The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.

Analysis of discrete response data using
    unidimensional and multidimensional item analysis models under the Item
    Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). 
    Exploratory and confirmatory item factor analysis models
	are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory
    bi-factor and two-tier models are available for modeling item testlets using
	dimension reduction EM algorithms, while multiple group analyses and 
	mixed effects designs are included for detecting differential item, bundle, 
	and test functioning, and for modeling item and person covariates. 
	Finally, latent class models such as the DINA, DINO, multidimensional latent class, 
	mixture, and zero-inflated response models are supported.

Provides tools to generate HTML interfaces for adaptive
    and non-adaptive tests using the shiny
    package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). 
    Suitable for applying unidimensional and multidimensional
    computerized adaptive tests (CAT) using item response theory methodology and for
    creating simple questionnaires forms to collect response data directly in R.
    Additionally, optimal test designs (e.g., "shadow testing") are supported
    for tests that contain a large number of item selection constraints.
    Finally, package contains tools useful for performing Monte Carlo simulations 
    for studying test item banks.