Data sets utilized by the SGP package as exemplars for users to conduct their own student growth percentiles (SGP) analyses.

This package provides data and functions used in the book "Statistical Methods for Quantitative Finance" by David Ardia (2026).

This package provides a specialized selection algorithm designed to align simulated fire perimeters with specific fire size distribution scenarios. The foundation of this approach lies in generating a vast collection of plausible simulated fires across a wide range of conditions, assuming a random pattern of ignition. The algorithm then assembles individual fire perimeters based on their specific probabilities of occurrence, e.g., determined by (i) the likelihood of ignition and (ii) the probability of particular fire-weather scenarios, including wind speed and direction. Implements the method presented in Rodrigues (2025a) <doi:10.5194/egusphere-egu25-8974>. Demo data and code examples can be found in Rodrigues (2025b) <doi:10.5281/zenodo.15282605>.

Runs the applicable tests for a comparison in one call and returns standardised result tables. One-sample, two-group, multi-group, factorial and categorical workflows report parametric, rank-based and robust results side by side with effect sizes, confidence intervals and multiplicity-adjusted p-values. Supervised fits, embeddings, clustering and simulators follow the same result contracts. Methods include those of Welch (1947) <doi:10.1093/biomet/34.1-2.28>, Wilcoxon (1945) <doi:10.2307/3001968>, Mann and Whitney (1947) <doi:10.1214/aoms/1177730491>, Kruskal and Wallis (1952) <doi:10.1080/01621459.1952.10483441>, Friedman (1937) <doi:10.1080/01621459.1937.10503522>, Tukey (1949) <doi:10.2307/3001913>, Dunn (1964) <doi:10.1080/00401706.1964.10490181>, Yuen (1974) <doi:10.1093/biomet/61.1.165>, Brunner and Munzel (2000) <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U>, Algina, Keselman and Penfield (2005) <doi:10.1037/1082-989X.10.3.317>, DeLong, DeLong and Clarke-Pearson (1988) <doi:10.2307/2531595>, Sun and Xu (2014) <doi:10.1109/LSP.2014.2337313>, Pencina, D'Agostino, D'Agostino and Vasan (2008) <doi:10.1002/sim.2929>, and Pencina, D'Agostino and Steyerberg (2011) <doi:10.1002/sim.4085>.

Implementation of the wavelet-based spatial verification method of Buschow and Friederichs "SAD: Verifying the Scale, Anisotropy and Direction of precipitation forecasts" (2020, submitted to QJRMS). Forecasts and Observations are transformed by a decimated or redundant dual-tree complex wavelet transform to analyze the spatial scale, degree of anisotropy and preferred direction in each field. These structural attributes are compared by a series of scores. An experimental algorithm for the correction of these errors is included as well.

This package provides a facility to generate balanced semi-Latin rectangles with any cell size (preferably up to ten) with given number of treatments, see Uto, N.P. and Bailey, R.A. (2020). "Balanced Semi-Latin rectangles: properties, existence and constructions for block size two". Journal of Statistical Theory and Practice, 14(3), 1-11, <doi:10.1007/s42519-020-00118-3>. It also provides facility to generate partially balanced semi-Latin rectangles for cell size 2, 3 and 4 for any number of treatments.

Programmatic access to Flipside Crypto data via the Compass RPC API: <https://api-docs.flipsidecrypto.xyz/>. As simple as auto_paginate_query() but with core functions as needed for troubleshooting. Note, 0.1.1 support deprecated 2023-05-31.

Based on the compound Poisson risk process that is perturbed by a Brownian motion, saddlepoint approximations to some measures of risk are provided. Various approximation methods for the probability of ruin are also included. Furthermore, exact values of both the risk measures as well as the probability of ruin are available if the individual claims follow a hypo-exponential distribution (i. e., if it can be represented as a sum of independent exponentially distributed random variables with different rate parameters). For more details see Gatto and Baumgartner (2014) <doi:10.1007/s11009-012-9316-5>.

