Rcpp, ggplot2, shiny, shinyjs, reshape2, fields, plyr, png,
tools, grid, abind, deldir, geometry, pdist, AdaptGauss, pracma

Inna Gerlovina [aut, cre],
Alan E. Hubbard [aut]
Calculates one-sample unbiased central moment estimates and
two-sample pooled estimates up to 6th order, including unbiased estimates
of powers and products of central moments. Provides the machinery for
obtaining unbiased central moment estimators beyond 6th order.

c(
person("Inna", "Gerlovina", email = "innager@berkeley.edu",
role = c("aut", "cre")),
person("Alan", "Hubbard", email = "hubbard.berkeley.edu",
role = "aut"))
Calculates one-sample unbiased central moment estimates and
two-sample pooled estimates up to 6th order, including estimates of
powers and products of central moments. Provides the machinery for
obtaining unbiased central moment estimators beyond 6th order by generating
expressions for expectations of raw sample moments and their powers and
products.
Gerlovina and Hubbard (2019) <doi:10.1080/25742558.2019.1701917>.
Inna Gerlovina [aut, cre],
Alan Hubbard [aut]

Simulating Realistic Gene Expression Data
The Ultimate Microrray Prediction, Reality and Inference
Engine (UMPIRE) is a package to facilitate the simulation of realistic
microarray data sets with link to associate outcomes. See Zhang and
Coombes (2012) <doi:10.1186/1471-2105-13-S13-S1>.

Simulating Realistic Gene Expression and Clinical Data
Kevin R. Coombes, Jiexin Zhang, Caitlin E. Coombes
The Ultimate Microrray Prediction, Reality and Inference
Engine (UMPIRE) is a package to facilitate the simulation of realistic
microarray data sets with links to associated outcomes. See Zhang and
Coombes (2012) <doi:10.1186/1471-2105-13-S13-S1>. Version 2.0 adds the
ability to simulate realistic mixed-typed clinical data.

c(person("Seonghyeon", "Kim", email = "shkim93@snu.ac.kr", role = c("aut", "cre")),
person("Yongdai", "Kim", role = "aut"),
person("Ilsang", "Ohn", role = "aut"))
Seonghyeon Kim [aut, cre],
Yongdai Kim [aut],
Ilsang Ohn [aut]
If a procedure consists of several stages and there are several scenarios that can be selected for each stage,
uncertainty of the procedure can be decomposed by stages or scenarios.
cum_uncertainty() is used to decompose uncertainty based on the cumulative uncertainty.
stage_uncertainty() and scenario_uncertainty() is used to decompose uncertainty based on the second order interaction ANOVA model.
In stage_uncertainty() and scenario_uncertainty(), the uncertainty from interaction effect from two stages is distributed equally to each stage.