R Observatory: Metrics for misl version 2.0.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

misl

2.0.0Package record

Multiple Imputation by Super Learning

0packages depend

2.5Kdownloads / year

83.4%test coverage

13/13flavors OK

Overview

About

Maintained by Justin ManjouridesCRAN page ↗GitHub ↗Issues ↗

Performs multiple imputation of missing data using an ensemble super learner built with the tidymodels framework. For each incomplete column, a stacked ensemble of candidate learners is trained on a bootstrap sample of the observed data and used to generate imputations via predictive mean matching (continuous), probability draws (binary), or cumulative probability draws (categorical). Supports parallelism across imputed datasets via the future framework.

Changes in 2.0.0

New features

misl() now accepts any parsnip-compatible model spec directly via the *_method arguments, allowing any learner in the tidymodels ecosystem to be used without requiring a package update. Named strings and parsnip specs can be freely mixed in the same list.
New ord_method argument in misl() for ordered categorical (ordinal) outcomes. Ordered factors are automatically detected and routed to ord_method.
New built-in learner "polr" (proportional odds logistic regression via MASS::polr()) for ordinal outcomes. Note that "polr" cannot currently be stacked with other ordinal learners — when supplied alongside other learners in ord_method, "polr" will be used as the sole ordinal learner and others will be ignored with a warning. Full stacking support for ordinal outcomes is planned for a future release.

Show all changes in 2.0.0Hide the rest of the changes in 2.0.0

New plot_misl_trace() function for visualising convergence of imputed values across iterations, with one line per imputed dataset.
list_learners() now accepts "ordinal" as a valid outcome_type filter and includes the new ordinal support column.

Bug fixes

cv_folds argument is now correctly passed through to the internal .fit_super_learner() function. Previously the value was accepted by misl() but ignored, with 5 folds always used regardless of the user's setting.
The preprocessing recipe is now built once from the full observed data and shared across both the bootstrap and full fits for continuous outcomes. Previously separate recipes were built for each fit, meaning step_zv() and step_nzv() could drop different predictors between the two fits and produce inconsistent PMM donor selection.
Two-level factors (e.g. "Yes"/"No") are now correctly identified as binomial outcomes rather than categorical. Previously these were routed to cat_method, which caused incorrect imputation and errors with learners that require a numeric binary outcome.
Trace statistics (mean, sd) are no longer computed for factor-coded binary columns, preventing errors from calling mean() and var() on factor vectors.
Stacking now gracefully recovers when individual learners fail during cross-validation resampling. Previously a single learner failure would crash the entire imputation. Failed learners are now skipped with a warning, and if all learners fail the first learner is used as a fallback.

From NEWS.md in the release

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("misl", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("JustinManjourides/misl")

Development version from JustinManjourides/misl. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-misl

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-misl

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-misl

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

CRAN check page ↗

Slowest check: 7.2 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK13 OK2026-06-09
ERROR12 OK, 1 ERROR2026-06-08
OK7 OK2026-03-31

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 2.0.0

READMEYes · 208 wordsVignettesYes · 1 · dynamicNEWSYes · 100% structured · mentions 2.0.0Citation fileNo

Help pages: 7 (4 internal)
Examples that run: 100%3 run in a plain check
Exported functions with examples: 100%
Documented parameters: 100%
Return-value docs: 100%
Help pages citing references: 0%

Vignettes (1)

Introduction to misl HTML

How these are measured

Downloads

2.5K

CRAN downloads in the past year

Rank #12,367 · ~7/day · ~210/mo

Releases R versions

Daily download trend is not available in this view yet.

35230 days

88690 days

2.5K1 year

Compare downloads with other packages →

Also on46 r2u6 autocran

How downloads are counted

Repository

justinmanjourides/misl ↗Account: JustinManjourides

0Stars

0Forks

0Open issues

0Open PRs

1Releases

30Commits

1Commit identities

30 commits · Last activity 2026-04-30

Responsiveness

not measured

Releases over time

2026-03-27 · 12026-07-07 · 1

How these are measured

PracticesBeta

7 development-tooling and community-health practices detected across 4 families in the upstream repository

Checks run against github.com/justinmanjourides/misl on 2026-10-11.

Continuous integration (1)

GitHub Actions

Docs source (3)

pkgdown

GitHub Pages site last deployed 2026-04-30 · The repository has a site deploy workflow

CRAN release process (2)

cran-comments.mdCRAN-SUBMISSION

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies

20 external dependencies (excludes base and recommended)

Installing misl adds 101 packages (48 with compiled code, 79 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.

Depends (1)

R >= 4.1.0

Imports (12)

dplyr future.apply parsnip (>= 1.2.0)recipes rsample stacks (>= 1.0.0)statstibble tidyr tune (>= 1.2.0)utilsworkflows

LinkingTo (0)

none

Suggests (11)

earth future ggforce ggplot2 knitrMASSranger rmarkdown scales testthat xgboost

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

People & History

People (2)

Justin Manjourides

Author, Maintainer since 1.0.0 (2026)

Thomas Carpenito

Author · added in 2.0.0

Releases (2)

Pick two releases to compare their code metrics.

R
R 4.6.0 released · 2026-04-24
2.0.0Latest
record
2026-04-08 · current release · diff ↗ ·
added 1 export
plot_misl_trace
1.0.0
record
2026-03-30
R
R 4.5.0 released · 2025-04-11

Package metadata

First published: 2026-03-30
Total releases: 2 / 1 yrs
License: MIT + file LICENSE OSI
Minimum R: ≥ 4.1.0
Download size: 32 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("misl")

Format

Manjourides, J., & Carpenito, T. (2026). misl: Multiple Imputation by Super Learning (Version 2.0.0) [Computer software]. https://doi.org/10.32614/CRAN.package.misl

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for misl version 2.0.0 [Data set]. HJJB, LLC. Data release v2026-10-18. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-18, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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