Plot an R package's recursive dependency graph and tabulate the number of unique downstream dependencies added by top-level dependencies. This helps R package developers identify which of their declared dependencies add the most downstream dependencies in order to prioritize them for removal if needed. Uses graph stress minimization adapted from Schoch (2023) <doi:10.21105/joss.05238> and originally reported in Gansner et al. (2004) <doi:10.1007/978-3-540-31843-9_25>.

The Ljung-Box test is one of the most important tests for time series diagnostics and model selection. The Hassani SACF (Sum of the Sample Autocorrelation Function) Theorem , however, indicates that the sum of sample autocorrelation function is always fix for any stationary time series with arbitrary length. This package confirms for sensitivity of the Ljung-Box test to the number of lags involved in the test and therefore it should be used with extra caution. The Hassani SACF Theorem has been described in : Hassani, Yeganegi and M. R. (2019) <doi:10.1016/j.physa.2018.12.028>.

Automatic construction of regular and irregular histograms as described in Rozenholc/Mildenberger/Gather (2010).

Provide functionality to manage, clean and match highfrequency trades and quotes data, calculate various liquidity measures, estimate and forecast volatility, detect price jumps and investigate microstructure noise and intraday periodicity. A detailed vignette can be found in the open-access paper "Analyzing Intraday Financial Data in R: The highfrequency Package" by Boudt, Kleen, and Sjoerup (2022, <doi:10.18637/jss.v104.i08>).

Probability functions and other utilities for the generalized Hermite distribution.

This package provides a utility to quickly obtain clean and tidy men's basketball play by play data. Provides functions to access live play by play and box score data from ESPN<https://www.espn.com> with shot locations when available. It is also a full NBA Stats API<https://www.nba.com/stats/> wrapper. It is also a scraping and aggregating interface for Ken Pomeroy's men's college basketball statistics website<https://kenpom.com>. It provides users with an active subscription the capability to scrape the website tables and analyze the data for themselves.

Perform statistical writership analysis of scanned handwritten documents. Webpage provided at: <https://github.com/CSAFE-ISU/handwriter>.

This package provides a function to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate. The main function can be used for either a continuous or discrete baseline covariate. More details will be available in the future in: Parast, L., Cai, T., Tian L (2021). "Testing for Heterogeneity in the Utility of a Surrogate Marker." Biometrics, In press.

This package provides methods for data engineering in the human resources (HR) corporate domain. Designed for HR analytics practitioners and workforce-oriented data sets.

This package provides a visualization suite primarily designed for single-cell RNA-sequencing data analysis applications but well-suited for other purposes as well. It introduces novel plots to represent two-variable and frequency data and optimizes some commonly used plotting options (e.g., correlation, network, density, alluvial and volcano plots) for ease of usage and flexibility.

Base R's default setting for stringsAsFactors within data.frame() and as.data.frame() is supposedly the most often complained about piece of code in the R infrastructure. The hellno package provides an explicit solution without changing R itself or having to mess around with options. It tries to solve this problem by providing alternative data.frame() and as.data.frame() functions that are in fact simple wrappers around base R's data.frame() and as.data.frame() with stringsAsFactors option set to HELLNO ( which in turn equals FALSE ) by default.

Hierarchical Modelling of Species Communities (HMSC) is a model-based approach for analyzing community ecological data. This package implements it in the Bayesian framework with Gibbs Markov chain Monte Carlo (MCMC) sampling (Tikhonov et al. (2020) <doi:10.1111/2041-210X.13345>).

This package provides a simple and time saving multiple linear regression function (OLS) with interpretation, optional bootstrapping, effect size calculation and all tested requirements.

This package provides functions implementing change point detection methods using the maximum pairwise Bayes factor approach. Additionally, the package includes tools for generating simulated datasets for comparing and evaluating change point detection techniques.

Implementation of S4 class of sets and multisets of numbers. The implementation is based on the hash table from the package hash'. Quick operations are allowed when the set is a dynamic object. The implementation is discussed in detail in Ceoldo and Wit (2023) <arXiv:2304.09809>.

We provide a stage-wise selection method using genetic algorithms, designed to efficiently identify main and two-way interactions within high-dimensional linear regression models. Additionally, it implements simulated annealing algorithm during the mutation process. The relevant paper can be found at: Ye, C.,and Yang,Y. (2019) <doi:10.1109/TIT.2019.2913417>.

This package provides pipe-friendly (%>%) wrapper functions for MASS::mvrnorm() to create simulated multivariate data sets with groups of variables with different degrees of variance, covariance, and effect size.

Convenience date tools for identifying weekends, business days, and Canadian holidays, including R wrappers for the Canada Holidays API <https://canada-holidays.ca/>.

It provides three implementations of hash tables and hash maps: 1. using std::unordered_map and std::unordered_set C++ libraries, 2. wrapping around the fastmatch package, 3. using R environment.

Initializes a class that obtains API credentials and provides a method to use those credentials to make GET requests to the Hakai API server. Usage instructions are documented at <https://hakaiinstitute.github.io/hakai-api/>.

This package provides datasets used for analysis and visualizations in the open-access Hello Data Science book.

Fits latent space models for single networks and hierarchical latent space models for ensembles of networks as described in Sweet, Thomas & Junker (2013).

Set of tools to help interested researchers to build hospital networks from data on hospitalized patients transferred between hospitals. Methods provided have been used in Donker T, Wallinga J, Grundmann H. (2010) <doi:10.1371/journal.pcbi.1000715>, and Nekkab N, Crépey P, Astagneau P, Opatowski L, Temime L. (2020) <doi:10.1038/s41598-020-71212-6>.

This package provides a dependency free interface to the H3 geospatial indexing system utilizing the Rust library h3o <https://github.com/HydroniumLabs/h3o> via the extendr library <https://github.com/extendr/extendr>.