CRAN/E | biokNN

biokNN

Bi-Objective k-Nearest Neighbors Imputation for Multilevel Data

Installation

About

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) () using a bi-objective approach. A brief description of the method can be found in Cubillos (2021) (). 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.

github.com/mcubillos3/biokNN
Bug report File report

Key Metrics

Version 0.1.0
R ≥ 2.10
Published 2021-04-22 1071 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks biokNN results

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Maintainer

Maintainer

Maximiliano Cubillos

mcub@econ.au.dk

Authors

Maximiliano Cubillos

aut / cre

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Depends

R ≥ 2.10

Imports

dplyr
cluster
mice
stats
magrittr
ggplot2
tidyr
desc
lme4
mitml

Suggests

knitr
rmarkdown
testthat