'QuantLib' bindings are provided for R using 'Rcpp' via an evolved version
of the initial header-only 'Quantuccia' project offering an subset of 'QuantLib' (now
maintained separately just for the calendaring subset). See the included file 'AUTHORS'
for a full list of contributors to 'QuantLib' (and hence also 'Quantuccia').

c(person("Markus", "Baaske", role = c("aut", "cre", "cph"),
email = "markus.baaske@uni-jena.de"),
person("K. Gerald", "van den Boogaart", role = c("ths"),
email = "boogaart@math.tu-freiberg.de"))
Markus Baaske [aut, cre, cph], K. Gerald van den Boogaart [ths]
A simulation-based quasi-likelihood method (Baaske, M. (2014) <doi:10.5566/ias.v33.p107-119>) for parameter estimation of parametric statistical models for which closed-form representations of distributional characteristics are unavailable and can only be obtained by computationally intensive simulations of the model.

A simulation-based quasi-likelihood method (Baaske, M., Ballani, F., v.d. Boogaart, K.G. (2014) <doi:10.5566/ias.v33.p107-119>) for parameter estimation of parametric statistical models for which closed-form representations of distributional characteristics are unavailable and can only be obtained by computationally intensive simulations of the model.

Markus Baaske [aut, cre, cph],
K. Gerald van den Boogaart [ths]

Managing and Building of Quarterly Life Tables
c(person(given = "Jose M.",
family = "Pavía",
role = c("aut", "cre"),
email = "jose.m.pavia@uv.es",
comment = c(ORCID = "0000-0002-0129-726X")),
person(given = "Josep",
family = "Lledó",
role = "aut",
email = "Josep.Lledo@uv.es"))
Manages, builds and computes statistics and datasets for the
construction of quarterly (sub-annual) life tables by exploiting micro-data from
either a general or an insured population.
References:
Pavía and Lledó (2022) <doi:10.1111/rssa.12769>.
Jose M. Pavía [aut, cre] (<https://orcid.org/0000-0002-0129-726X>),
Josep Lledó [aut]

Jichun Xie <jichun@mail.med.upenn.edu>, Justine Shults <jshults@mail.med.upenn.edu>
QLS is a two-stage computational approach for estimation of the correlation parameters within the framework of GEE. It helps solving parameters in mean, scale, and correlation structures for longitudinal data.