A method for the quantitative prediction using omics data. 
    This package provides functions to construct the quantitative prediction 
    model using omics data.

Performs the O2PLS data integration method for two datasets, yielding joint and data-specific parts for each dataset.
    The algorithm automatically switches to a memory-efficient approach to fit O2PLS to high dimensional data.
    It provides a rigorous and a faster alternative cross-validation method to select the number of components,
    as well as functions to report proportions of explained variation and to construct plots of the results. 
    See the software article by el Bouhaddani et al (2018) <doi:10.1186/s12859-018-2371-3>, 
    and Trygg and Wold (2003) <doi:10.1002/cem.775>.
    It also performs Sparse Group (Penalized) O2PLS, see Gu et al (2020) <doi:10.1186/s12859-021-03958-3> and cross-validation for the degree of sparsity.

Provides unified workflows for quality control, normalization, and visualization of proteomic and metabolomic data. The package simplifies preprocessing through automated imputation, scaling, and principal component analysis (PCA)-based exploratory analysis, enabling researchers to prepare omics datasets efficiently for downstream statistical and machine learning analyses.

A method that analyzes quality control metrics from multi-sample genomic sequencing studies and nominates poor quality samples for exclusion. Per sample quality control data are transformed into z-scores and aggregated. The distribution of aggregated z-scores are modelled using parametric distributions. The parameters of the optimal model, selected either by goodness-of-fit statistics or user-designation, are used for outlier nomination. Two implementations of the Cosine Similarity Outlier Detection algorithm are provided with flexible parameters for dataset customization.

Processing and analyzing omics data from genomics, transcriptomics, proteomics, and metabolomics platforms. It provides functions for preprocessing, normalization, visualization, and statistical analysis, as well as machine learning algorithms for predictive modeling. 'omicsTools' is an essential tool for researchers working with high-throughput omics data in fields such as biology, bioinformatics, and medicine.The QC-RLSC (quality control–based robust LOESS signal correction) algorithm is used for normalization. Dunn et al. (2011) <doi:10.1038/nprot.2011.335>.