The first 300 rows belong to the database A, while the next 400 rows belong to the database B. Five covariates: Gender, Treatment, Dosage, Smoking and Age are common to both databases (same encodings). Gender is the only complete covariate. The variables Yb1 and Yb2 are the target variables of A and B respectively, summarizing a same information encoded in two different scales. that summarize a same information saved in two distinct encodings, that is why, Yb1 is missing in the database B and Yb2 is missing in the database A.



A data.frame made of 2 overlayed databases (A and B) with 700 observations on the following 8 variables.


the database identifier, a character with 2 possible classes: A or B


the target variable of the database A, stored as factor and encoded in 3 ordered levels: [20-40], [40-60[,[60-80] (the values related to the database B are missing)


the target variable of the database B, stored as integer (an unknown scale from 1 to 5) in the database B (the values related to A are missing)


a factor with 2 levels (Female or Male) and no missing values


a covariate of 3 classes stored as a character with 2% of missing values: Placebo, Trt A, Trt B


a factor with 4 levels and 5% of missing values: from Dos 1 to dos 4


a covariate of 2 classes stored as a character and 10% of missing values: NO for non smoker, YES otherwise


a numeric corresponding to the age of participants in years. This variable counts 5% of missing values


randomly generated


The purpose of the functions contained in this package is to predict the missing information on Yb1 and Yb2 in database A and database B using the Optimal Transportation Theory.

Missing information has been simulated to some covariates following a simple MCAR process.