Iscores

Iscores provides scoring rules for evaluating and comparing imputation methods.

The package implements the methodology introduced in Näf et al. (2022) and Näf, Grzesiak, and Scornet (2025). The package supports:

• numerical datasets,
• mixed numerical and categorical data,
• deterministic imputations,
• stochastic and multiple imputations,
• comparison of several imputation methods.

For more details about the energy-I-Score check our vignettes:

• Energy-I-Score: Implementation Details
• Energy-I-Score: First Steps

Installation

CRAN

install.packages("Iscores")

Development version

install.packages("devtools")

# devtools::install_github("KrystynaGrzesiak/Iscores")
install.packages("Iscores", repos = c('https://krystynagrzesiak.r-universe.dev', 'https://cloud.r-project.org'))

Issues

If you encounter a bug, have a problem using Iscores, or would like to suggest an improvement, please open an issue.

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Basic workflow

The package evaluates user-defined imputation methods.

An imputation function must:

• accept a dataset with missing values,
• return a completed dataset with the same dimensions.

Below we define a simple zero-imputation method.

library(Iscores)

impute_zero <- function(X) {

  X[is.na(X)] <- 0

  X
}

We now generate example data with missing values.

set.seed(10)

X <- Iscores:::random_mcar_data(100, 4)

head(X)

Energy-I-Score

The energy_IScore() function evaluates the quality of an imputation method.

sc <- energy_IScore(
  X = X,
  imputation_func = impute_zero,
  N = 10,
  silent = TRUE
)

sc

Detailed variable-level results are stored as an attribute:

attr(sc, "dat")

DR-I-Score

The package also provides the density-ratio based DR-I-Score.

sc_dr <- DR_IScore(
  X = X,
  imputation_func = impute_zero,
  m = 3,
  n_proj = 10,
  n_trees_per_proj = 2,
  n_cores = 1
)

sc_dr

Comparing imputation methods

Several methods can be compared simultaneously using compare_Iscores().

library(mice)

impute_mice_norm <- function(X) {

  imp <- mice(
    X,
    m = 1,
    method = "norm",
    maxit = 5,
    printFlag = FALSE
  )

  complete(imp)
}

impute_mice_rf <- function(X) {

  imp <- mice(
    X,
    m = 1,
    method = "rf",
    maxit = 5,
    printFlag = FALSE
  )

  complete(imp)
}

methods_list <- list(
  zero = impute_zero,
  norm = impute_mice_norm,
  rf = impute_mice_rf
)

compare_Iscores(
  X = X,
  methods_list = methods_list,
  score = c("energy_IScore", "DR_IScore"),
  N = 10,
  m = 3,
  silent = TRUE
)

References

Näf, Jeffrey, Krystyna Grzesiak, and Erwan Scornet. 2025. “How to Rank Imputation Methods?” https://arxiv.org/abs/2507.11297.

Näf, Jeffrey, Meta-Lina Spohn, Loris Michel, and Nicolai Meinshausen. 2022. “Imputation Scores.” https://arxiv.org/abs/2106.03742.