A Collection of Functions to Help with Analysis of Coarsely
Observed Data
Functions to analyze coarse data.
Specifically, it contains functions to (1) fit parametric accelerated
failure time models to interval-censored survival time data, and (2)
estimate the case-fatality ratio in scenarios with under-reporting.
This package's development was motivated by applications to infectious
disease: in particular, problems with estimating the incubation period and
the case fatality ratio of a given disease.  Sample data files are included
in the package.

c(person("Nicholas G.", "Reich", email = "nick@schoolph.umass.edu", role = c("aut", "cre")),
person("Justin", "Lessler", email = "jlessler@jhsph.edu", role = "aut"),
person("Andrew", "Azman", email = "andrewazman@gmail.com", role = "aut"),
person("Zhian N.", "Kamvar", email = "zkamvar@gmail.com", role = "ctb"))
Functions to analyze coarse data.
Specifically, it contains functions to (1) fit parametric accelerated
failure time models to interval-censored survival time data, and (2)
estimate the case-fatality ratio in scenarios with under-reporting.
This package's development was motivated by applications to infectious
disease: in particular, problems with estimating the incubation period and
the case fatality ratio of a given disease.  Sample data files are included
in the package. See Reich et al. (2009) <doi:10.1002/sim.3659>,
Reich et al. (2012) <doi:10.1111/j.1541-0420.2011.01709.x>, and
Lessler et al. (2009) <doi:10.1016/S1473-3099(09)70069-6>.
Nicholas G. Reich [aut, cre],
Justin Lessler [aut],
Andrew Azman [aut],
Zhian N. Kamvar [ctb]

c(person("Nicholas G.", "Reich", email = "nick@umass.edu", role = c("aut", "cre")),
person("Justin", "Lessler", email = "jlessler@unc.edu", role = "aut"),
person("Andrew", "Azman", email = "andrewazman@gmail.com", role = "aut"),
person("Zhian N.", "Kamvar", email = "zkamvar@gmail.com", role = "ctb"))

Generate balance tables and plots for covariates of groups
preprocessed through matching, weighting or subclassification, for example,
using propensity scores. Includes integration with 'MatchIt', 'twang', 'Matching', and
'CBPS' for assessing balance on the output of their preprocessing functions. Users
can also specify their data not generated through the above packages.