knitr, covr, testthat (>= 3.0.0), rmarkdown, badger

Synthetic Experience Tracking Insurance Claims
c(
person("Benjamin", "Avanzi", role = "aut", email = "b.avanzi@unimelb.edu.au"),
person("Greg", "Taylor", role = "aut", email = "greg_taylor60@hotmail.com"),
person("Melantha", "Wang", role = c("aut", "cre"), email = "wang.melantha@gmail.com"),
person("Bernard", "Wong", role = "aut", email = "bernard.wong@unsw.edu.au"))
Benjamin Avanzi [aut],
Greg Taylor [aut],
Melantha Wang [aut, cre],
Bernard Wong [aut]
Creation of an individual claims simulator which generates various
features of non-life insurance claims. An initial set of test parameters,
designed to mirror the experience of an Auto Liability portfolio, were set
up and applied by default to generate a realistic test data set of
individual claims (see vignette). The simulated data set then allows
practitioners to back-test the validity of various reserving models and to
prove and/or disprove certain actuarial assumptions made in claims
modelling. The distributional assumptions used to generate this data set can
be easily modified by users to match their experiences. Reference: Avanzi B,
Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim
simulator with feature control" <arXiv:2008.05693>.

stats, ggplot2, magrittr, rlang, methods

Tools and Tests for Experiments with Partially Synthetic Data
Sets
person(given = "Charlotte",
family = "Looby",
role = c("aut", "cre"),
email = "clooby@rti.org")
A set of functions to support experimentation in the utility of partially synthetic data sets.  All functions compare an observed data set to one or a set of partially synthetic data sets derived from the observed data to (1) check that data sets have identical attributes, (2) calculate overall and specific variable perturbation rates, (3) check for potential logical inconsistencies, and (4) calculate confidence intervals and standard errors of desired variables in multiple imputed data sets. Confidence interval and standard error formulas have options for either synthetic data sets or multiple imputed data sets. For more information on the formulas and methods used, see Reiter & Raghunathan (2007) <doi:10.1198/016214507000000932>.