R Observatory: Metrics for llmimpute version 0.1.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

llmimpute

0.1.0Package record

Missing Data Imputation via Language Models and Statistics

0packages depend

1.1Kdownloads / year

62.9%test coverage

13/13flavors OK

Overview

About

Maintained by Sadikul IslamCRAN page ↗Issues ↗

Provides missing data imputation through two complementary engines: a large language model engine that communicates with the 'Anthropic' 'Claude' application programming interface for context-aware semantic imputation, and a fully self-contained offline engine implementing nineteen statistical and machine learning algorithms entirely in base R with no additional package dependencies. Offline methods include mean, median, mode, last observation carried forward, next observation carried backward, hot-deck, predictive mean matching, k-nearest neighbours, ordinary least-squares regression, Lasso with coordinate descent, Ridge with closed-form solution, Bayesian Ridge regression with evidence approximation following MacKay (1992), support vector regression with a radial basis function kernel, classification and regression trees, random forests, gradient boosting, iterative random forest imputation, principal component analysis imputation via iterative singular value decomposition, and nuclear-norm minimisation via singular value thresholding. When no API key is available the package automatically falls back to the offline engine, ensuring full operation in environments without internet access. Every imputed value is accompanied by a confidence score and a plain-language reasoning string, producing reproducible audit trails. The automatic method selector chooses the best algorithm per column based on data type, skewness, missingness rate, and inter-column correlations.

Changes in 0.1.0

Initial CRAN release

New features

lmi_impute() — unified imputation entry point. Automatically selects between the LLM engine (Anthropic Claude API) and the offline statistical engine based on API key availability.
lmi_impute_offline() — fully self-contained imputation using nineteen algorithms implemented in base R. No internet connection or API key required.
lmi_diagnose() — local missingness audit: reports missing counts, percentages, and column types without any API calls.
lmi_set_api_key() / lmi_get_api_key() — session-scoped API key management via options() or the ANTHROPIC_API_KEY environment variable.

Show all changes in 0.1.0Hide the rest of the changes in 0.1.0

lmi_set_model() / lmi_get_model() — select the Anthropic Claude model used for LLM imputation.
lmi_export() — write the imputed data frame and audit trail to CSV or RDS files.
lmi_methods() — print a formatted catalogue of all available offline imputation methods with usage guidance.
S3 methods print.lmi_result(), summary.lmi_result(), and as.data.frame.lmi_result() for the lmi_result class.
Nineteen offline imputation algorithms:
Classical: mean, median, mode, LOCF, NOCB, hot-deck, PMM
Regression: linear, Lasso (coordinate descent), Ridge (closed form), Bayesian Ridge (evidence approximation)
Kernel/neighbour: KNN (scaled Euclidean), SVR (RBF kernel)
Tree/ensemble: decision tree (CART), random forest, gradient boosting
Iterative: MissForest
Matrix completion: PCA imputation (iterative SVD), SoftImpute (nuclear-norm minimisation)

From NEWS.md in the release

Install

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

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

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

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-llmimpute

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-llmimpute

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-llmimpute

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: 1.5 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK8 OK2026-06-24

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.1.0

READMENoVignettesYes · 1 · staticNEWSYes · 67% structured · mentions 0.1.0Citation fileNoWebsitenot declared in DESCRIPTION

Help pages: 14 (2 internal)includes a package overview page
Examples that run: 92%11 run in a plain check · 1 never run
Exported functions with examples: 100%
Documented parameters: 98%
Return-value docs: 100%
Help pages citing references: 0%

Vignettes (1)

Getting started with llmimpute HTML

How these are measured

Downloads

1.1K

CRAN downloads in the past year

Rank #16,654 · ~3/day · ~91/mo

Releases R versions

Daily download trend is not available in this view yet.

27630 days

76190 days

1.1K1 year

Compare downloads with other packages →

Also on22 r2u6 autocran

How downloads are counted

Dependencies

Declared dependencies

7 external dependencies (excludes base and recommended)

Installing llmimpute adds 14 packages (10 with compiled code, 6.1 MB of source tarballs). Heaviest: openssl 1.1 MB, jsonlite 1.0 MB, vctrs 1.0 MB.

Depends (1)

R >= 4.1.0

Imports (4)

httr2methodsjsonlite cli (>= 3.6.0)

LinkingTo (0)

none

Suggests (4)

testthat knitr rmarkdown withr

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)

Sadikul Islam

Author, Maintainer since 0.1.0 (2026)

Rajesh Kaushal

Author

Releases (1)

0.1.0Latest
record
first seen 2026-06-23 · current release
R
R 4.6.0 released · 2026-04-24

Package metadata

First seen: 2026-06-23
Total releases: 1 / 1 yrs
License: MIT + file LICENSE OSI
Minimum R: ≥ 4.1.0
Download size: 43 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("llmimpute")

Format

Islam, S., & Kaushal, R. (2026). llmimpute: Missing Data Imputation via Language Models and Statistics (Version 0.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.llmimpute

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 llmimpute version 0.1.0 [Data set]. HJJB, LLC. Data release v2026-10-24. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-24, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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