Packaged: 2021-10-06 17:34:28 UTC; payton-jones

Packaged: 2022-06-03 21:53:17 UTC; payton-jones

Title: Recursive Partitioning of Network Models
Authors@R: c(person("Payton", "Jones", email = "payton_jones@g.harvard.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-6513-8498")),
person(given = "Thorsten", family = "Simon", role = c("aut"), email = "thorsten.simon@uibk.ac.at", comment = c(ORCID = "0000-0002-3778-7738")),
Description: Methods to create tree models with correlation-based network models (multivariate normal distributions).
Imports: partykit, qgraph, stats, utils, Matrix, mvtnorm, Formula,
Author: Payton Jones [aut, cre] (<https://orcid.org/0000-0001-6513-8498>),

Suggests: R.rsp, knitr, rmarkdown, fxregime, zoo

Authors@R: 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")),
Description: 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.
Packaged: 2020-12-11 18:33:26 UTC; payton-jones

Description: 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>.
Packaged: 2021-02-04 14:10:22 UTC; payton-jones

Title: Functions to generate fMRI data including activated data, noise
Author: Marijke Welvaert with contributions from Joke Durnez, Beatrijs
Moerkerke, Yves Rosseel and Geert Verdoolaege
Depends: R (>= 2.10.1), Rniftilib, AnalyzeFMRI, deSolve
Description: 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