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  "Title": "Study Strap and Multi-Study Learning Algorithms",
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  "Description": "Implements multi-study learning algorithms such as\nmerging, the study-specific ensemble\n(trained-on-observed-studies ensemble) the study strap, the\ncovariate-matched study strap, covariate-profile similarity\nweighting, and stacking weights. Embedded within the 'caret'\nframework, this package allows for a wide range of single-study\nlearners (e.g., neural networks, lasso, random forests). The\npackage offers over 20 default similarity measures and allows\nfor specification of custom similarity measures for\ncovariate-profile similarity weighting and an accept/reject\nstep. This implements methods described in Loewinger, Kishida,\nPatil, and Parmigiani. (2019) <doi:10.1101/856385>.",
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