The bi-objective k-nearest neighbors method (biokNN) is an imputation method designed to estimate missing values on data with a multilevel structure. The original algorithm is an extension of the k-nearest neighbors method proposed by Bertsimas et al. (2017) (<https://jmlr.org/papers/v18/17-073.html>) using a bi-objective approach. A brief description of the method can be found in Cubillos (2021) (<https://pure.au.dk/portal/files/214627979/biokNN.pdf>). The 'biokNN' package provides an R implementation of the method for datasets with continuous variables (e.g. employee productivity, student grades) and a categorical class variable (e.g. department, school). Given an incomplete dataset with such structure, this package produces complete datasets using both single and multiple imputation, including visualization tools to better understand the pattern of the missing values.
Package details |
|
---|---|
Author | Maximiliano Cubillos [aut, cre] (<https://orcid.org/0000-0002-2826-9728>) |
Maintainer | Maximiliano Cubillos <mcub@econ.au.dk> |
License | GPL (>= 2) |
Version | 0.1.0 |
URL | https://github.com/mcubillos3/biokNN |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.