Expert Algorithm Verbal Autopsy assigns causes of death to 2016 WHO Verbal Autopsy Questionnaire data. odk2EAVA() converts data to a standard input format for cause of death determination building on the work of Thomas (2021) <https://cran.r-project.org/src/contrib/Archive/CrossVA/>. codEAVA() uses the presence and absence of signs and symptoms reported in the Verbal Autopsy interview to diagnose common causes of death. A deterministic algorithm assigns a single cause of death to each Verbal Autopsy interview record using a hierarchy of all common causes for neonates or children 1 to 59 months of age.

Fitting and testing multi-attribute probabilistic choice
  models, especially the Bradley-Terry-Luce (BTL) model (Bradley &
  Terry, 1952 <doi:10.1093/biomet/39.3-4.324>; Luce, 1959),
  elimination-by-aspects (EBA) models (Tversky, 1972 <doi:10.1037/h0032955>),
  and preference tree (Pretree) models (Tversky & Sattath, 1979
  <doi:10.1037/0033-295X.86.6.542>).

Implements entropy balancing, a data preprocessing procedure
    described in Hainmueller (2012, <doi:10.1093/pan/mpr025>) that allows
    users to reweight a dataset such that the covariate distributions in
    the reweighted data satisfy a set of user-specified moment conditions.
    Useful for creating balanced samples in observational studies with a
    binary treatment where the control group is reweighted to match the
    covariate moments of the treatment group, and for reweighting a
    survey sample to known characteristics from a target population.

Estimate ecosystem metabolism in a Bayesian framework for
  individual water quality monitoring stations with continuous dissolved
  oxygen time series. A mass balance equation is used that provides
  estimates of parameters for gross primary production, respiration,
  and gas exchange. Methods adapted from Grace et al. (2015)
  <doi:10.1002/lom3.10011> and Wanninkhof (2014) <doi:10.4319/lom.2014.12.351>.
  Details in Beck et al. (2024) <doi:10.1002/lom3.10620>.