Distribution free heteroscedastic tests for functional data. 
    The following tests are included in this package: test of no main treatment or contrast
    effect and no simple treatment effect given in 
    Wang, Higgins, and Blasi (2010) <doi:10.1016/j.spl.2009.11.016>,
    no main time effect, and no interaction effect based on 
    original observations given in Wang and Akritas (2010a) 
    <doi:10.1080/10485250903171621>
    and tests based on ranks given in Wang and Akritas (2010b)
    <doi:10.1016/j.jmva.2010.03.012>.

A method for estimating the correlation matrix of the Gaussian copula from the observed data. This package also contains a penalized estimation of the corresponding precision matrix, and enables to generate random vectors that are distributed according to a Gaussian copula.

A comprehensive suite of spatial 
  functions created to analyze and assess data heterogeneity and climate variability 
  in spatial datasets. This package is specifically designed to address the challenges associated
  with characterizing and understanding complex spatial patterns in environmental and climate-related data.

The goal of this package is to user-friendly realizing Gaussian 
             graphical model-based heterogeneity analysis.
             Recently, several Gaussian graphical model-based heterogeneity 
             analysis techniques have been developed. A common methodological limitation 
             is that the number of subgroups is assumed to be known a priori, which 
             is not realistic. In a very recent study (Ren et al., 2022), a novel approach 
             based on the penalized fusion technique is developed to fully 
             data-dependently determine the number and structure of subgroups in 
             Gaussian graphical model-based heterogeneity analysis. It opens the door for utilizing 
             the Gaussian graphical model technique in more practical settings. Beyond 
             Ren et al. (2022), more estimations and functions are added, so 
             that the package is self-contained and more comprehensive and can 
             provide ``more direct'' insights to practitioners (with the 
             visualization function). Reference: 
             Ren, M., Zhang S., Zhang Q. and Ma S. (2022). Gaussian Graphical 
             Model-based Heterogeneity Analysis via Penalized Fusion. 
             Biometrics, 78 (2), 524-535.