Functions for metabolomics data analysis: data preprocessing, 
  orthogonal signal correction, PCA analysis, PCA-DA analysis, 
	PLS-DA analysis, classification, feature selection, correlation 
	analysis, data visualisation and re-sampling strategies.

Build multiscalar territorial analysis based on various contexts.

Developed a data-driven estimation framework for the multi-threshold accelerate failure time (MTAFT) model. The MTAFT model features different linear forms in different subdomains, and one of the major challenges is determining the number of threshold effects. The package introduces a data-driven approach that utilizes a Schwarz' information criterion, which demonstrates consistency under mild conditions. Additionally, a cross-validation (CV) criterion with an order-preserved sample-splitting scheme is proposed to achieve consistent estimation, without the need for additional parameters. The package establishes the asymptotic properties of the parameter estimates and includes an efficient score-type test to examine the existence of threshold effects. The methodologies are supported by numerical experiments and theoretical results, showcasing their reliable performance in finite-sample cases.

Dealing with neutrosophic data in single valued form using score,
    accuracy and certainty functions to calculate ranks of Single Valued 
    Neutrosophic Set (SVNS), also to calculate the  Mann-Whitney test,
    and making a post-hoc test after rejecting the null hypothesis using the Neutrosophic
    Statistics Kruskal-Wallis test. For more information see
    Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022)
    <https://digitalrepository.unm.edu/nss_journal/vol51/iss1/60/>.

Perform multi-trait rare-variant association tests using the summary statistics and adjust for possible sample overlap. Package is based on "Multi-Trait Analysis of Rare-Variant Association Summary Statistics using MTAR" by Luo, L., Shen, J., Zhang, H., Chhibber, A. Mehrotra, D.V., Tang, Z., 2019 (submitted).