Imports: Rcpp (>= 1.0.0), lazyeval, stats, spatstat (>= 2.0.0),
spatstat.geom, utils, forcats, rgr, Matrix, parallel

comment = c("author of the original Matlab code", ORCID = "0000-0002-9656-2272"))
Description: Simulation of the generalized fiducial distribution for
normal linear mixed models with interval data. Fiducial inference is
somehow similar to Bayesian inference, in the sense that it is based
on a distribution that represents the uncertainty about the
parameters, like the posterior distribution in Bayesian statistics. It
does not require a prior distribution, and it yields results close to
frequentist results. Reference: Cisewski and Hannig (2012)
Imports: forcats, lazyeval, Matrix, parallel, Rcpp (>= 1.0.0), spatstat
Suggests: AOV1R, car, emmeans, GGally, kde1d, knitr, lmerTest,

Title: Generalized Fiducial Inference for Binary Logistic Regression
Description: Fiducial framework for the logistic regression model. The fiducial distribution of the parameters of the logistic regression is simulated, allowing to perform statistical inference on any parameter of interest. The algorithm is taken from Jessi Cisewski's PhD thesis: Jessi Cisewski (2012), "Generalized fiducial inference for mixed linear models".
Imports: rcdd, lazyeval, spatstat, EigenR, stats, Rcpp
LinkingTo: Rcpp, RcppArmadillo, roptim, BH

Imports: rcdd, lazyeval, spatstat (>= 2.0.0), spatstat.geom, EigenR,

Title: Implements Group Fused Multinomial Regression
Depends: R (>= 3.1.0), parallel, nnet, faraway
Description: Software to implement methodology to preform automatic response category
combinations in multinomial logistic regression. There are functions for both cross validation
and AIC for model selection.  The method provides regression coefficient estimates
that may be useful for better understanding the true probability distribution of
multinomial logistic regression when category probabilities are similar. These methods are not
recommended for a large number of predictor variables.

Authors@R: c(person("Victoria", "Lin", role = c("aut"),
comment = "V. Lin and S. McGrath made equal
person("Sean", "McGrath", role = c("aut", "cre"),
"V. Lin and S. McGrath made equal contributions")),
person("Zilu", "Zhang", role = c("aut"),
person("Roger W.", "Logan", role = c("aut"),
person("Lucia C.", "Petito", role = c("aut"),
person("Jessica G.", "Young", role = c("aut"),
"M.A. Hernán and J.G. Young made equal contributions")),
person("Miguel A.", "Hernán", role = c("aut"),
comment = "M.A. Hernán and J.G. Young made equal
person("2019 The President and Fellows of Harvard College",
Description: Implements the parametric g-formula algorithm of Robins (1986)
<doi:10.1016/0270-0255(86)90088-6>. The g-formula can be used to estimate
the causal effects of hypothetical time-varying treatment interventions on
the mean or risk of an outcome from longitudinal data with time-varying
confounding. This package allows: 1) binary or continuous/multi-level
time-varying treatments; 2) different types of outcomes (survival or
continuous/binary end of follow-up); 3) data with competing events or
truncation by death and loss to follow-up and other types of censoring
events; 4) different options for handling competing events in the case of
survival outcomes; 5) a random measurement/visit process; 6) joint
interventions on multiple treatments; and 7) general incorporation of a
Imports: data.table, ggplot2, ggpubr, grDevices, nnet, parallel, stats,
stringr, survival, truncnorm, truncreg, utils
Author: Victoria Lin [aut] (V. Lin and S. McGrath made equal contributions),
Lin and S. McGrath made equal contributions),
Hernán and J.G. Young made equal contributions),
Miguel A. Hernán [aut] (M.A. Hernán and J.G. Young made equal
2019 The President and Fellows of Harvard College [cph]