A Method for Handling Missing Values in Prediction Applications
Overview
Non-imputational method for handling missing values in a prediction context, meaning that not only are there missing values in the training dataset, but also some values may be missing in future cases to be predicted. Based on the notion of regression averaging (Matloff (2017, ISBN: 9781498710916)).
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- NOTE12 OK, 1 NOTE2026-06-07
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- OK14 OK2026-03-10
Documentation
- Help pages
- 4includes a package overview page
- Examples that run
- 100%2 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
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Repository
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Dependencies
Installing toweranNA adds 104 packages (56 with compiled code, 91 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
Datasets
People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 0.1.0Latest2023-03-15 · current release
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2023-03-15
- Total releases
- 1 / 3 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.6.0
- Bundled data
- 32 KB / 1 file
- Download size
- 37 KB
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