A random forest variant 'block forest' ('BlockForest') tailored to the 
  prediction of binary, survival and continuous outcomes using block-structured 
  covariate data, for example, clinical covariates plus measurements of a certain 
  omics data type or multi-omics data, that is, data for which measurements of 
  different types of omics data and/or clinical data for each patient exist. Examples 
  of different omics data types include gene expression measurements, mutation data
  and copy number variation measurements.
  Block forest are presented in Hornung & Wright (2019). The package includes four
  other random forest variants for multi-omics data: 'RandomBlock', 'BlockVarSel', 
  'VarProb', and 'SplitWeights'. These were also considered in Hornung & Wright (2019), 
  but performed worse than block forest in their comparison study based on 20 real 
  multi-omics data sets. Therefore, we recommend to use block forest ('BlockForest') 
  in applications. The other random forest variants can, however, be consulted for 
  academic purposes, for example, in the context of further methodological 
  developments. 
  Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. <doi:10.1186/s12859-019-2942-y>.

A set of functions to select the optimal block-length for a 
    dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing
    (1995) <doi:10.1093/biomet/82.3.561> cross-validation method and the 
    Politis and White (2004) <doi:10.1081/ETC-120028836> Spectral Density 
    Plug-in method, including the Patton, Politis, and White (2009)
    <doi:10.1080/07474930802459016> correction with a corresponding set of S3
    plot methods.

Some elementary matrix algebra tools are implemented to manage
    block matrices or partitioned matrix, i.e. "matrix of matrices"
    (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here
    defined as a new S3 object. In this package, some methods for "matrix"
    object are rewritten for "blockmatrix" object. New methods are implemented.
    This package was created to solve equation systems with block matrices for
    the analysis of environmental vector time series .
    Bugs/comments/questions/collaboration of any kind are warmly welcomed.