An implementation of four stochastic methods of integrating in R, including:
    1. Stochastic Point Method (or Monte Carlo Method); 
    2. Mean Value Method; 
    3. Important Sampling Method; 
    4. Stratified Sampling Method.
    It can be used to estimate one-dimension or multi-dimension integration by Monte Carlo methods. And the estimated variance (precision) is given.
    Reference: Caflisch, R. E. (1998) <doi:10.1017/S0962492900002804>.

Package including additional modules for interactive
    'ShinyItemAnalysis' application for the psychometric analysis of
    educational tests, psychological assessments, health-related and other
    types of multi-item measurements, or ratings from multiple raters.

A comprehensive suite of functions designed for constructing
    and managing 'ShinyItemAnalysis' modules, supplemented with detailed
    guides, ready-to-use templates, linters, and tests. This package
    allows developers to seamlessly create and integrate one or more
    modules into their existing packages or to start a new module project
    from scratch.

Fits bi-variate ellipses to stable isotope data using Bayesian
    inference with the aim being to describe and compare their isotopic
    niche.

Provides models to identify bimodally expressed genes from
 RNAseq data based on the Bimodality Index. SIBERG models the  RNAseq data in
 the finite mixture modeling framework and incorporates mechanisms  for
 dealing with RNAseq normalization. Three types of mixture models are
 implemented,  namely, the mixture of log normal, negative binomial, or 
 generalized Poisson distribution. See Tong et al. (2013)
 <doi:10.1093/bioinformatics/bts713>.

Aims to quantify time intensity data by using sigmoidal and
    double sigmoidal curves. It fits straight lines, sigmoidal, 
    and double sigmoidal curves on to time vs intensity data. 
    Then all the fits are used to make decision on which model 
    best describes the data. This method was first developed 
    in the context of single-cell viral growth analysis (for
    details, see Caglar et al. (2018) <doi:10.7717/peerj.4251>),
    and the package name stands for "SIngle CEll Growth Analysis in R".