Title: 'Correctly Analyse Disease Recurrence with Missing at Risk
Description: Implements two methods: a nonparametric risk adjustment and a data imputation method that use general population mortality tables to allow a correct analysis of time to disease recurrence.

Title: Simulating and Analyzing Time to Event Data in the Presence of
Description: Implements two methods: a nonparametric risk adjustment and a
data imputation method that use general population mortality tables to allow a
correct analysis of time to disease recurrence. Also includes a powerful set of
object oriented survival data simulation functions.
Depends: survival, rms, relsurv, cmprsk, MASS, methods

Title: Comparing Observed and Imputed Values under MAR and MCAR
Author: Moritz Marbach <moritz.marbach@tamu.edu> [aut, cre]
Description: Implements the computation of discrepancy statistics summarizing differences between the density of imputed and observed values and the construction of weights to balance covariates that are part of the missing data mechanism as described in Marbach (2021) <arXiv:2107.05427>.
Imports: Formula (>= 1.2-3), cobalt (>= 4.1.0)

Title: Nonparametric Missing Value Imputation using Random Forest
Author: Daniel J. Stekhoven, <stekhoven@stat.math.ethz.ch>
Maintainer: Daniel J. Stekhoven, <stekhoven@stat.math.ethz.ch>
Description: The function 'missForest' in this package is used to
impute missing values particularly in the case of mixed-type
data. It uses a random forest trained on the observed values of
a data matrix to predict the missing values. It can be used to
impute continuous and/or categorical data including complex
interactions and nonlinear relations. It yields an out-of-bag
(OOB) imputation error estimate without the need of a test set

Description: The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and nonlinear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation.