This package implements univariate gamma frailty regression models for survival data with six different baseline distributions: the Arvind distribution (Pandey et al., 2024), the Lindley distribution (Lindley, 1958), the Linear Failure Rate distribution (Bain, 1974), the Power Xgamma distribution (Tyagi et al., 2022), the Modified Topp-Leone distribution (Singh et al., 2025), and the Power Failure Rate distribution (Mugdadi, 2005). The package supports uncensored (complete) and censored data (right, left, interval, and progressive censoring) with and without covariates. It provides maximum likelihood estimation, standard errors, confidence intervals, t-statistics, p-values, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), a bootstrap approximation of the Widely Applicable Information Criterion (WAIC), k-fold cross-validation, variance inflation factors, R-squared, adjusted R-squared, Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), an overall model F-test, frailty variance estimation, survival probabilities at user-specified time points, median survival, expected survival within a fixed window, risk predictions, marginal predictions, martingale and deviance residuals, standardized and studentized residuals, leverage values, Cook's distance, Difference in Fits (DFFITS), Difference in Betas (DFBETAS), and a comprehensive suite of diagnostic and survival plots including Kaplan-Meier overlays and coefficient forest plots. Random number generation is available for each baseline distribution and the full frailty model, and a simulation study function evaluates parameter recovery across sample sizes and censoring scenarios. References are Lindley (1958) <doi:10.1111/j.2517-6161.1958.tb00278.x>, Mugdadi (2005) <doi:10.1016/j.amc.2004.09.064>, Bain (1974) <doi:10.1080/00401706.1974.10489237>, Singh, Tyagi, Singh, and Tyagi (2025) <https://ph02.tci-thaijo.org/index.php/thaistat/article/view/257215>, Pandey, Singh, Tyagi, and Tyagi (2024) <https://ssca.org.in/journal.html>, and Tyagi, Kumar, Pandey, Saha, and Bagariya (2022) <https://ijsreg.com/>.

This package provides tools for efficient processing of large, whole genome genotype data sets in variant call format (VCF). It includes several functions to calculate commonly used population genomic metrics and a method for reference panel free genotype imputation, which is described in the preprint Gurke & Mayer (2024) <doi:10.22541/au.172515591.10119928/v1>.

This package provides implementation of the generic composite similarity measure (GCSM) described in Liu et al. (2020) <doi:10.1016/j.ecoinf.2020.101169>. The implementation is in C++ and uses RcppArmadillo'. Additionally, implementations of the structural similarity (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), are included.

This package provides a ggplot2 extension that allows text to follow curved paths. Curved text makes it easier to directly label paths or neatly annotate in polar co-ordinates.

Implementation of several generalized F-statistics. The current version includes a generalized F-statistic based on the flexible isotonic/monotonic regression or order restricted hypothesis testing. Based on: Y. Lai (2011) <doi:10.1371/journal.pone.0019754>.

Visualise overlapping time series lines as a heatmap of line density. Provides a ggplot2 statistic implementing the DenseLines algorithm, which "normalizes time series by the arc length to compute accurate densities" (Moritz and Fisher, 2018) <doi:10.48550/arXiv.1808.06019>.

Shiny application for the analysis of groundwater monitoring data, designed to work with simple time-series data for solute concentration and ground water elevation, but can also plot non-aqueous phase liquid (NAPL) thickness if required. Also provides the import of a site basemap in GIS shapefile format.

Data sets and scripts used in the book Generalized Additive Models: An Introduction with R', Wood (2006,2017) CRC.

An implementation of a new Gini covariance and correlation to measure dependence between a categorical and numerical variables. Dang, X., Nguyen, D., Chen, Y. and Zhang, J., (2018) <arXiv:1809.09793>.

Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network structure, potentially with exogenous variables. Such models are described in Knight et al. (2020) <doi:10.18637/jss.v096.i05> and Nason and Wei (2021) <doi:10.1111/rssa.12875>. Diagnostic tools for GNAR(X) models can be found in Nason et al. (2023) <doi:10.48550/arXiv.2312.00530>.

