Stephanie Lane [aut, cre], Clark Jeffries [aut], Diana Perkins [aut]
Contains a greedy algorithm for coarse approximation linear function.

Contains a greedy algorithm for coarse approximation linear
function.

Contains greedy algorithms for coarse approximation linear
functions.

Stephanie Lane [aut, cre], John Ford [aut], Clark Jeffries [aut], Diana Perkins [aut]
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>.

Contains greedy algorithms for coarse approximation linear functions.

Multiple imputation using MICE and Random Forest
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).

c(person("Anoop", "Shah", role=c("aut", "cre"), email="anoop@doctors.org.uk"),
person("Jonathan", "Bartlett", role="ctb"),
person("Harry",  "Hemingway", role="ths"),
person("Owen", "Nicholas", role="ths"),
person("Aroon", "Hingorani", role="ths"))
Anoop Shah [aut, cre],
Jonathan Bartlett [ctb],
Harry Hemingway [ths],
Owen Nicholas [ths],
Aroon Hingorani [ths]

c(person("Anoop", "Shah", role=c("aut", "cre"), email="anoop@doctors.org.uk"),
person("Jonathan", "Bartlett", role="ctb"),
person("Harry", "Hemingway", role="ths"),
person("Owen", "Nicholas", role="ths"),
person("Aroon", "Hingorani", role="ths"))

Multiple Imputation Using MICE and Random Forest

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>.