This package provides a wrapper around the new cleaner package, that allows data cleaning functions for classes logical', factor', numeric', character', currency and Date to make data cleaning fast and easy. Relying on very few dependencies, it provides smart guessing, but with user options to override anything if needed.

Collection of utility functions for visualizing body map data collected with the Collaborative Health Outcomes Information Registry.

This package provides functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from extRemes', including for fitting the statistical models. Details are given in Paciorek, Stone, and Wehner (2018) <doi:10.1016/j.wace.2018.01.002>.

Simplifies the execution of command line interface (CLI) tools within isolated and reproducible environments. It enables users to effortlessly manage Conda environments, execute command line tools, handle dependencies, and ensure reproducibility in their data analysis workflows.

Implementation of a probabilistic method for biclustering adapted to overdispersed count data. It is a Gamma-Poisson Latent Block Model. It also implements two selection criteria in order to select the number of biclusters.

Implementations of recent complex-valued wavelet spectral procedures for analysis of irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.

Surrounds the usual sample variance of a univariate numeric sample with a confidence interval for the population variance. This has been done so far only under the assumption that the underlying distribution is normal. Under the hood, this package implements the unique least-variance unbiased estimator of the variance of the sample variance, in a formula that is equivalent to estimating kurtosis and square of the population variance in an unbiased way and combining them according to the classical formula into an estimator of the variance of the sample variance. Both the sample variance and the estimator of its variance are U-statistics. By the theory of U-statistic, the resulting estimator is unique. See Fuchs, Krautenbacher (2016) <doi:10.1080/15598608.2016.1158675> and the references therein for an overview of unbiased estimation of variances of U-statistics.

This package provides functions for performing experimental comparisons of algorithms using adequate sample sizes for power and accuracy. Implements the methodology originally presented in Campelo and Takahashi (2019) <doi:10.1007/s10732-018-9396-7> for the comparison of two algorithms, and later generalised in Campelo and Wanner (Submitted, 2019) <arxiv:1908.01720>.

This package provides an expectation maximization (EM) algorithm to fit a mixture of continuous time Markov models for use with clickstream or other sequence type data. Gallaugher, M.P.B and McNicholas, P.D. (2018) <arXiv:1802.04849>.

This package provides function declarations and inline function definitions that facilitate communication between R and the Eigen C++ library for linear algebra and scientific computing.

Broken adaptive ridge estimator for censored data is used to select variables and estimate their coefficients in the semi-parametric accelerated failure time model for right-censored survival data.

Generates synthetic data distributions to enable testing various modelling techniques in ways that real data does not allow. Noise can be added in a controlled manner such that the data seems real. This methodology is generic and therefore benefits both the academic and industrial research.

Estimation and inference methods for the continuous threshold expectile regression. It can fit the continuous threshold expectile regression and test the existence of change point, for the paper, "Feipeng Zhang and Qunhua Li (2016). A continuous threshold expectile regression, submitted.".

Simple interpolation methods designed to be used from C code. Supports constant, linear and spline interpolation. An R wrapper is included but this package is primarily designed to be used from C code using LinkingTo'. The spline calculations are classical cubic interpolation, e.g., Forsythe, Malcolm and Moler (1977) <ISBN: 9780131653320>.

Estimation of quantile regression models for survival data.

Frequentist statistical inference for cluster randomised trials with multiple outcomes that controls the family-wise error rate and provides nominal coverage of confidence sets. A full description of the methods can be found in Watson et al. (2023) <doi:10.1002/sim.9831>.

An implementation of Fan plots for cytometry data in ggplot2'. For reference see Britton, E.; Fisher, P. & J. Whitley (1998) The Inflation Report Projections: Understanding the Fan Chart <https://www.bankofengland.co.uk/quarterly-bulletin/1998/q1/the-inflation-report-projections-understanding-the-fan-chart>).

Check digits are used like file hashes to verify that a number has been transcribed accurately. The functions provided by this package help to calculate and verify check digits according to various algorithms.

This package provides a fast and general implementation of the Elston-Stewart algorithm that can calculate the likelihoods of large and complex pedigrees. References for the Elston-Stewart algorithm are Elston & Stewart (1971) <doi:10.1159/000152448>, Lange & Elston (1975) <doi:10.1159/000152714> and Cannings et al. (1978) <doi:10.2307/1426718>.

Images are cropped to a circle with a transparent background. The function takes a vector of images, either local or from a link, and circle crops the image. Paths to the cropped image are returned for plotting with ggplot2'. Also includes cropping to a hexagon, heart, parallelogram, and square.

Classification method described in Dancik et al (2011) <doi:10.1158/0008-5472.CAN-11-2427> that classifies a sample according to the class with the maximum mean (or any other function of) correlation between the test and training samples with known classes.

This package implements a Bayesian approach to causal impact estimation in time series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>. See the package documentation on GitHub <https://google.github.io/CausalImpact/> to get started.

Write executable specifications in a natural language that describes how your code should behave. Write specifications in feature files using Gherkin language and execute them using functions implemented in R. Use them as an extension to your testthat tests to provide a high level description of how your code works.

Computing elliptical joint confidence regions at a specified confidence level. It provides the flexibility to estimate either classical or robust confidence regions, which can be visualized in 2D or 3D plots. The classical approach assumes normality and uses the mean and covariance matrix to define the confidence regions. Alternatively, the robustified version employs estimators like minimum covariance determinant (MCD) and M-estimator, making them less sensitive to outliers and departures from normality. Furthermore, the functions allow users to group the dataset based on categorical variables and estimate separate confidence regions for each group. This capability is particularly useful for exploring potential differences or similarities across subgroups within a dataset. Varmuza and Filzmoser (2009, ISBN:978-1-4200-5947-2). Johnson and Wichern (2007, ISBN:0-13-187715-1). Raymaekers and Rousseeuw (2019) <DOI:10.1080/00401706.2019.1677270>.