Recent technological advances have enable the simultaneous collection
    of multi-omics data i.e., different types or modalities of molecular data, 
    presenting challenges for integrative prediction modeling due to the heterogeneous,
    high-dimensional nature and possible missing modalities of some individuals. 
    We introduce this package for late integrative prediction modeling, enabling 
    modality-specific variable selection and prediction modeling, followed by the 
    aggregation of the modality-specific predictions to train a final meta-model. 
    This package facilitates conducting late integration predictive modeling in a 
    systematic, structured, and reproducible way.

Use Rmarkdown First method to build your package. Start your
    package with documentation, functions, examples and tests in the same
    unique file. Everything can be set from the Rmarkdown template file
    provided in your project, then inflated as a package. Inflating the
    template copies the relevant chunks and sections in the appropriate
    files required for package development.

Enables high-dimensional penalized regression across heterogeneous 
    subgroups. Fusion penalties are used to share information about the linear 
    parameters across subgroups. The underlying model is described 
    in detail in Dondelinger and Mukherjee (2017) <arXiv:1611.00953>.

FusionCharts provides awesome and minimalist functions to make beautiful interactive charts <https://www.fusioncharts.com/>.

Provides the Big Merge Tracker and COSCI algorithms for convex clustering and 
    feature screening using L1 fusion penalty. See Radchenko, P. and Mukherjee, G. (2017) <doi:10.1111/rssb.12226> and 
    T.Banerjee et al. (2017) <doi:10.1016/j.jmva.2017.08.001> for more details.

The fusion learning method uses a model selection algorithm to learn from multiple data sets across different experimental platforms through group penalization. The responses of interest may include a mix of discrete and continuous variables. The responses may share the same set of predictors, however, the models and parameters differ across different platforms. Integrating information from different data sets can enhance the power of model selection. Package is based on Xin Gao, Raymond J. Carroll (2017) <arXiv:1610.00667v1>.