Package: Iscores 1.2.0

Iscores: Proper Scoring Rules for Missing Value Imputation

Provides tools for evaluating and ranking missing value imputation methods using proper scoring rules. Implements the Energy-I-Score and the DR-I-Score for the assessment of deterministic, stochastic and multiple imputation methods for numerical and mixed datasets, following Näf et al. (2022) <doi:10.48550/arXiv.2106.03742> and Näf et al. (2025) <doi:10.48550/arXiv.2507.11297>.

Authors:Krystyna Grzesiak [aut, cre], Loris Michel [aut, ctb], Meta-Lina Spohn [aut, ctb], Jeffrey Näf [aut, ctb]

Iscores_1.2.0.tar.gz
Iscores_1.2.0.zip(r-4.7-any)Iscores_1.2.0.zip(r-4.6-any)Iscores_1.2.0.zip(r-4.5-any)
Iscores_1.2.0.tgz(r-4.6-any)Iscores_1.2.0.tgz(r-4.5-any)
Iscores_1.2.0.tar.gz(r-4.7-any)Iscores_1.2.0.tar.gz(r-4.6-any)
Iscores_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
Iscores/json (API)

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

Bug tracker:https://github.com/krystynagrzesiak/iscores/issues

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

On CRAN:

Conda:

4.95 score 18 scripts 309 downloads 9 exports 19 dependencies

Last updated from:6b43e8746a. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK357
source / vignettesOK246
linux-releaseOK332
macos-releaseOK300
macos-oldrelOK319
windows-develOK338
windows-releaseOK333
windows-oldrelOK313
wasm-releaseOK100

Exports:compare_IscoresDR_IScoreedistanceenergy_IScoreexp_imputationmedian_mode_imputationnorm_imputationrandom_mcar_datarandom_mcar_mixed_data

Dependencies:bootenergyevaluategslhighrkernlabknitrlatticeMASSMatrixpbapplypbmcapplyrangerRcppRcppArmadilloRcppEigenscoringRulesxfunyaml

Energy-I-Score: First Steps
Introduction | Installation | Preparing data | Imputation Function | Calculating the energy-I-Score | Important parameters | Number of imputations: N | Limiting the number of scored variables: max_length | Handling incomplete training sets: skip_if_needed | Scaling variables: scale | Energy-I-Score for mixed data | Calculating the DR-I-Score | Parameters | Comparing multiple imputation methods | Comparing methods using multiple scores | Summary of best practices | Energy score | References

Last update: 2026-06-19
Started: 2026-05-20

Energy-I-Score: Implementation Details
Introduction | Notation | Algorithm Overview | Step 1: Selection of Predictor Set | Step 2: Data Partitioning | Step 3: Multiple Imputations | Step 4: Energy Distance Calculation | For each $i \in L_j$, the energy-I-Score component is computed as:[\widehat{S}^j_{\mathrm{NA}}(H,P) =\frac{1}{|L_j|} \sum_{i \in L_j}\left[\frac{1}{2N^2} \sum_{l=1}^N \sum_{\ell=1}^N|\tilde{X}{i,j}^{(l)} - \tilde{X}{i,j}^{(\ell)}| | Step 5: Weighting | Step 6: Final Score | Practical Interpretation | References

Last update: 2026-05-21
Started: 2026-05-20