This package provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) <DOI:10.1002/sim.7577>.

Use of Knock Out and Round Robin Techniques in preparing tournament fixtures as discussed in the Book Health and Physical Education by Dr. V K Sharma'(2018,ISBN:978-93-5272-134-4).

This package provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.

Implementation of Sequential BATTing (bootstrapping and aggregating of thresholds from trees) for developing threshold-based multivariate (prognostic/predictive) biomarker signatures. Variable selection is automatically built-in. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output. Detail algorithms are described in Huang et al (2017) <doi:10.1002/sim.7236>.

Inter-rater reliability analysis for binary classification tasks involving two or more raters within a signal detection-theoretic framework. User-supplied rating data are standardised into a common long-format structure. The package automatically computes Cohen's kappa for two raters and Fleiss kappa for multiple raters. When ground-truth labels are available, rater-specific hit rates, false-alarm rates, sensitivity, specificity, and decision thresholds are estimated from observed classification responses using standard signal detection-theoretic transformations (DeCarlo, 1998) <doi:10.1037/1082-989X.3.2.186>. The package implements the Strategic Convergence Index (SCI; Gianeselli, 2026) <doi:10.1177/00131644261417643>, defined as SCI = 1 - [Var(t_i) / Var_max], where Var(t_i) denotes the variance of rater-specific decision thresholds and Var_max denotes the reference variance under maximal threshold dispersion. SCI quantifies convergence in rater decision criteria beyond observed agreement alone and complements classical agreement coefficients by distinguishing agreement in observed categorical outcomes from convergence in latent decision thresholds under an explicit signal detection-theoretic model of categorical judgment. The package provides structured summaries and threshold-based diagnostics for applications in which similar agreement coefficients may reflect substantively different underlying decision criteria across raters.

Extends the functionality of R serialization by augmenting the built-in reference hook system. This enhanced implementation allows optimal, one-pass integrated serialization that combines R serialization with third-party serialization methods. Facilitates the serialization of even complex R objects, which contain non-system reference objects, such as those accessed via external pointers, for use in parallel and distributed computing.

Stepwise models for the optimal linear combination of continuous variables in binary classification problems under Youden Index optimisation. Information on the models implemented can be found at Aznar-Gimeno et al. (2021) <doi:10.3390/math9192497>.

The strip function deletes components of R model outputs that are useless for specific purposes, such as predict[ing], print[ing], summary[izing], etc.

Efficient R interface to the Cancer Intervention and Surveillance Modeling Network (CISNET) Smoking History Generator microsimulation engine, which synthesizes individual smoking histories (initiation, cessation, intensity) and ages at death from calibrated initiation, cessation, cigarettes-per-day, and mortality tables. The wrapper exposes fixed-cohort and population data-frame simulation, multi-threaded segmentation, reproducible pseudo-random streams (L'Ecuyer RngStream MRG32k3a or Matsumoto--Nishimura Mersenne Twister), legacy CLI-style configuration files, and portable YAML configuration save/load with optional split smoking and mortality parameter bundles. Methods follow Jeon et al. (2012) <doi:10.1111/j.1539-6924.2011.01775.x>. Random number generators: Matsumoto and Nishimura (1998) <doi:10.1145/272991.272995>; L'Ecuyer (1999) <doi:10.1287/opre.47.1.159>; L'Ecuyer et al. (2002) <doi:10.1287/opre.50.6.1073.358>.

Analysis of seed germination data using the physiological time modelling approach. Includes functions to fit hydrotime and thermal-time models with the traditional approaches of Bradford (1990) <doi:10.1104/pp.94.2.840> and Garcia-Huidobro (1982) <doi:10.1093/jxb/33.2.288>. Allows to fit models to grouped datasets, i.e. datasets containing multiple species, seedlots or experiments.

Soil health assessment builds information to improve decision in soil management. It facilitates assessment of soil conditions for crop suitability [such as those given by FAO <https://www.fao.org/land-water/databases-and-software/crop-information/en/>], groundwater recharge, fertility, erosion, salinization [<doi:10.1002/ldr.4211>], carbon sequestration, irrigation potential, and status of soil resources.

This package contains tests for association between a set of genetic variants and multiple correlated outcomes that are interval censored. Interval-censored data arises when the exact time of the onset of an outcome of interest is unknown but known to fall between two time points.

Evolutionary reconstruction based on substitutions and insertion-deletion (indels) analyses in a distance-based framework as described in Muñoz-Pajares (2013) <doi:10.1111/2041-210X.12118>.

This package provides a powerful, easy to use syntax for specifying and estimating complex Structural Equation Models. Models can be estimated using Partial Least Squares Path Modeling or Covariance-Based Structural Equation Modeling or covariance based Confirmatory Factor Analysis (Ray, Danks, and Valdez 2021 <doi:10.2139/ssrn.3900621>).

This package provides functions that automate accessing, downloading and exploring Soil Moisture and Ocean Salinity (SMOS) Level 4 (L4) data developed by Barcelona Expert Center (BEC). Particularly, it includes functions to search for, acquire, extract, and plot BEC-SMOS L4 soil moisture data downscaled to ~1 km spatial resolution. Note that SMOS is one of Earth Explorer Opportunity missions by the European Space Agency (ESA). More information about SMOS products can be found at <https://earth.esa.int/eogateway/missions/smos/data>.

Identification of sets of objects with shared features is a common operation in all disciplines. Analysis of intersections among multiple sets is fundamental for in-depth understanding of their complex relationships. This package implements a theoretical framework for efficient computation of statistical distributions of multi-set intersections based upon combinatorial theory, and provides multiple scalable techniques for visualizing the intersection statistics. The statistical algorithm behind this package was published in Wang et al. (2015) <doi:10.1038/srep16923>.

This package provides a consistent interface for constructing commonly used spatial covariates from polygon data. Computes polygon areas, distances to reference features, point and line intersection counts, line lengths within polygons, polygon overlap areas and shares, and raster zonal summaries. Handles coordinate reference system validation, geometry repair, unit conversion, row preservation, and standardised missing value semantics while relying on established spatial libraries for the underlying geometry operations.

Detects change points in long univariate time series using the SCAN framework. The implementation uses a native Rust backend exposed to R via extendr'.

Decompose a time series into seasonal, trend, and remainder components using an implementation of Seasonal Decomposition of Time Series by Loess (STL) that provides several enhancements over the STL method in the stats package. These enhancements include handling missing values, providing higher order (quadratic) loess smoothing with automated parameter choices, frequency component smoothing beyond the seasonal and trend components, and some basic plot methods for diagnostics.

This package provides methods to detect structural changes in time series or random fields (spatial data). Focus is on the detection of abrupt changes or trends in independent data, but the package also provides a function to de-correlate data with dependence. The functions are based on the test suggested in Schmidt (2024) <DOI:10.3150/23-BEJ1686> and the work in Görz and Fried (2025) <DOI:10.48550/arXiv.2512.11599>.

Settings and functions to extend the knitr SAS engine.

R language bindings for SolveBio's API. SolveBio is a biomedical knowledge hub that enables life science organizations to collect and harmonize the complex, disparate "multi-omic" data essential for today's R&D and BI needs.

This package provides functions for creating and manipulating 12-tone (i.e., dodecaphonic) musical matrices using Arnold Schoenberg's (1923) serialism technique. This package can generate random 12-tone matrices and can generate matrices using a pre-determined sequence of notes.

Fits univariate and multivariate spatio-temporal random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley and Banerjee <doi:10.1016/j.envsoft.2019.104608>.