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>.

Providing a collection of techniques for semi-supervised 
    classification, regression and clustering. In semi-supervised problem, both labeled and unlabeled
    data are used to train a classifier. The package includes a collection of 
    semi-supervised learning techniques: self-training, co-training, democratic, 
    decision tree, random forest, 'S3VM' ... etc, with a fairly intuitive interface 
    that is easy to use.

Creates an S4 class "SSM" and defines functions for fitting smooth
    supersaturated models, a polynomial model with spline-like behaviour.
    Functions are defined for the computation of Sobol indices for sensitivity
    analysis and plotting the main effects using FANOVA methods. It also
    implements the estimation of the SSM metamodel error using a GP model with
    a variety of defined correlation functions.