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

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.

Smoothing techniques and computing bandwidth selectors of the nth derivative of a probability density for one-dimensional data (described in Arsalane Chouaib Guidoum (2020) <arXiv:2012.06102> [stat.CO]).

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>.

Computes group centrality scores and identifies the most central group of players in a network.

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

Detect and test for changes in covariance structures of functional data, as well as changepoint detection for multivariate data more generally. Method for detecting non-stationarity in resting state functional Magnetic Resonance Imaging (fMRI) scans as seen in Ramsay, K., & Chenouri, S. (2025) <doi:10.1080/10485252.2025.2503891> is implemented in fmri_changepoints(). Also includes depth- and rank-based implementation of the wild binary segmentation algorithm for detecting multiple changepoints in multivariate data.

This package provides functions for analysing eye tracking data, including event detection, visualizations and area of interest (AOI) based analyses. The package includes implementations of the IV-T, I-DT, adaptive velocity threshold, and Identification by two means clustering (I2MC) algorithms. See separate documentation for each function. The principles underlying I-VT and I-DT algorithms are described in Salvucci & Goldberg (2000) <doi:10.1145/355017.355028>. Two-means clustering is described in Hessels et al. (2017), <doi: 10.3758/s13428-016-0822-1>. The adaptive velocity threshold algorithm is described in Nyström & Holmqvist (2010),<doi:10.3758/BRM.42.1.188>. A documentation of the kollaR can be found in Kleberg et al (2026) <doi:10.3758/s13428-025-02903-z>. Cite this paper when using kollaR See a demonstration in the URL.

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>.

This package provides utilities for Kokudo Suuchi', the GIS data service of the Japanese government. See <https://nlftp.mlit.go.jp/index.html> for more information.

Routines to handle family data with a pedigree object. The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. Also includes a tool for pedigree drawing which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the pedigree object with various criteria, and kinship for the X chromosome.

Understanding the current status of forest resources is essential for monitoring changes in forest ecosystems and generating related statistics. In South Korea, the National Forest Inventory (NFI) surveys over 4,500 sample plots nationwide every five years and records 70 items, including forest stand, forest resource, and forest vegetation surveys. Many researchers use NFI as the primary data for research, such as biomass estimation or analyzing the importance value of each species over time and space, depending on the research purpose. However, the large volume of accumulated forest survey data from across the country can make it challenging to manage and utilize such a vast dataset. To address this issue, we developed an R package that efficiently handles large-scale NFI data across time and space. The package offers a comprehensive workflow for NFI data analysis. It starts with data processing, where read_nfi() function reconstructs NFI data according to the researcher's needs while performing basic integrity checks for data quality.Following this, the package provides analytical tools that operate on the verified data. These include functions like summary_nfi() for summary statistics, diversity_nfi() for biodiversity analysis, iv_nfi() for calculating species importance value, and biomass_nfi() and cwd_biomass_nfi() for biomass estimation. Finally, for visualization, the tsvis_nfi() function generates graphs and maps, allowing users to visualize forest ecosystem changes across various spatial and temporal scales. This integrated approach and its specialized functions can enhance the efficiency of processing and analyzing NFI data, providing researchers with insights into forest ecosystems. The NFI Excel files (.xlsx) are not included in the R package and must be downloaded separately. Users can access these NFI Excel files by visiting the Korea Forest Service Forestry Statistics Platform <https://kfss.forest.go.kr/stat/ptl/article/articleList.do?curMenu=11694&bbsId=microdataboard> to download the annual NFI Excel files, which are bundled in .zip archives. Please note that this website is only available in Korean, and direct download links can be found in the notes section of the read_nfi() function.

Extends RT-QuIC (Real-Time Quaking-Induced Conversion) statistical analysis to complex environmental matrices through hierarchical adaptive classification. KWELA is named after a deity of the Fore people of Papua New Guinea, among whom Kuru, a notable human prion disease, was identified. Implements a 6-layer architecture: hard gate biological constraints, per-well adaptive scoring, separation-aware combination, Youden-optimized cutoffs, replicate consensus, and matrix instability detection. Features dual-mode operation (diagnostic/research), auto-profile selection (Standard/Sensitive/Matrix-Robust), RAF integration for artifact detection, matrix-aware baseline correction, and multiple consensus rules. Methods include energy distance (Szekely and Rizzo (2013) <doi:10.1016/j.jspi.2013.03.018>), CRPS (Gneiting and Raftery (2007) <doi:10.1198/016214506000001437>), SSMD (Zhang (2007) <doi:10.1016/j.ygeno.2007.01.005>), and Jensen-Shannon divergence (Lin (1991) <doi:10.1109/18.61115>). This package implements methodology currently under peer review; please contact the author before publication using this approach. Development followed an iterative human-machine collaboration where all algorithmic design, statistical methodologies, and biological validation logic were conceptualized, tested, and iteratively refined by Richard A. Feiss through repeated cycles of running experimental data, evaluating analytical outputs, and selecting among candidate algorithms and approaches. AI systems ('Anthropic Claude and OpenAI GPT') served as coding assistants and analytical sounding boards under continuous human direction. The selection of statistical methods, evaluation of biological plausibility, and all final methodology decisions were made by the human author. AI systems did not independently originate algorithms, statistical approaches, or scientific methodologies.

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.

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

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>.

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>.

This package provides a method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. kfino is a robust sequential algorithm allowing to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These are data points that differ significantly from other observations. ML (Maximization Likelihood) and EM (Expectation-Maximization algorithm) algorithms were implemented in kfino'. The method is described in full details in the following arXiv e-Print: <arXiv:2208.00961>.

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 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, the Banerjee error correction test, and the restricted ECM test, together with diagnostic tools including Wald tests for asymmetry, ARCH tests, and stability procedures (CUSUM and CUSUMQ). 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).

The K-sample omnibus non-proportional hazards (KONP) tests are powerful non-parametric tests for comparing K (>=2) hazard functions based on right-censored data (Gorfine, Schlesinger and Hsu, 2020, <doi:10.1177/0962280220907355>). These tests are consistent against any differences between the hazard functions of the groups. The KONP tests are often more powerful than other existing tests, especially under non-proportional hazard functions.

This package provides data for Kaya identity variables (population, gross domestic product, primary energy consumption, and energy-related CO2 emissions) for the world and for individual nations, and utility functions for looking up data, plotting trends of Kaya variables, and plotting the fuel mix for a given country or region. The Kaya identity (Yoichi Kaya and Keiichi Yokobori, "Environment, Energy, and Economy: Strategies for Sustainability" (United Nations University Press, 1998) and <https://en.wikipedia.org/wiki/Kaya_identity>) expresses a nation's or region's greenhouse gas emissions in terms of its population, per-capita Gross Domestic Product, the energy intensity of its economy, and the carbon-intensity of its energy supply.

Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.