The modified Adult Treatment Panel -III guidelines (ATP-III) proposed by American Heart Association (AHA) and National Heart, Lung and Blood Institute (NHLBI) are used widely for the clinical diagnosis of Metabolic Syndrome. The AHA-NHLBI criteria advise using parameters such as waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), triglycerides (TG) and high-density lipoprotein cholesterol (HDLC) for diagnosis of metabolic syndrome. Each parameter has to be interpreted based on the proposed cut-offs, making the diagnosis slightly complex and error-prone. This package is developed by incorporating the modified ATP-III guidelines, and it will aid in the easy and quick diagnosis of metabolic syndrome in busy healthcare settings and also for research purposes. The modified ATP-III-AHA-NHLBI criteria for the diagnosis is described by Grundy et al ., (2005) <doi:10.1161/CIRCULATIONAHA.105.169404>.

A set of functions to investigate raw data from (metabol)omics experiments intended to be used on a raw data matrix, i.e. following peak picking and signal deconvolution. Functions can be used to normalize data, detect biomarkers and perform sample classification. A detailed description of best practice usage may be found in the publication <doi:10.1007/978-1-4939-7819-9_20>.

Provides a framework to perform soft clustering using 
        simplex-structured matrix factorisation (SSMF). The package contains a set of functions
        for determining the optimal number of prototypes, the optimal algorithmic
        parameters, the estimation confidence intervals and the diversity of clusters.
        Abdolali, Maryam & Gillis, Nicolas (2020) <doi:10.1137/20M1354982>.

Takes QC signal for each day and normalize metabolomic
    data that has been acquired in a certain period of time. At least
    three QC per day are required.

Contains functions that allow Bayesian meta-analysis (1) with binomial data, counts(y) and total counts (n) or, (2) with user-supplied point estimates and associated variances.   Case (1) provides an analysis based on the logit transformation of the sample proportion. This methodology is also appropriate for combining data from sample surveys and related sources. The functions can  calculate the corresponding similarity matrix. More details can be found in Cahoy and Sedransk (2023), Cahoy and Sedransk (2022)  <doi:10.1007/s42519-018-0027-2>, Evans and Sedransk (2001) <doi:10.1093/biomet/88.3.643>, and Malec and Sedransk (1992) <doi:10.1093/biomet/79.3.593>.