Estimate Graphical Models from Multiple Types of Datasets and
Construct Networks
Provides an equivalent measure of partial correlation coefficients for high-dimensional Gaussian Graphical Models to learn and visualize the underlying relationships between variables from single or multiple datasets. You can refer to <doi:10.1080/01621459.2015.1012391> for more detail. Based on this method, the package also provides the method for constructing networks for Next Generation Sequencing Data.

igraph, huge, XMRF, ZIM, mvtnorm, speedglm, SIS, ncvreg,
survival, bnlearn,doParallel, parallel, foreach
Provides an equivalent measure of partial correlation coefficients for high-dimensional Gaussian Graphical Models to learn and visualize the underlying relationships between variables from single or multiple datasets. You can refer to Liang, F., Song, Q. and Qiu, P. (2015) <doi:10.1080/01621459.2015.1012391> for more detail. Based on this method, the package also provides the method for constructing networks for Next Generation Sequencing Data, jointly estimating multiple Gaussian Graphical Models, constructing single graphical model for heterogeneous dataset, inferring graphical models from high-dimensional missing data and estimating moral graph for Bayesian network.

Testing the Equality of Two Covariance Matrices
Jun Li [aut, cre],
Song Xi Chen [aut],
Lingjun Li [ctb],
Clay James [ctb]
Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) <arXiv:1206.0917>.

Examine Measurement Invariance via Equivalence Testing and
Projection Method
Functions for examining measurement invariance via equivalence testing along with adjusted RMSEA(root mean square error of approximation; Steiger & Lind, 1980) cutoff values. In particular, a projection-based method is implemented to test the equality of latent factor means across groups without assuming the equality of intercepts.

c(person("Ge", "Jiang", role = c("aut", "cre"),
email = "gejiang2@illinois.edu"),
person("Yujiao", "Mai", role = "aut", email = "ymai@nd.edu"),
person("Ke-Hai", "Yuan", role = "ctb", email = "kyuan@nd.edu"))
Functions for examining measurement invariance via equivalence testing are included in this package. The traditionally used RMSEA (Root Mean Square Error of Approximation) cutoff values are adjusted based on simulation results. In addition, a projection-based method is implemented to test the equality of latent factor means across groups without assuming the equality of intercepts. For more information, see Yuan, K. H., & Chan, W. (2016) <doi:10.1037/met0000080>, Deng, L., & Yuan, K. H. (2016) <doi:10.1007/s11336-015-9491-8>, and Jiang, G., Mai, Y., & Yuan, K. H. (2017) < doi:10.3389/fpsyg.2017.01823>.
Ge Jiang [aut, cre],
Yujiao Mai [aut],
Ke-Hai Yuan [ctb]