Generates simulated data representing the LOX drop testing
process (also known as impact testing). A simulated process allows for
accelerated study of test behavior. Functions are provided to simulate
trials, test series, and groups of test series. Functions for creating plots
specific to this process are also included. Test attributes and criteria can
be set arbitrarily. This work is not endorsed by or affiliated with NASA.
See "ASTM G86-17, Standard Test Method for Determining Ignition Sensitivity
of Materials to Mechanical Impact in Ambient Liquid Oxygen and Pressurized
Liquid and Gaseous Oxygen Environments" <doi:10.1520/G0086-17>.

Doubly-Robust Estimators for Ordinal Outcomes
c(
person("David", "Benkeser", email = "benkeser@emory.edu",
role = c("aut", "cre","cph"),
comment = c(ORCID = "0000-0002-1019-8343"))
)
Efficient covariate-adjusted estimators of quantities that are useful for
establishing the effects of treatments on ordinal outcomes.
David Benkeser [aut, cre, cph]
(<https://orcid.org/0000-0002-1019-8343>)

Statistical Modeling and Assessment of Drought
Provide tools for drought monitoring based on univariate and multivariate drought indicators.Statistical drought prediction based on Ensemble Streamflow Prediction (ESP) and drought risk assessments are also provided. Please see Hao Zengchao et al. (2017) <doi:10.1016/j.envsoft.2017.02.008>.

Provide tools for drought monitoring based on univariate and multivariate drought indicators.Statistical drought prediction based on Ensemble Streamflow Prediction (ESP), drought risk assessments, and drought propagation are also provided. Please see Hao Zengchao et al. (2017) <doi:10.1016/j.envsoft.2017.02.008>.

Efficient Doubly Robust Population Size Estimation
c(person(given = "Manjari",
family = "Das",
role = c("aut", "cre"),
email = "manjari8d@gmail.com",
comment = c(ORCID = "0000-0002-6781-6368")),
person(given = "Edward H.",
family = "Kennedy",
role = c("aut"),
email = "edward@stat.cmu.edu",
comment = c(ORCID = "0000-0002-1510-8175")))
Estimation of the total population size from capture-recapture data efficiently and with low bias implementing the methods from Das M, Kennedy EH, and Jewell NP (2021) <arXiv:2104.14091>. The estimator is doubly robust against errors in the estimation of the intermediate nuisance parameters. Users can choose from the flexible estimation models provided in the package, or use any other preferred model.
gam, janitor, reshape2, stringr, tidyr, dplyr, SuperLearner,
ggplot2, nnet, nnls, parallel, ranger