Author: Stephanie Lane [aut, cre], John Ford [aut], Clark Jeffries [aut], Diana Perkins [aut]
Description: Forward selection linear regression greedy algorithm where selection is driven by optimization of Welch t-test p-value or AUC (binary dependent vector), or Pearson correlation (nonbinary). Functions now enabled include data outputs for permutation tests of several types plus cross-validation features. Please see preprint at <https://www.biorxiv.org/content/10.1101/2020.03.27.011700v1> or <doi:10.1101/2020.03.27.011700>.

Description: Contains greedy algorithms for coarse approximation linear functions.

Title: Multiple imputation using MICE and Random Forest
Description: Functions to impute using Random Forest under Full Conditional Specifications (Multivariate Imputation by Chained Equations). The CALIBER programme is funded by the Wellcome Trust (086091/Z/08/Z) and the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research programme (RP-PG-0407-10314). The author is supported by a Wellcome Trust Clinical Research Training Fellowship (0938/30/Z/10/Z).

Suggests: missForest, rpart, survival, xtable

Authors@R: c(person("Anoop", "Shah", role=c("aut", "cre"), email="anoop@doctors.org.uk"),

Title: Multiple Imputation Using MICE and Random Forest

Description: Functions to impute using random forest under full conditional specifications (multivariate imputation by chained equations). The methods are described in Shah and others (2014) <doi:10.1093/aje/kwt312>.

Title: R interface to Fortran 77 implementation of CALINE3

Depends: glmnet, mboost, Matrix, parallel, mgcv
Description: The code takes an n x p data matrix and fits a Causal Additive Model (CAM) for estimating the causal structure of the underlying process. The output is a p x p adjacency matrix (a one in entry (i,j) indicates an edge from i to j). Details of the algorithm can be found in: P. Bühlmann, J. Peters, J. Ernest: "CAM: Causal Additive Models, high-dimensional order search and penalized regression", Annals of Statistics 42:2526-2556, 2014.