The EQ-5D is a widely-used standarized instrument for measuring Health Related Quality Of Life (HRQOL), 
    developed by the EuroQol group <https://euroqol.org/>. It assesses five dimensions; mobility, self-care, 
    usual activities, pain/discomfort, and anxiety/depression, using either a three-level (EQ-5D-3L) or five-level (EQ-5D-5L) scale.
    Scores from these dimensions are commonly converted into a single utility index using country-specific value sets, 
    which are critical in clinical and economic evaluations of healthcare and in population health surveys. 
    The eq5dsuite package enables users to calculate utility index values for the EQ-5D instruments, 
    including crosswalk utilities using the original crosswalk developed by van Hout et al. (2012) <doi:10.1016/j.jval.2012.02.008> 
    (mapping EQ-5D-5L responses to EQ-5D-3L index values), or the recently developed reverse crosswalk 
    by van Hout et al. (2021) <doi:10.1016/j.jval.2021.03.009> (mapping EQ-5D-3L responses
    to EQ-5D-5L index values). Users are allowed to add and/or remove user-defined value sets. 
    Additionally, the package provides tools to analyze EQ-5D data according to the recommended 
    guidelines outlined in "Methods for Analyzing and Reporting EQ-5D data" by Devlin et al. (2020) <doi:10.1007/978-3-030-47622-9>.

Computation of the EQL for a given family of variance
        functions, Saddlepoint-approximations and related auxiliary
        functions (e.g. Hermite polynomials).

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>.

The equality of a large number k of densities is tested by measuring the L2 distance between the corresponding kernel density estimators and the one based on the pooled sample. The test even works for sample sizes as small as 2.

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>.