Artifacts using qgraph version 1.4.4-b20

This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.

This permutation based hypothesis test, suited for Gaussian and binary data, assesses the difference between two networks based on several invariance measures (network structure invariance, global strength invariance, edge invariance). Network structures are estimated with l1-regularized partial correlations (Gaussian data) or with l1-regularized logistic regression (eLasso, binary data). Suited for comparison of independent and dependent samples (currently, only for one group measured twice).

Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see linked paper.

The core function of this R package is to provide the implementation of the well-cited and well-reviewed QUBIC algorithm, aiming to deliver an effective and efficient biclustering capability. This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters ...

We proposed a hybrid approach using the computational and statistical resources of the Bayesian Networks to learn a network structure from a data set using 4 different algorithms and the robustness of the statistical methods present in the Structural Equation Modeling to check the goodness of fit from model over data. We built an intermediate algorithm to join the features of 'bnlearn' and 'lavaan' R packages. The Bayesian Networks structure learning algorithms used were 'Hill-Climbing', 'Max-Min ...

Methods to create tree models with correlation-based network models (multivariate normal distributions).

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.

Description: Uses k-fold cross-validation and elastic-net regularization to estimate the Ising model on binary data. Produces 3D plots of the cost function as a function of the tuning parameter in addition to the optimal network structure.

Includes assorted tools for network analysis. Useful for calculating and plotting expected influence, bridge centrality, and impact statistics.

Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003, <doi:10.1037/0022-3514.85.2.197>) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015, <doi:10.1371/journal.pone.0129601>).