Provide routines for filtering and smoothing, forecasting, sampling and Bayesian analysis of Dynamic Generalized Linear Models using the methodology described in Alves et al. (2024)<doi:10.48550/arXiv.2201.05387> and dos Santos Jr. et al. (2024)<doi:10.48550/arXiv.2403.13069>.

Dynamic Mode Decomposition (DMD) with Koopman operator theory extensions, powered by a Rust backend via extendr'. Provides standard DMD as described in Schmid (2010) <doi:10.1017/S0022112010001217>, DMD with control for forced linear systems following Proctor, Brunton, and Kutz (2016) <doi:10.1137/15M1013857>, Extended DMD with lifting functions, Hankel-DMD via time-delay embedding, Generalized Laplace Analysis for direct eigenfunction computation, and harmonic time averages and mesochronic harmonic plots for phase space analysis as developed in Mezic (2020) <doi:10.48550/arXiv.2009.05883>. Includes built-in area-preserving and chaotic maps for experimentation.

Estimate and forecast Bayesian simultaneous equation models for macroeconomic time series. Provides tools to specify systems of behavioral equations and accounting identities, transform and manage time series, simulate from the posterior using a Metropolis-within-Gibbs sampler, and generate unconditional and conditional forecasts with user-defined priors and restrictions. Methods are described in Rathke A. and Sarferaz S. (forthcoming) "Bayesian Estimation of Simultaneous Equations Model".

This package provides a novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination. Reference: Wang and Zou (2018) <doi:10.1111/rssb.12244>.

The official Trends in International Mathematics and Science Study (TIMSS) 2023 website provides Teacher Context Data Files for Grade 4 in RData format. However, the available data are presented solely as numerical values. This package transforms the numerical data into categorical variables, enabling clearer interpretation and reducing ambiguity in statistical analysis. The category labels are provided in Bahasa Indonesia. This initiative contributes to promoting the use of Bahasa Indonesia in programming, in line with its designation as one of the official languages of the United Nations. For more details see <https://timss2023.org/>.

This package provides a comprehensive set of geostatistical, visual, and analytical methods, in conjunction with the expanded version of the acclaimed J.E. Klovan's mining dataset, are included in klovan'. This makes the package an excellent learning resource for Principal Component Analysis (PCA), Factor Analysis (FA), kriging, and other geostatistical techniques. Originally published in the 1976 book Geological Factor Analysis', the included mining dataset was assembled by Professor J. E. Klovan of the University of Calgary. Being one of the first applications of FA in the geosciences, this dataset has significant historical importance. As a well-regarded and published dataset, it is an excellent resource for demonstrating the capabilities of PCA, FA, kriging, and other geostatistical techniques in geosciences. For those interested in these methods, the klovan datasets provide a valuable and illustrative resource. Note that some methods require the RGeostats package. Please refer to the README or Additional_repositories for installation instructions. This material is based upon research in the Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3-COE), and supported by the Department of Energy's National Nuclear Security Administration under Award Number DE-NA0004104.

Matches a data set with semi-structured address data, e.g., street and house number as a concatenated string, wrongly spelled street names or non-existing house numbers to a reference index. The methods are specifically designed for German municipalities ('KOR'-community) and German address schemes.

k Nearest Neighbors with variable selection, combine grid search and forward selection to achieve variable selection in order to improve k Nearest Neighbors predictive performance.

Makes visually pleasing diagrams of knot projections using optimized Bezier curves.

This package provides a progress bar similar to dplyr that can write progress out to a variety of locations, including stdout(), stderr(), or from file(). Useful when using knitr or rmarkdown', and you still want to see progress of calculations in the terminal.

This package provides tools for keeping track of information, named "keys", about rows of data frame like objects. This is done by creating special attribute "keys" which is updated after every change in rows (subsetting, ordering, etc.). This package is designed to work tightly with dplyr package.

The official Trends in International Mathematics and Science Study (TIMSS) 2023 website provides Home Context Data Files for Grade 4 in RData format. However, the available data are presented solely as numerical values. This package transforms the numerical data into categorical variables, enabling clearer interpretation and reducing ambiguity in statistical analysis. The category labels are provided in Bahasa Indonesia. This initiative contributes to promoting the use of Bahasa Indonesia in programming, in line with its designation as one of the official languages of the United Nations. For more details see <https://timss2023.org/>.

The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) <arXiv:1202.3878> is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, <doi:10.1016/j.ins.2018.03.010>). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0.

This package implements Bayesian polynomial chaos expansion methods for surrogate modeling, uncertainty quantification, and sensitivity analysis. Includes sparse regression-based approaches and adaptive Bayesian models based on reversible-jump Markov chain Monte Carlo. Optional screening and basis-enrichment strategies are provided to improve scalability in moderate to high dimensions.

This package implements the Kidney Failure Risk Equation (KFRE; Tangri and colleagues (2011) <doi:10.1001/jama.2011.451>; Tangri and colleagues (2016) <doi:10.1001/jama.2015.18202>) to compute 2- and 5-year kidney failure risk using 4-, 6-, and 8-variable models. Includes helpers to append risk columns to data frames, classify chronic kidney disease (CKD) stages and end-stage renal disease (ESRD) outcomes, and evaluate and plot model performance.

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>.

Comparative evaluation of families and candidate variants in rare-variant association studies. The package can be used for two methodologically overlapping but distinct purposes. First, the prior to any genetic or genomic evaluation, evaluation of relative detection power of pedigrees, can direct recruitment efforts by showing which individuals not yet sampled would be the most meaningful additions to a study. Second, after sequencing and analysis, variants based on association with disease status and familial relationships of individuals, aids in variant prioritization. Methodology is described in Nugent (2025) <doi:10.1101/2025.10.06.25337426>.

Estimates kriging models for geographical point-referenced data. Method is described in Gill (2020) <doi:10.1177/1532440020930197>.

Kernel functions for diverse types of data (including, but not restricted to: nonnegative and real vectors, real matrices, categorical and ordinal variables, sets, strings), plus other utilities like kernel similarity, kernel Principal Components Analysis (PCA) and features importance for Support Vector Machines (SVMs), which expand other R packages like kernlab'.

This function performs the two-sample Kuiper test to assess the anomaly of continuous, one-dimensional probability distributions. References used for this method are (1). Kuiper, N. H. (1960). <DOI:10.1016/S1385-7258(60)50006-0> and (2). Paltani, S. (2004). <DOI:10.1051/0004-6361:20034220>.

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.

Helps make implicit data assumptions explicit by attaching keys to flat-file data that error when those assumptions are violated. Designed for CSV-first workflows without database infrastructure or version control. Provides key definition, assumption checks, join diagnostics, and automatic drift detection via watched data frames that snapshot before each transformation and report cell-level changes.

This package provides a self-guided, weakly supervised learning algorithm for feature extraction from noisy and high-dimensional data. It facilitates the identification of patterns that reflect underlying group structures across all samples in a dataset. The method incorporates a novel strategy to integrate spatial information, improving the clarity of results in spatially resolved data.

The goal of kronos is to provide an easy-to-use framework to analyse circadian or otherwise rhythmic data using the familiar R linear modelling syntax, while taking care of the trigonometry under the hood.