Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. For binary outcome, data can be sampled in prospective cohort studies or case-control studies. Described in Zhang et al. (2020)<doi:10.1093/biomet/asaa014>.

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>.

Data-driven approach for arriving at person-specific time series models from within a Graphical Vector Autoregression (VAR) framework. 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. All estimates are obtained uniquely for each individual in the final models. The method for the 'graphicalVAR' approach is found in Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.

Simplifies the process of creating essential visualizations
    in R, offering a range of plotting functions for common chart types
    like violin plots, pie charts, and histograms.  With an intuitive
    interface, users can effortlessly customize colors, labels, and
    styles, making it an ideal tool for both beginners and experienced
    data analysts. Whether exploring datasets or producing quick visual
    summaries, this package provides a streamlined solution for
    fundamental graphics in R.

This is a set of functions to retrieve information about GIMMS
    NDVI3g files currently available online; download (and re-arrange, in the 
    case of NDVI3g.v0) the half-monthly data sets; import downloaded files from 
    ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based 
    on the widespread 'raster' package; conduct quality control; and generate 
    monthly composites (e.g., maximum values) from the half-monthly input data. 
    As a special gimmick, a method is included to conveniently apply the 
    Mann-Kendall trend test upon 'Raster*' images, optionally featuring 
    trend-free pre-whitening to account for lag-1 autocorrelation.