Radiation Safety

Provides functions for radiation safety, also known as "radiation protection" and "radiological control". The science of radiation protection is called "health physics" and its engineering functions are called "radiological engineering". Functions in this package cover many of the computations needed by radiation safety professionals. Examples include: obtaining updated calibration and source check values for radiation monitors to account for radioactive decay in a reference source, simulating instrument readings to better understand measurement uncertainty, correcting instrument readings for geometry and ambient atmospheric conditions. Many of these functions are described in Johnson and Kirby (2011, ISBN-13: 978-1609134198). Utilities are also included for developing inputs and processing outputs with radiation transport codes, such as MCNP, a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron, or coupled neutron/photon/electron transport (Werner et. al. (2018) ).

Generates Expectations for 'testthat' Unit Testing

Helps systematize and ease the process of building unit tests with the 'testthat' package by providing tools for generating expectations.

Tools for Case 1 Best-Worst Scaling (MaxDiff) Designs

Tools to design best-worst scaling designs (i.e., balanced incomplete block designs) and to analyze data from these designs, using aggregate and individual methods such as: difference scores, Louviere, Lings, Islam, Gudergan, & Flynn (2013) ; analytical estimation, Lipovetsky & Conklin (2014) ; empirical Bayes, Lipovetsky & Conklin (2015) ; Elo, Hollis (2018) ; and network-based measures.

Configurable Reporting on your External Compute Environment

Runs a series of configurable tests against a user's compute environment. This can be used for checking that things like a specific directory or an environment variable is available before you start an analysis. Alternatively, you can use the package's situation report when filing error reports with your compute infrastructure.

Multiple Assemblage Dissimilarity for Orders q = 0-N

Calculate multiple or pairwise dissimilarity for orders q = 0-N (CqN; Chao et al. 2008 ) for a set of species assemblages or interaction networks.

Generate Bootstrap Prediction Intervals from a 'tidymodels' Workflow

Provides functions for generating bootstrap prediction intervals from a 'tidymodels' workflow. 'tidymodels' < https://www.tidymodels.org/> is a collection of packages for modeling and machine learning using 'tidyverse' < https://www.tidyverse.org/> principles. This package is not affiliated with or maintained by 'RStudio' or the 'tidymodels' maintainers.

Tools for Working with NHS Number Checksums

Provides functions for working with NHS number checksums. The UK's National Health Service issues NHS numbers to all users of its services and this package implements functions for verifying that the numbers are valid according to the checksum scheme the NHS use. Numbers can be validated and checksums created.

Performance Criteria Modeler for Discrete Trial Training

Provides a tool for computing probabilities and other quantities that are relevant in selecting performance criteria for discrete trial training. The main function, miebl(), computes Bayesian and frequentist probabilities and bounds for each of n possible performance criterion choices when attempting to determine a student's true mastery level by counting their number of successful attempts at displaying learning among n trials. The reporting function miebl_re() takes output from miebl() and prepares it into a brief report for a specific criterion. miebl_cp() combines 2 to 5 distributions of true mastery level given performance criterion in one plot for comparison. Ramos (2025) .

Transformed and Relative Lorenz Curves for Survey Weighted Data

Functions for constructing Transformed and Relative Lorenz curves with survey sampling weights. Given a variable of interest measured in two groups with scaled survey weights so that their hypothetical populations are of equal size, tlorenz() computes the proportion of members of the group with smaller values (ordered from smallest to largest) needed for their sum to match the sum of the top qth percentile of the group with higher values. rlorenz() shows the fraction of the total value of the group with larger values held by the pth percentile of those in the group with smaller values. Fd() is a survey weighted cumulative distribution function and Eps() is a survey weighted inverse cdf used in rlorenz(). Ramos, Graubard, and Gastwirth (2025) .

Adapt Numerical Records to Fit (in)Equality Restrictions

Minimally adjust the values of numerical records in a data.frame, such that each record satisfies a predefined set of equality and/or inequality constraints. The constraints can be defined using the 'validate' package. The core algorithms have recently been moved to the 'lintools' package, refer to 'lintools' for a more basic interface and access to a version of the algorithm that works with sparse matrices.