Package: missr 1.0.1.9000

missr: Classify Missing Data as MCAR, MAR, or MNAR

Classify missing data as missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). This step is required before handling missing data (e.g. mean imputation) so that bias is not introduced. See Little (1988) <doi:10.1080/01621459.1988.10478722> for the statistical rationale for the methods used.

Authors:Noah William Trelawny Hellen [aut, cre, cph]

missr_1.0.1.9000.tar.gz
missr_1.0.1.9000.zip(r-4.7)missr_1.0.1.9000.zip(r-4.6)missr_1.0.1.9000.zip(r-4.5)
missr_1.0.1.9000.tgz(r-4.6-any)missr_1.0.1.9000.tgz(r-4.5-any)
missr_1.0.1.9000.tar.gz(r-4.7-any)missr_1.0.1.9000.tar.gz(r-4.6-any)
missr_1.0.1.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
missr/json (API)

# Install 'missr' in R:
install.packages('missr', repos = c('https://noahhellen.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/noahhellen/missr/issues

Pkgdown/docs site:https://noahhellen.github.io

Datasets:

On CRAN:

Conda:

4.30 score 2 stars 1 scripts 386 downloads 3 exports 11 dependencies

Last updated from:de8a7bce98. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK132
source / vignettesOK185
linux-release-x86_64OK125
macos-release-arm64OK123
macos-oldrel-arm64OK145
windows-develOK66
windows-releaseOK65
windows-oldrelOK78
wasm-releaseOK116

Exports:marmcarmnar

Dependencies:cligluelifecyclemagrittrnormpillarpkgconfigrlangtibbleutf8vctrs

Background
Assumptions about missingness | Probabilistic interpretation | Mathematical formalism | References
Rendered frombackground.Rmdusingknitr::rmarkdown

Last update: 2025-05-13
Started: 2025-05-06

Introduction to missr
What is missing data? | The problem of missing data | Why can I not just impute the missing data? | How does missr work? | Why was missr created? | References
Rendered fromintroduction.Rmdusingknitr::rmarkdown

Last update: 2025-05-13
Started: 2025-05-06