wkNNMI is an R tool for the imputation of static and dynamic mixed-type data. A typical example of this kind of data are clinical registers containing subsequent screening visits for several patients.

This package implements an adaptive weighted k-nearest neighbours (wk-NN) imputation algorithm for clinical register data developed to explicitly handle missing values of continuous/ordinal/categorical and static/dynamic features conjointly. For each subject with missing data to be imputed, the method creates a feature vector constituted by the information collected over his/her first window_size time units of visits. This vector is used as sample in a k-nearest neighbours procedure, in order to select, among the other patients, the ones with the most similar temporal evolution of the disease over time. An ad hoc similarity metric was implemented for the sample comparison, capable of handling the different nature of the data, the presence of multiple missing values and include the cross-information among features.


The package requires the following R packages to function correctly: infotheo and foreach.

Installation from CRAN


Installation from GitLab

wkNNMI is available on GitLab at https://gitlab.com/sysbiobig/wkNNMI

To install wkNNMI from GitLab, please use the following commands:


Installation from source package

The wkNNMI R source package can be downloaded at http://sysbiobig.dei.unipd.it/?q=wkNNMI

To install it from source, please use the following command:

install.packages("wkNNMI_X.X.X.tar.gz", repos = NULL, type = "source")

where “X.X.X” indicates the package version.

Getting started

The package contains two functions impute.subject and impute.wknn. Load the package and check the documentation of the two functions.