Short and understandable commands that generate tabulated, 
    formatted, and rounded survey estimates. Mostly a wrapper for the 
    'survey' package (Lumley (2004) <doi:10.18637/jss.v009.i08> 
    <https://CRAN.R-project.org/package=survey>) that identifies 
    low-precision estimates using the National Center for Health 
    Statistics (NCHS) presentation standards (Parker et al. (2017) 
    <https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf>,
    Parker et al. (2023) <doi:10.15620/cdc:124368>).

Decision support tool for prioritizing sites for ecological
    surveys based on their potential to improve plans for conserving
    biodiversity (e.g. plans for establishing protected areas). Given a set of
    sites that could potentially be acquired for conservation management,
    it can be used to generate and evaluate plans for surveying additional
    sites. Specifically, plans for ecological surveys can be
    generated using various conventional approaches (e.g. maximizing expected
    species richness, geographic coverage, diversity of sampled environmental
    algorithms. After generating such survey plans, they can be evaluated using
    conditions) and maximizing value of information. Please note that several
    functions depend on the 'Gurobi' optimization software (available from
    <https://www.gurobi.com>). Additionally, the 'JAGS' software (available from
    <https://mcmc-jags.sourceforge.io/>) is required to fit hierarchical
    generalized linear models. For further details, see Hanson et al. (2022) <doi:10.1111/1365-2664.14309>.

Contains a suite of functions for survival analysis in health economics.
    These can be used to run survival models under a frequentist (based on maximum likelihood)
    or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian
    Monte Carlo). To run the Bayesian models, the user needs to install additional modules 
    (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed using 
    'remotes::install_github' from their GitHub repositories: 
    (<https://github.com/giabaio/survHEhmc> and <https://github.com/giabaio/survHEinla/> 
    respectively). 'survHEinla' is based on the package INLA, which is available for download at
    <https://inla.r-inla-download.org/R/stable/>. The user can specify a set of parametric models 
    using a common notation and select the preferred mode of inference. The results can also be 
    post-processed to produce probabilistic sensitivity analysis and can be used to export the 
    output to an Excel file (e.g. for a Markov model, as often done by modellers and 
    practitioners). <doi:10.18637/jss.v095.i14>.