We propose a procedure for sample size calculation while
    controlling false discovery rate for RNA-seq experimental design. Our
    procedure depends on the Voom method proposed for RNA-seq data analysis
    by Law et al. (2014) <DOI:10.1186/gb-2014-15-2-r29> and the sample size 
    calculation method proposed for microarray experiments by Liu and Hwang 
    (2007) <DOI:10.1093/bioinformatics/btl664>. We develop a set of functions
    that calculates appropriate sample sizes for two-sample t-test for RNA-seq
    experiments with fixed or varied set of parameters. The outputs also contain a
    plot of power versus sample size, a table of power at different sample sizes,
    and a table of critical test values at different sample sizes. 
    To install this package, please use 
    'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'. 
    For R version 3.5 or greater, please use  
    'if(!requireNamespace("BiocManager", quietly = TRUE)){install.packages("BiocManager")}; BiocManager::install("ssizeRNA")'.

Efficient coordinate ascent algorithm for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO of Rockova and George (2018) <doi:10.1080/01621459.2016.1260469>.

Implements a semi-supervised learning framework for finite mixture
    models under a mixed-missingness mechanism. The approach models both
    missing completely at random (MCAR) and entropy-based missing at random
    (MAR) processes using a logistic–entropy formulation. Estimation is carried
    out via an Expectation–-Conditional Maximisation (ECM) algorithm with robust
    initialisation routines for stable convergence. The methodology relates to
    the statistical perspective and informative missingness behaviour discussed
    in Ahfock and McLachlan (2020) <doi:10.1007/s11222-020-09971-5> and
    Ahfock and McLachlan (2023) <doi:10.1016/j.ecosta.2022.03.007>. The package
    provides functions for data simulation, model estimation, prediction, and
    theoretical Bayes error evaluation for analysing partially labelled data
    under a mixed-missingness mechanism.