This package provides functions to efficiently query ArcGIS REST APIs <https://developers.arcgis.com/rest/>. Both spatial and SQL queries can be used to retrieve data. Simple Feature (sf) objects are utilized to perform spatial queries. This package was neither produced nor is maintained by Esri.

Developer oriented utility functions designed to be used as the building blocks of R packages that work with ArcGIS Location Services. It provides functionality for authorization, Esri JSON construction and parsing, as well as other utilities pertaining to geometry and Esri type conversions. To support ArcGIS Pro users, authorization can be done via arcgisbinding'. Installation instructions for arcgisbinding can be found at <https://developers.arcgis.com/r-bridge/installation/>.

An application for analysis of Adverse Events, as described in Chen, et al., (2023) <doi:10.3390/cancers15092521>. The required data for the application includes demographics, follow up, adverse event, drug administration and optional tumor measurement data. The app can produce swimmers plots of adverse events, Kaplan-Meier plots and Cox Proportional Hazards model results for the association of adverse event biomarkers and overall survival and progression free survival. The adverse event biomarkers include occurrence of grade 3, low grade (1-2), and treatment related adverse events. Plots and tables of results are downloadable.

Compute a tree level hierarchy, judgment matrix, consistency index and ratio, priority vectors, hierarchic synthesis and rank. Based on the book entitled "Models, Methods, Concepts and Applications of the Analytic Hierarchy Process" by Saaty and Vargas (2012, ISBN 978-1-4614-3597-6).

Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model.

Calculate the area of triangles and polygons using the shoelace formula. Area may be signed, taking into account path orientation, or unsigned, ignoring path orientation. The shoelace formula is described at <https://en.wikipedia.org/wiki/Shoelace_formula>.

Collect your data on digital marketing campaigns from Amazon Sp using the Windsor.ai API <https://windsor.ai/api-fields/>.

Three Shiny apps are provided that introduce Harvest Control Rules (HCR) for fisheries management. Introduction to HCRs provides a simple overview to how HCRs work. Users are able to select their own HCR and step through its performance, year by year. Biological variability and estimation uncertainty are introduced. Measuring performance builds on the previous app and introduces the idea of using performance indicators to measure HCR performance. Comparing performance allows multiple HCRs to be created and tested, and their performance compared so that the preferred HCR can be selected.

This package implements the alpha-power hazard model and regression frameworks for survival data based on the flexible hazard rate function h(x; alpha, beta) = alpha^x + x^(beta-1) (Pal et al., 2026 <doi:10.1007/s41096-026-00297-5>). Provides standard distribution functions (d, p, q, r, h, H, s) and distributional properties including raw/central moments, variance, skewness, kurtosis, quantile statistics (Bowley's skewness, Moors's kurtosis), Lambert W hazard rate function minimum (Corless et al., 1996), order statistics, and stochastic ordering (Shaked & Shanthikumar, 1994). Computes five classical estimation methods for baseline parameters: Maximum Likelihood Estimation (Casella & Berger, 2002), Least Squares Estimation (Swain et al., 1988), Weighted Least Squares Estimation (Styan, 1973), Maximum Product of Spacings Estimation (Cheng & Amin, 1983 <doi:10.1111/j.2517-6161.1983.tb01241.x>), and Cramer-von Mises Estimation (Macdonald, 1971). Supports four hazard regression models (M1-M4) within proportional hazards and parametric frameworks across uncensored data, right censoring, left censoring, interval censoring, and progressive Type-I and Type-II censoring schemes (Lee & Wang, 2003; Lawless, 2011; Balakrishnan & Aggarwala, 2000). Includes comprehensive model diagnostics, Cox-Snell, martingale, deviance, standardized, and studentized residuals, leverage, Cook's distance, DFFITS, DFBETAS, model comparisons (AIC, BIC, WAIC), k-fold cross-validation, prediction suites, random data generators, and an eight-panel diagnostic visualization suite.

This package provides tools for the multiscale spatial analysis of multivariate data. Several methods are based on the use of a spatial weighting matrix and its eigenvector decomposition (Moran's Eigenvectors Maps, MEM). Several approaches are described in the review Dray et al (2012) <doi:10.1890/11-1183.1>.

Functionalities to automatically generate interactive visualizations for statistical results supported by ggfortify', such as time series, PCA, clustering and survival analysis, with plotly.js <https://plotly.com/> and ggplot2 style. The generated visualizations can also be easily extended using ggplot2 and plotly syntax while staying interactive.

