Bootstrap methods to assess accuracy and stability of estimated network structures and centrality indices. Allows for flexible specification of any undirected network estimation procedure in R, and offers default sets for 'qgraph', 'IsingFit', 'IsingSampler', 'glasso', 'huge' and 'parcor' packages.

Changes in version 0.3: - Eiko Fried joined the author list - Added 'estimateNetwork' function, allowing one to estimate the network structure from within bootnet - The plot method will run qgraph on the estimated network structure - The qgraph function getWmat can now be applied to networks estimated in bootnet. Allowing one to use, e.g., centralityPlot on a network estimated with estimateNetwork - Added 'differenceTest' function to test for significant differences between edge weights and centrality indices - Added 'corStability' to compute the CS-coefficient as described in our paper: - Epskamp, S., Borsboom, D., & Fried, E. I. (2016). Estimating psychological networks and their accuracy: a tutorial paper. arXiv preprint, arXiv:1604.08462. - The plot method now supports 'plot = "difference"', to make plots of significant differences between edge-weights and centralities - New default sets: - "huge" - "adalasso" - 'nCores' argument added to bootnet to use parallel computing - bootnet print methods now print a list of relevant references on the network estimation procedure used - When EBICglasso is used as default set, variables that are made ordinal are now printed only when estimating the first network - Updated CITATION such that citation("bootnet") now references the pre-print - Bootnet now gives a message on loading that it is BETA software - Added 'statistics' argument to bootnet. Now, distance and length are not stored by default - Several minor bugfixes