Author: Stephanie Lane [aut, cre, trl], Kathleen Gates [aut], Peter Molenaar
Depends: R (>= 3.0.2), lavaan (>= 0.5-17), qgraph, igraph

Imports: lavaan (>= 0.5-17), MASS, doSNOW, foreach, snow, igraph,

Imports: lavaan (>= 0.5-19), MASS, doSNOW, foreach, snow, igraph,

Imports: lavaan (>= 0.5-19), igraph, qgraph, gWidgets

Imports: lavaan (>= 0.5-19), igraph, qgraph

Imports: lavaan (>= 0.5-19), igraph, qgraph, data.tree, MIIVsem,

Imports: lavaan (>= 0.5-19), igraph (>= 1.0-0), qgraph, data.tree,

MIIVsem(>= 0.5.4), imputeTS(>= 3.0), nloptr, graphics, stats

individual-level relations in time series data.

Imports: lavaan (>= 0.6-9), igraph (>= 1.0-0), qgraph, data.tree,

Maintainer: Kathleen M Gates <gateskm@email.unc.edu>
Description: Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.

MIIVsem(>= 0.5.4), imputeTS(>= 3.0), nloptr, graphics, stats,

Title: Supplemental Tools for the 'gimme' R Package
Authors@R: person("Stephanie", "Lane", email = "lane.stephanie.t@gmail.com", role = c("aut", "cre"))
Description: Supplemental tools for the 'gimme' R package. It contains an interactive graphical user interface, allowing for the flexible specification of a variety of both basic and advanced options. It will expand to include a variety of tools for navigating output.
Imports: shiny, shinyjs, rintrojs, easycsv, miniUI, shinyWidgets,

Title: Download and Process GIMMS NDVI3g Data
We provide a set of functions to retrieve information about GIMMS NDVI3g
files currently available online; download and re-arrange the bi-monthly
datasets according to creation time; import downloaded files from native
binary (ENVI) format directly into R based on the widely applied 'raster'
package; and calculate monthly value composites (e.g. maximum value
composites, MVC) from the bi-monthly input data.