Description: Implements several methods to meta-analyze one-group or two-group
studies that report the median of the outcome. These methods estimate the
pooled median in the one-group context and the pooled raw difference of
medians across groups in the two-group context.

studies that report the median of the outcome. In the one-group context,
the methods of McGrath et al. (2018a) <doi:10.1002/sim.8013> estimate
the pooled median. In the two-group context, the methods of McGrath et al.
(2018b) <arXiv:1809.01278> estimate the pooled raw difference of medians

Imports: estmeansd, Hmisc, metafor, stats

Description: Implements several methods to meta-analyze studies that report the
sample median of the outcome. When the primary studies are one-group
studies, the methods of McGrath et al. (2019) <doi:10.1002/sim.8013> can
be applied to estimate the pooled median. In the two-group context, the
methods of McGrath et al. (2018) <arXiv:1809.01278> can be applied to
estimate the pooled raw difference of medians across groups.

methods of McGrath et al. (2020) <doi:10.1002/bimj.201900036> can be
applied to estimate the pooled raw difference of medians across groups.

Title: Creates Data with Identical Statistics
Description: Creates data with identical statistics (metamers) using an iterative
algorithm proposed by Matejka & Fitzmaurice (2017) <DOI:10.1145/3025453.3025912>.

Title: Create Data with Identical Statistics
Imports: FNN, progress (>= 1.2.0), methods
Suggests: shiny, miniUI, testthat (>= 2.1.0), data.table, covr

Suggests: shiny, miniUI, testthat (>= 2.1.0), data.table, covr, sf

Title: Analysis of Microbiome Relative Abundance Data using Zero
Inflated Beta GAMLSS and Meta-Analysis Across Studies using
Authors@R: person("Nhan", "Ho", email = "nhanhocumc@gmail.com",
Description: Generalized Additive Model for Location, Scale and Shape (GAMLSS)
with zero inflated beta (BEZI) family for analysis of microbiome relative abundance data
(with various options for data transformation/normalization to address compositional effects) and
random effects meta-analysis models for meta-analysis pooling estimates across microbiome studies
Random Forest model to predict microbiome age based on relative abundances of
shared bacterial genera with the Bangladesh data (Subramanian et al 2014),
comparison of multiple diversity indexes using linear/linear mixed effect models
and some data display/visualization are also implemented.
Imports: meta, lme4, gdata, matrixStats, plyr, dplyr, tidyr, ggplot2,
gridExtra, RColorBrewer, lmerTest, mgcv, reshape2, caret,
randomForest, grDevices, gplots, magrittr, tools, foreign,
knitr, rmarkdown, zCompositions, compositions, RCurl, httr,