The function generates and plots random snowflakes. Each snowflake is defined by a given diameter, width of the crystal, color, and random seed. Snowflakes are plotted in such way that they always remain round, no matter what the aspect ratio of the plot is. Snowflakes can be created using transparent colors, which creates a more interesting, somewhat realistic, image. Images of the snowflakes can be separately saved as svg files and used in websites as static or animated images.

This package provides tools for smoothing and tidying spatial features (i.e. lines and polygons) to make them more aesthetically pleasing. Smooth curves, fill holes, and remove small fragments from lines and polygons.

This package provides a pipeline-friendly toolkit for assembling stop motion animations from sequences of still images. Provides functions to read image directories, restructure frame sequences (duplicate, splice, arrange), apply per-frame pixel transformations (rotate, wiggle, flip, flop, blur, scale, crop, trim, border, background), and export the result as a GIF. All transformation functions accept a frames argument to target any subset of frames, bridging the gap between magick functions that operate on an entire image stack and fine-grained stop motion editing. Image processing is performed via ImageMagick Studio LLC (2024) <https://imagemagick.org>.

Sample size and effect size calculations for survival endpoints based on mixture survival-by-response model. The methods implemented can be found in Bofill, Shen & Gómez (2021) <arXiv:2008.12887>.

This package implements a spatially varying change point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and the five spatially varying parameter are modeled jointly using a multivariate conditional autoregressive (MCAR) prior. The MCAR is a unique process that allows for a dissimilarity metric to dictate the local spatial dependencies. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in the corresponding paper published in Spatial Statistics by Berchuck et al (2019): "A spatially varying change points model for monitoring glaucoma progression using visual field data", <doi:10.1016/j.spasta.2019.02.001>.

Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. stacks implements a grammar for tidymodels'-aligned model stacking.

Transform complex statistical output into straightforward, understandable, and context-aware natural language descriptions using Large Language Models (LLMs), making complex analyses more accessible to individuals with varying statistical expertise. It relies on the ellmer package to interface with LLM providers including OpenAI <https://openai.com/>, Google AI Studio <https://aistudio.google.com/>, and Anthropic <https://www.anthropic.com/> (API keys are required and managed via ellmer').

This package provides image-related C/C++ header files from the stb single-file libraries for image loading, writing, and resizing.

An R interface to the Python sqlfluff SQL linter and formatter via the reticulate package. Enables linting, fixing, and parsing of SQL queries with support for multiple dialects. Includes special handling for glue SQL syntax with curly-brace placeholders.

The complete scripts from the American version of the Office television show in tibble format. Use this package to analyze and have fun with text from the best series of all time.

This package provides tools to define factorial simulation conditions and generate binary or ordinal item responses under common-factor and probit graded response model parameterizations. Supports multivariate normal and correlated gamma latent traits, reproducible replications, parameter conversion, and structured storage of generated datasets. The graded response model follows Samejima (1969).

Fit a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. Fits linear, logistic and Cox models.

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

Various tools for semantic vector spaces, such as correspondence analysis (simple, multiple and discriminant), latent semantic analysis, probabilistic latent semantic analysis, non-negative matrix factorization, latent class analysis, EM clustering, logratio analysis and log-multiplicative (association) analysis. Furthermore, there are specialized distance measures, plotting functions and some helper functions.

This package provides functionality for simulating data generation processes across various spatial regression models, conceptually aligned with the dgp module of the Python library spreg <https://pysal.org/spreg/api.html#dgp>.

This package provides a collection of functions for preparing data and fitting Bayesian count spatial regression models, with a specific focus on the Gamma-Count (GC) model. The GC model is well-suited for modeling dispersed count data, including under-dispersed or over-dispersed counts, or counts with equivalent dispersion, using Integrated Nested Laplace Approximations (INLA). The package includes functions for generating data from the GC model, as well as spatially correlated versions of the model. See Nadifar, Baghishani, Fallah (2023) <doi:10.1007/s13253-023-00550-5>.