Multivariate Outlier Detection and Imputation for Incomplete Survey Data
Overview
Algorithms for multivariate outlier detection when missing values occur. Algorithms are based on Mahalanobis distance or data depth. Imputation is based on the multivariate normal model or uses nearest neighbour donors. The algorithms take sample designs, in particular weighting, into account. The methods are described in Bill and Hulliger (2016) doi:10.17713/ajs.v45i1.86.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 29 (10 internal)includes a package overview page
- Examples that run
- 100%18 run in a plain check
- Exported functions with examples
- 59%
- Documented parameters
- 99%
- Return-value docs
- 55%
- Help pages citing references
- 55%
Downloads
Repository
- PRs merged (all time)
- 100%
- Median days to close, 50 most recently updated closed issues
- not measured
- Median days to close, 50 most recently updated closed PRs
- 1
Stars over time
Forks over time
Releases over time
PRs over time
5 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/martinster/modi on 2026-10-22.
Dependencies
Installing modi adds 1 package (1 with compiled code, 19 KB of source tarballs). Heaviest: norm 19 KB.
Code & Tests
Datasets
People & History
Previously maintained by Martin Sterchi (0.1.0).
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 0.1.3Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 0.1.22023-03-14 · diff ↗
- 0.1.1
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 0.1.02018-11-20
- RR 3.5.0 released · 2018-04-23
Package metadata
- First published
- 2018-11-20
- Total releases
- 4 / 8 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 330 KB / 5 files
- Download size
- 454 KB
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