette.

This package provides methods for selecting the optimal bandwidth in kernel density estimation for dependent samples, such as those generated by Markov chain Monte Carlo (MCMC). Implements a modified biased cross-validation (mBCV) approach that accounts for sample dependence, improving the accuracy of estimated density functions.

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.