A new method to implement clustering from multiple modality data of certain samples, 
    the function M2SMjF() jointly factorizes multiple similarity matrices into a shared sub-matrix 
    and several modality private sub-matrices, which is further used for clustering. Along with 
    this method, we also provide function to calculate the similarity matrix and function to 
    evaluate the best cluster number from the original data.

Provides functions to read in and manipulate air quality model output from Models3-formatted files.  This format is used by the Community Multiscale Air Quality (CMAQ) model.

Multi modality data matrices are factorized conjointly into the multiplication of a shared sub-matrix and multiple modality specific sub-matrices, group sparse constraint is applied to the shared sub-matrix to capture the homogeneous and heterogeneous information, respectively. Then the samples are classified by clustering the shared sub-matrix with kmeanspp(), a new version of kmeans() developed here to obtain concordant results. The package also provides the cluster number estimation by rotation cost. Moreover, cluster specific features could be retrieved using hypergeometric tests.

Data manipulation in one package and in base R.
  Minimal. No dependencies.
  'dplyr' and 'tidyr'-like in one place.
  Nothing else than base R to build the package.

A computational method developed for model-based analysis of alternative polyadenylation (APA) using 3' end-linked reads. It accurately assigns 3' RNA-seq reads to polyA sites through statistical modeling, and generates multiple statistics for APA analysis. Please also see Li WV, Zheng D, Wang R, Tian B (2021) <doi:10.1186/s13059-021-02429-5>.

Provides an extension of the shadow-test approach to computerized adaptive
    testing (CAT) implemented in the 'TestDesign' package for the assessment framework
    involving multiple tests administered periodically throughout the year. This framework
    is referred to as the Multiple Administrations Adaptive Testing (MAAT) and supports
    multiple item pools vertically scaled and multiple phases (stages) of CAT within each test.
    Between phases and tests, transitioning from one item pool (and associated constraints)
    to another is allowed as deemed necessary to enhance the quality of measurement.