partykit, qgraph, stats, utils, Matrix, mvtnorm, Formula,
grid, graphics, gridBase, reshape2

c(person("Payton", "Jones", email = "paytonjjones@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-6513-8498")),
person(given = "Thorsten", family = "Simon", role = "aut", email = "Thorsten.Simon@uibk.ac.at", comment = c(ORCID = "0000-0002-3778-7738")),
person(given = "Achim", family = "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org", comment = c(ORCID = "0000-0003-0918-3766")))
Network trees recursively partition the data with respect to covariates. Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data.

Network trees recursively partition the data with respect to covariates. Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data. For details see Jones, Mair, Simon, & Zeileis (2020) <doi:10.1007/s11336-020-09731-4>.

Functions to generate fMRI data including activated data, noise
data and resting state data.
Marijke Welvaert with contributions from Joke Durnez, Beatrijs
Moerkerke, Yves Rosseel and Geert Verdoolaege
R (>= 2.10.1), Rniftilib, AnalyzeFMRI, deSolve
The package allows users to generate fMRI time series or
4D data. Some high-level functions are created for fast data
generation with only a few arguments and a diversity of
functions to define activation and noise. For more advanced
users it is possible to use the low-level functions and
manipulate the arguments.

Functions to Generate fMRI Data Including Activated Data, Noise
Data and Resting State Data
The package allows users to generate fMRI time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments.