Provides a general toolkit for drug target identification. We include functionality to reduce large graphs to subgraphs and prioritize nodes. In addition to being optimized for use with generic graphs, we also provides support to analyze protein-protein interactions networks from online repositories. For more details on core method, refer to Weaver et al. (2021) <https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008755>.

Doubly robust methods for evaluating surrogate markers as outlined in: Agniel D, Hejblum BP, Thiebaut R & Parast L (2022). 
             "Doubly robust evaluation of high-dimensional surrogate markers", Biostatistics <doi:10.1093/biostatistics/kxac020>. You can use these methods to determine how much of the overall treatment effect is explained by a (possibly high-dimensional) set of surrogate markers.

Enables transformation of Verbal Autopsy data collected with the WHO 2016 questionnaire (versions 1.4.1 & 1.5.1)
  or the WHO 2014 questionnaire for automated coding of Cause of Death using the InSilicoVA (data.type = "WHO2016") and
  InterVA5 algorithms. Previous versions of this package supported user-supplied mappings (via the map_records function), but
  this functionality has been removed.  This package is made available by WHO and the Bloomberg Data for Health Initiative.

Contains generic functions for performing 
  cross validation and for computing diagnostic errors.

Defines classes and methods to cross-validate various
  binary classification algorithms used for "class prediction"
  problems.

Implements the cross-validation methodology from Pein and Shah (2021) <arXiv:2112.03220>. Can be customised by providing different cross-validation criteria, estimators for the change-point locations and local parameters, and freely chosen folds. Pre-implemented estimators and criteria are available. It also includes our own implementation of the COPPS procedure <doi:10.1214/19-AOS1814>.

A pair of functions for renaming and encoding data frames
	     using external crosswalk files. It is especially useful when
	     constructing master data sets from multiple smaller data
	     sets that do not name or encode variables consistently
	     across files. Based on similar commands in 'Stata'.