Utility functions to download and process data produced by the ALARM Project, including 2020 redistricting files Kenny and McCartan (2021) <https://alarm-redist.org/posts/2021-08-10-census-2020/> and the 50-State Redistricting Simulations of McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki, and Imai (2022) <doi:10.7910/DVN/SLCD3E>. The package extends the data introduced in McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki, and Imai (2022) <doi:10.1038/s41597-022-01808-2> to also include states with only a single district. The package also includes the Japanese 2022 redistricting files from the 47-Prefecture Redistricting Simulations of Miyazaki, Yamada, Yatsuhashi, and Imai (2022) <doi:10.7910/DVN/Z9UKSH>.

This package provides a unified and straightforward interface for performing a variety of meta-analysis methods directly from user data. Users can input a data frame, specify key parameters, and effortlessly execute and compare multiple common meta-analytic models. Designed for immediate usability, the package facilitates transparent, reproducible research without manual implementation of each analytical method. Ideal for researchers aiming for efficiency and reproducibility, it streamlines workflows from data preparation to results interpretation.

Bindings to FFmpeg <http://www.ffmpeg.org/> AV library for working with audio and video in R. Generates high quality video from images or R graphics with custom audio. Also offers high performance tools for reading raw audio, creating spectrograms', and converting between countless audio / video formats. This package interfaces directly to the C API and does not require any command line utilities.

Dynamic regression for time series using Extreme Gradient Boosting with hyper-parameter tuning via Bayesian Optimization or Random Search.

Analysis of complex plant root system architectures (RSA) using the output files created by Data Analysis of Root Tracings (DART), an open-access software dedicated to the study of plant root architecture and development across time series (Le Bot et al (2010) "DART: a software to analyse root system architecture and development from captured images", Plant and Soil, <DOI:10.1007/s11104-009-0005-2>), and RSA data encoded with the Root System Markup Language (RSML) (Lobet et al (2015) "Root System Markup Language: toward a unified root architecture description language", Plant Physiology, <DOI:10.1104/pp.114.253625>). More information can be found in Delory et al (2016) "archiDART: an R package for the automated computation of plant root architectural traits", Plant and Soil, <DOI:10.1007/s11104-015-2673-4>.

Multivariate tools to analyze comparative data, i.e. a phylogeny and some traits measured for each taxa. The package contains functions to represent comparative data, compute phylogenetic proximities, perform multivariate analysis with phylogenetic constraints and test for the presence of phylogenetic autocorrelation. The package is described in Jombart et al (2010) <doi:10.1093/bioinformatics/btq292>.

Computation of adherence to medications from Electronic Health care Data and visualization of individual medication histories and adherence patterns. The package implements a set of S3 classes and functions consistent with current adherence guidelines and definitions. It allows the computation of different measures of adherence (as defined in the literature, but also several original ones), their publication-quality plotting, the estimation of event duration and time to initiation, the interactive exploration of patient medication history and the real-time estimation of adherence given various parameter settings. It scales from very small datasets stored in flat CSV files to very large databases and from single-thread processing on mid-range consumer laptops to parallel processing on large heterogeneous computing clusters. It exposes a standardized interface allowing it to be used from other programming languages and platforms, such as Python.

This package provides a lightweight but powerful R interface to the Azure Resource Manager REST API. The package exposes a comprehensive class framework and related tools for creating, updating and deleting Azure resource groups, resources and templates. While AzureRMR can be used to manage any Azure service, it can also be extended by other packages to provide extra functionality for specific services. Part of the AzureR family of packages.

Formatter functions in the apa package take the return value of a statistical test function, e.g. a call to chisq.test() and return a string formatted according to the guidelines of the APA (American Psychological Association).

Penalized variable selection tools for the Cox proportional hazards model with interval censored and possibly left truncated data. It performs variable selection via penalized nonparametric maximum likelihood estimation with an adaptive lasso penalty. The optimal thresholding parameter can be searched by the package based on the profile Bayesian information criterion (BIC). The asymptotic validity of the methodology is established in Li et al. (2019 <doi:10.1177/0962280219856238>). The unpenalized nonparametric maximum likelihood estimation for interval censored and possibly left truncated data is also available.

Power and associated functions useful in prospective planning and monitoring of a clinical trial when a recurrent event endpoint is to be assessed by the robust Andersen-Gill model, see Lin, Wei, Yang, and Ying (2010) <doi:10.1111/1467-9868.00259>. The equations developed in Ingel and Jahn-Eimermacher (2014) <doi:10.1002/bimj.201300090> and their consequences are employed.

Pair of simple convenience functions to convert a vector of birth dates to age and age distributions. These functions may be helpful when related age and custom age distributions are desired given a vector of birth dates.

This package provides an htmlwidgets interface to apexcharts.js'. Apexcharts is a modern JavaScript charting library to build interactive charts and visualizations with simple API. Apexcharts examples and documentation are available here: <https://apexcharts.com/>.