Functions designed to connect disease-related differential proteins and 
  co-expression network. It provides the basic statics analysis included t test, ANOVA analysis. 
  The network construction is not offered by the package, you can used 'WGCNA' package which you 
  can learn in Peter et al. (2008) <doi:10.1186/1471-2105-9-559>. It also provides module analysis 
  included PCA analysis, two enrichment analysis, Planner maximally filtered graph extraction and 
  hub analysis.

Provides an implementation of the framework of reversed graph embedding (RGE) which projects data into a reduced dimensional space while constructs a principal tree which passes through the middle of the data simultaneously. DDRTree shows superiority to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic structure of the single cell genomics data. In general, it could be used to reconstruct the temporal progression as well as bifurcation structure of any datatype.

A sparse Partial Least Squares implementation which uses soft-threshold
	estimation of the covariance matrices and therein introduces
	sparsity. Number of components and regularization coefficients are automatically set.

Smooth testing of goodness of fit. These tests are data
    driven (alternative hypothesis is dynamically selected based on data). In this
    package you will find various tests for exponent, Gaussian, Gumbel and uniform
    distribution.

Implements a Bayesian algorithm for overcoming weak separation in Bayesian latent class analysis. 
    Reference: Li et al. (2023) <arXiv:2306.04700>.

Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,<doi:10.3390/s6010049>). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms.

Bayesian networks with continuous and/or discrete
        variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.