Packaged: 2021-02-18 23:40:24 UTC; daeyounglim

Description: Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Hui Yao, Sungduk Kim, Ming-Hui Chen, Joseph G. Ibrahim, Arvind K. Shah, and Jianxin Lin (2015) <doi:10.1080/01621459.2015.1006065> and Hao Li, Daeyoung Lim, Ming-Hui Chen, Joseph G. Ibrahim, Sungduk Kim, Arvind K. Shah, Jianxin Lin (2021) <doi:10.1002/sim.8983>. For maximal computational efficiency, the Markov chain Monte Carlo samplers for each model, written in C++, are fine-tuned. This software has been developed under the auspices of the National Institutes of Health and Merck & Co., Inc., Kenilworth, NJ, USA.
Packaged: 2021-04-21 03:06:34 UTC; daeyounglim

Imports: Rcpp, ggplot2, methods, gridExtra, Formula

Packaged: 2021-10-20 19:32:25 UTC; daeyounglim

Packaged: 2021-12-14 00:18:33 UTC; daeyounglim

Packaged: 2022-05-30 14:27:05 UTC; daeyounglim

Title: Formalized Plots for Self-Describing Data
Description: Creates fully-annotated plots with minimum guidance.
Since the data is self-describing, less effort is needed for
creating the plot. Generally expects data of class folded
(see fold package). If attributes GUIDE and LABEL are present,
they will be used to create formal axis labels. Several aesthetics
are supported, such as reference lines, unity lines, smooths, and
Imports: encode, lattice, magrittr, lazyeval, dplyr, fold,

Imports: encode, lattice, magrittr, lazyeval, dplyr (>= 0.7.1), fold,

(see 'fold' package). If attributes GUIDE and LABEL are present,

Title: Formalized Plots with Systematic Metadata
Description: Designs plots in terms of core structure.  See 'example(metaplot)'.
Primary arguments are (unquoted) column names; order and type (numeric or not)
dictate the resulting plot.  Specify any y variables, x variable, any groups variable,
and any conditioning variables to metaplot() to generate density plots, boxplots,
scatterplots, scatterplot matrices, or conditioned plots.  Honors scalar column
attributes 'label' and 'guide' where present to produce fully annotated plots
using concise syntax. Attribute 'guide' is typically units, but may be encoded()
to provide interpretations of categorical values (see 'encode' package).  Utility
unpack() transforms scalar column attributes to row values and pack() does the
reverse, supporting tool-neutral storage of metadata along with primary data.
Supports customizable aesthetics such as such as reference lines, unity lines,
smooths, log transformation, and linear fits. Compact syntax and isolated metadata
Imports: encode (>= 0.3.2), lattice, magrittr, lazyeval, dplyr (>=