Suggests: knitr, bipartite, vegan, rmarkdown

Title: Bayesian Individual Patient Data Meta-Analysis using 'JAGS'
Imports: rjags (>= 4-6), coda (>= 0.13), mvtnorm, dplyr
Suggests: dclone, R2WinBUGS, mice, micemd, miceadds, mitools, knitr,
person("Michael", "Seo", email = "swj8874@gmail.com", role = c("aut", "cre")))
Description: We use a Bayesian approach to run individual patient data meta-analysis using 'JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) <DOI:10.1002/sim.8859>. This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages.

Description: We use a Bayesian approach to run individual patient data meta-analysis and network meta-analysis using 'JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) <DOI:10.1002/sim.8859>. This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages.

Author: Ana Belen Nieto Librero<ananieto@usal.es>, Purificacion Galindo Villardon<pgalindo@usal.es>
Maintainer: Ana Belen Nieto Librero <ananieto@usal.es>
Description: A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots

Title: Bootstrap on Classical Biplots and Clustering Disjoint Biplot
Description: A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot.
Imports: rgl, tkrplot, tcltk, tcltk2, shapes, cluster, dendroextras,

Author: Ana Belen Nieto Librero<ananieto@usal.es>, Purificacion Galindo Villardon<pgalindo@usal.es>, Adelaide Freitas<Adelaide@ua.pt>
Description: A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot. This GUI is also aimed for estimate any numerical data matrix using the Clustering and Disjoint Principal component (CDPCA) methodology.
Imports: cluster, dendroextras, graphics, grDevices, MASS, matlib, rgl,
shapes, stats, tcltk, tcltk2, tkrplot, utils