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  "Title": "Block Forests: Random Forests for Blocks of Clinical and Omics\nCovariate Data",
  "Version": "0.2.7",
  "Date": "2026-01-14",
  "Authors@R": "c(\nperson(\"Marvin N.\", \"Wright\", email = \"cran@wrig.de\", role = c(\"aut\", \"cre\")),\nperson(\"Roman\", \"Hornung\", role = \"aut\"))",
  "Description": "A random forest variant 'block forest' ('BlockForest')\ntailored to the prediction of binary, survival and continuous\noutcomes using block-structured covariate data, for example,\nclinical covariates plus measurements of a certain omics data\ntype or multi-omics data, that is, data for which measurements\nof different types of omics data and/or clinical data for each\npatient exist. Examples of different omics data types include\ngene expression measurements, mutation data and copy number\nvariation measurements. Block forest are presented in Hornung &\nWright (2019). The package includes four other random forest\nvariants for multi-omics data: 'RandomBlock', 'BlockVarSel',\n'VarProb', and 'SplitWeights'. These were also considered in\nHornung & Wright (2019), but performed worse than block forest\nin their comparison study based on 20 real multi-omics data\nsets. Therefore, we recommend to use block forest\n('BlockForest') in applications. The other random forest\nvariants can, however, be consulted for academic purposes, for\nexample, in the context of further methodological developments.\nReference: Hornung, R. & Wright, M. N. (2019) Block Forests:\nrandom forests for blocks of clinical and omics covariate data.\nBMC Bioinformatics 20:358. <doi:10.1186/s12859-019-2942-y>.",
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