This package provides a Chernoff face geom for ggplot2'. Maps multivariate data to human-like faces. Inspired by Chernoff (1973) <doi:10.1080/01621459.1973.10482434>.

This package provides a compilation of tools to complete common tasks for studying gerrymandering. This focuses on the geographic tool side of common problems, such as linking different levels of spatial units or estimating how to break up units. Functions exist for creating redistricting-focused data for the US.

Basic functions for plotting 2D and 3D views of a sphere, by default the Earth with its major coastline, and additional lines and points.

Collect marketing data from Google Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

Likelihood-based boosting approaches for generalized mixed models are provided.

River hydrograph separation and daily runoff time series analysis. Provides various filters to separate baseflow and quickflow. Implements advanced separation technique by Rets et al. (2022) <doi:10.1134/S0097807822010146> which involves meteorological data to reveal genetic components of the runoff: ground, rain, thaw and spring (seasonal thaw). High-performance C++17 computation, annually aggregated variables, statistical testing and numerous plotting functions for high-quality visualization.

Calculates grey level co-occurrence matrix (GLCM) based texture measures (Hall-Beyer (2017) <https://prism.ucalgary.ca/bitstream/handle/1880/51900/texture%20tutorial%20v%203_0%20180206.pdf>; Haralick et al. (1973) <doi:10.1109/TSMC.1973.4309314>) of raster layers using a sliding rectangular window. It also includes functions to quantize a raster into grey levels as well as tabulate a glcm and calculate glcm texture metrics for a matrix.

Draw geospatial objects by clicks on the map. This packages can help data analyst who want to check their own geospatial hypothesis but has no ready-made geospatial objects.

Simplifies the creation, management, and updating of local databases using data extracted from Google Earth Engine ('GEE'). It integrates with GEE to store, aggregate, and process spatio-temporal data, leveraging SQLite for efficient, serverless storage. The geeLite package provides utilities for data transformation and supports real-time monitoring and analysis of geospatial features, making it suitable for researchers and practitioners in geospatial science. For details, see Kurbucz and Andrée (2025) "Building and Managing Local Databases from Google Earth Engine with the geeLite R Package" <https://hdl.handle.net/10986/43165>.

This package provides flexible tools for the visualization of genomic data. Supports interactive and static plots tailored for presentations and publications, with customizable features like colors, themes, and annotations to align with specific analytical and presentation goals.

Fit a regression model for when the response variable is presented as a ratio or proportion. This adjustment can occur globally, with the same estimate for the entire study space, or locally, where a beta regression model is fitted for each region, considering only influential locations for that area. Da Silva, A. R. and Lima, A. O. (2017) <doi:10.1016/j.spasta.2017.07.011>.

This package provides a range of filters that can be applied to layers from the ggplot2 package and its extensions, along with other graphic elements such as guides and theme elements. The filters are applied at render time and thus uses the exact pixel dimensions needed.

Algebra of operations for blending, copying, adjusting, and compositing layers in ggplot2'. Supports copying and adjusting the aesthetics or parameters of an existing layer, partitioning a layer into multiple pieces for re-composition, applying affine transformations to layers, and combining layers (or partitions of layers) using blend modes (including commutative blend modes, like multiply and darken). Blend mode support is particularly useful for creating plots with overlapping groups where the layer drawing order does not change the output; see Kindlmann and Scheidegger (2014) <doi:10.1109/TVCG.2014.2346325>.

Spatio-temporal causal inference based on point process data. You provide the raw data of locations and timings of treatment and outcome events, specify counterfactual scenarios, and the package estimates causal effects over specified spatial and temporal windows. See Papadogeorgou, et al. (2022) <doi:10.1111/rssb.12548> and Mukaigawara, et al. (2024) <doi:10.31219/osf.io/5kc6f>.