This package provides functions for the collection of 3D points and curves using a stereo camera setup.

Build a project framework for users with access to only the most basic of automation tools.

Generates synthetic clinical datasets that preserve statistical properties while reducing re-identification risk. Implements Gaussian copula simulation, bootstrap with noise injection, and Laplace noise perturbation, with built-in utility and privacy validation metrics. Useful for privacy-aware data sharing in multi-site clinical research. Validates synthetic data quality via distributional similarity (Kolmogorov-Smirnov), discriminative accuracy (real-vs-synthetic classifier), and nearest-neighbor privacy ratio. Methods described in Jordon et al. (2022) <doi:10.48550/arXiv.2205.03257> and Snoke et al. (2018) <doi:10.1111/rssa.12358>.

This package provides additional convenience functions for gtsummary (Sjoberg et al. (2021) <doi:10.32614/RJ-2021-053>) & gt tables, including automatic variable labeling from dictionaries, standardized missing value display, and consistent formatting helpers for streamlined table styling workflows.

Generates physically based sky environment maps and radiance samples using the spectral Hosek-Wilkie and Prague atmosphere models. Functions write high-dynamic-range OpenEXR domes in latitude-longitude projections, compute per-direction RGB or 55-channel values, and optionally composite time-accurate star fields and moon phases. Features include automatic sun and moon positioning from date, time and location, support for sea-level and high-altitude observers, wide-spectrum coefficients, and multithreaded C++ acceleration for fast, high-resolution output. For model details, see Hosek and Wilkie (2012) <doi:10.1145/2185520.2185591>, Hosek and Wilkie (2013) <doi:10.1109/MCG.2013.18>, Wilkie et al. (2021) <doi:10.1145/3450626.3459758>, and Vevoda et al. (2022) <doi:10.1111/cgf.14677>.

Characterize daily stream discharge and water quality data and subsample water quality data. Provide dates, discharge, and water quality measurements and streamsampler can find gaps, get summary statistics, and subsample according to common stream sampling protocols. Stream sampling protocols are described in Lee et al. (2016) <doi:10.1016/j.jhydrol.2016.08.059> and Lee et al. (2019) <doi:10.3133/sir20195084>.

Construct subtests from a pool of items by using ant-colony-optimization, genetic algorithms, brute force, or random sampling. Schultze (2017) <doi:10.17169/refubium-622>.

This package provides methods and data for cluster detection and disease mapping.

Allows shiny developers to incorporate UI elements based on Google's Material design. See <https://material.io/guidelines/> for more information.

An user-friendly framework to preprocess raw item scores of questionnaires into factors or scores and standardize them. Standardization can be made either by their normalization in representative sample, or by import of premade scoring table.

To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects (the default specification assumes no-time-effect) under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) <doi:10.1093/biostatistics/kxy031>, Li et al. (2018) <doi:10.1111/biom.12918>. Supplementary documents can be found at: <https://ysph.yale.edu/cmips/research/software/study-design-power-calculation/swdpwr/>. The Shiny app for swdpwr can be accessed at: <https://jiachenchen322.shinyapps.io/swdpwr_shinyapp/>. The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively.

This package provides functions for converting and processing network data from a SpatialLinesDataFrame -Class object to an igraph'-Class object.

This package provides a wrapper for sparse VAR (Vector Autoregression) and VECM (Vector Error Correction Model) time series models estimation using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped Absolute Deviation) and MCP (Minimax Concave Penalty). Based on the work of Basu and Michailidis (2015) <doi:10.1214/15-AOS1315>.

Tests for equality of two survival functions based on integrated weighted differences of two Kaplan-Meier curves.

Shapley Value Regression for calculating the relative importance of independent variables in linear regression with avoiding the collinearity.

Lexicons and tools to perform sentiment analysis on Italian texts. Lexicons included: Sentix 3.0, MAL, ElIta VAD and basic emotions (Plutchik's wheel of emotions). For more details about the lexicons, see Basile & Nissim (2013), "Sentiment Analysis on Italian Tweets", <https://aclanthology.org/W13-1614/>; Vassallo et al. (2019), "The Tenuousness of Lemmatization in Lexicon-based Sentiment Analysis", <https://aclanthology.org/2019.clicit-1.79/>; Di Palma (2024), "ELIta: A New Italian Language Resource for Emotion Analysis", <https://aclanthology.org/2024.clicit-1.36/>.

This package provides easy to use functions to create all-sky grid plots of widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and scatter plots of data on any of these systems including on-the-fly system conversion. It supports any type of spherical projection to the plane defined by the mapproj package.

This package provides convenient snapshot testing functions for packages, including expect_snapshot_data() for data.frames and expect_snapshot_object() for any R object.

This package provides a collection of statistical hypothesis tests of functional time series. While it will include more tests when the related literature are enriched, this package contains the following key tests: functional stationarity test, functional trend stationarity test, functional unit root test, to name a few.

This package implements the Bayesian model selection method with suspected latent grouping factor methodology of Metzger and Franck (2020), <doi:10.1080/00401706.2020.1739561>. SLGF detects latent heteroscedasticity or group-based regression effects based on the levels of a user-specified categorical predictor.

Make interactive d3.js sequence sunburst diagrams in R with the convenience and infrastructure of an htmlwidget'.

This package provides a set of functions that can be used to spatially thin species occurrence data. The resulting thinned data can be used in ecological modeling, such as ecological niche modeling.

This package provides methods to fit robust alternatives to commonly used models used in Small Area Estimation. The methods here used are based on best linear unbiased predictions and linear mixed models. At this time available models include area level models incorporating spatial and temporal correlation in the random effects.

This package provides a fast and flexible set of tools for large scale estimation. It features many stochastic gradient methods, built-in models, visualization tools, automated hyperparameter tuning, model checking, interval estimation, and convergence diagnostics.