Provides computational tools to generate efficient blocked and unblocked
    fractional factorial designs for two-level and three-level factors using the
    generalized Minimum Aberration (MA) criterion and related optimization algorithms.
    Methodological foundations include the general theory of minimum aberration as
    described by Cheng and Tang (2005) <doi:10.1214/009053604000001228>, and the 
    catalogue of three-level regular fractional factorial designs developed by Xu 
    (2005) <doi:10.1007/s00184-005-0408-x>. The main functions dol2() and dol3() generate
    blocked two-level and three-level fractional factorial designs, respectively, using
    beam search, optimization-based ranking, confounding assessment, and structured 
    output suitable for complete factorial situations.

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>.