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.

Implements a flexible, versatile, and computationally tractable model for density regression based on a single-weights dependent Dirichlet process mixture of normal distributions model for univariate continuous responses. The model assumes an additive structure for the mean of each mixture component and the effects of continuous covariates are captured through smooth nonlinear functions. The key components of our modelling approach are penalised B-splines and their bivariate tensor product extension. The proposed method can also easily deal with parametric effects of categorical covariates, linear effects of continuous covariates, interactions between categorical and/or continuous covariates, varying coefficient terms, and random effects. Please see Rodriguez-Alvarez, Inacio et al. (2025) for more details.

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.