The main function is icweib(), which fits a stratified Weibull
    proportional hazards model for left censored, right censored, interval censored,
    and non-censored survival data. We parameterize the Weibull regression model
    so that it allows a stratum-specific baseline hazard function, but where the
    effects of other covariates are assumed to be constant across strata. 
    Please refer to Xiangdong Gu, David Shapiro, Michael D. Hughes and
    Raji Balasubramanian (2014) <doi:10.32614/RJ-2014-003> for more details.

API for fast data extraction for .hic files that provides programmatic access to the matrices. It doesn't store the pointer data for all the matrices, only the one queried, and currently we are only supporting matrices (not vectors).

This is a modification of 'HDoutliers' package. The 'HDoutliers' algorithm is a powerful 
    unsupervised algorithm for detecting anomalies in high-dimensional data, with a 
    strong theoretical foundation. However, it suffers from some limitations that 
    significantly hinder its performance level, under certain circumstances. This package 
    implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019) 
    <arXiv:1908.04000>  for detecting anomalies in high-dimensional data
    that addresses these limitations of 'HDoutliers' algorithm. We define an anomaly as an observation that deviates markedly from the majority
    with a large distance gap. An approach based on extreme value theory is used 
    for the anomalous threshold calculation.

Performs receptor abundance estimation for single cell RNA-sequencing data using a supervised feature selection mechanism and a thresholded gene set scoring procedure. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for reduced rank reconstruction and rank-k selection is detailed in: Javaid et al., (2022) <doi:10.1101/2022.10.08.511197>. Gene set scoring procedure is described in: Frost et al., (2020) <doi:10.1093/nar/gkaa582>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.