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mixgb: multiple imputation through XGBoost

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# mixgb <a href="https://agnesdeng.github.io/mixgb/"><img src="man/figures/logo.png" align="right" height="138" alt="mixgb website" /></a>

[![Made with
R](https://img.shields.io/badge/Made%20With-R-9cf)](https://github.com/agnesdeng/mixgb)
[![CRAN
version](https://img.shields.io/cran/v/mixgb?color=9cf)](https://cran.r-project.org/package=mixgb)
[![CRAN
downloads](https://cranlogs.r-pkg.org/badges/grand-total/mixgb)](https://cranlogs.r-pkg.org/badges/grand-total/mixgb)
[![GitHub release (latest by
date)](https://img.shields.io/github/v/release/agnesdeng/mixgb?color=green)](https://github.com/agnesdeng/mixgb/releases)
[![R-CMD-check](https://github.com/agnesdeng/mixgb/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/agnesdeng/mixgb/actions/workflows/R-CMD-check.yaml)

The R package **mixgb** provides a scalable approach for multiple
imputation by leveraging XGBoost, subsampling and predictive mean
matching. We have shown that our method can yield less biased estimates
and reflect appropriate imputation variability, while achieving high
computational efficiency. For further details, see our paper [Multiple
Imputation Through XGBoost](https://www.yongshideng.com/papers).

## New updates

**New Development Version v2.2.3 on GitHub - Jan 2026**

- Support saving intermediate imputation summary statistics so that they
  can be passed to the [vismi](https://agnesdeng.github.io/vismi/)
  package for convergence diagnostics.

**New Release on CRAN - Dec 2025**

- New CRAN version 2.0.3. Now compatible with XGBoost (3.1.2.1) on CRAN.

- This update addresses breaking changes introduced in the latest
  XGBoost release on CRAN in December 2025. If you experience any
  problem, please post an issue on GitHub.

## 1. Installation

You can install the development version of mixgb from
[GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("agnesdeng/mixgb")
```

``` r
library(mixgb)
```

## 2. Sanity check input data before imputation

### Common issues

Please clean and check your data before imputation. Here are some common
issues:

- Data should be a data frame.
- ID should be removed
- Missing values should be coded as `NA` not `NaN`
- `Inf` or `-Inf` are not allowed
- Empty cells should be coded as `NA` or sensible values
- Variables of “character” type should be converted to “factor”
- Variables of “factor” type should have at least two levels

### Using `check_data()` for preliminary data check

The function `check_data()` performs a preliminary check and attempts to
fix some obvious issues.

- Step 1: `data` input needs to be a data.frame, tibble or data.table

- Step 2: convert character variables to factor type if
  `stringAsFactors = TRUE`

- Step 3: convert `NaN`, `"NaN"` to `NA`

- Step 4: convert `Inf`, `-Inf`, `"Inf"`, `"-Inf"` to `NA`

- Step 5: convert empty strings `""` to `NA`

- Step 6: check factor variables with only single level and ask user
  whether to remove them

- Step 7: check factor variables with too many levels (more than
  `max_levels`) and ask user whether to keep them

- Return: preliminarily checked data, with missing values encoded as NA.

**Please note that this function serves as a reminder of how important
data cleaning is and it almost surely cannot address all data quality
issues :)**

``` r
bad_data <- newborn
bad_data[, "ethnicity"] <- as.character(bad_data[, "ethnicity",
    drop = TRUE])
bad_data[2, "ethnicity"] <- "NaN"
bad_data[, "age_months"] <- as.factor(bad_data[, "age_months",
    drop = TRUE])
bad_data[, 1] <- ""
bad_data[, "sex"] <- "Unknown"
bad_data[4, "weight_kg"] <- NaN
bad_data[5, "weight_kg"] <- Inf

checked_data < -check_data(data = bad_data)
```

## 3. Impute missing values with `mixgb`

Please read:
<https://agnesdeng.github.io/mixgb/articles/Using-mixgb.html>

## 4. Impute new unseen data using a saved imputer object

Please read:
<https://agnesdeng.github.io/mixgb/articles/Imputing-newdata.html>

## 5. Visualisation Diagnostics for Multiple Imputation

It is crucial to assess the plausibility of imputations before doing any
analysis.

We have a standalone R package **vismi** ([Visualisation Diagnostics for
Multiple Imputation](https://agnesdeng.github.io/vismi/)) which provides
a comprehensive suite of diagnostics for assessing the quality of
multiply imputed data. The package supports imputed data generated by
various multiple imputation methods, including `mixgb`, `mice`, and
more.

For more details, please check: <https://agnesdeng.github.io/vismi/>

## 6. Install `mixgb` with GPU support

Multiple imputation can be run with GPU support for machines with NVIDIA
GPUs. Users must first install the R package `xgboost` with GPU support.

### 6.1 Newest Version

#### XGBoost \>= 3.1.2.1, mixgb \>= 2.0.3

XGBoost has recently introduced a breaking change. The current mixgb
release on CRAN (≥ 2.0.3) is compatible with XGBoost 3.1.2.1. GPU
support has not yet been fully tested with this update. **I’ll test GPU
support with this update as soon as I have time.**

### 6.2 Older Version

**Please refer to the instructions for the older version below if you
want to use GPU support with mixgb.**

#### XGBoost \>= 2.0.0, mixgb \>= 1.3.1

Please download the Newest version of XGBoost with GPU support via
[XGBoost GitHub Releases](https://github.com/dmlc/xgboost/releases).

``` r
# Change the file path where you saved the downloaded
# XGBoost package
install.packages("path_to_downloaded_file/xgboost_r_gpu_win64_2.0.0.tar.gz",
    repos = NULL)
```

Then users can install the newest version of our package `mixgb` in R.

``` r
devtools::install_github("agnesdeng/mixgb")
library(mixgb)
```

To utilise the GPU version of mixgb(), users can simply specify
`device = "cuda"` in the params list which will then be passed to the
`xgb.params` argument in the function `mixgb()`. Note that by default,
`tree_method = "hist"` from XGBoost 2.0.0.

``` r
params <- list(device = "cuda", subsample = 0.7, nthread = 1,
    tree_method = "hist")

mixgb.data <- mixgb(data = withNA.df, m = 5, xgb.params = params)
```

#### XGBoost \< 2.0.0, mixgb \< 1.3.1

The `xgboost` R package pre-built binary on Linux x86_64 with GPU
support can be downloaded from the release page
<https://github.com/dmlc/xgboost/releases/tag/v1.4.0>

The package can then be installed by running the following commands:

# Install dependencies
    $ R -q -e "install.packages(c('data.table', 'jsonlite'))"

# Install XGBoost
    $ R CMD INSTALL ./xgboost_r_gpu_linux.tar.gz

Then users can install package `mixgb` in R.

``` r
devtools::install_github("agnesdeng/mixgb")
library(mixgb)
```

To utilise the GPU version of mixgb(), users can simply specify
`tree_method = "gpu_hist"` in the params list which will then be passed
to the `xgb.params` argument in the function `mixgb()`. Other adjustable
GPU-related arguments include `gpu_id` and `predictor`. By default,
`gpu_id = 0` and `predictor = "auto"`.

``` r
params <- list(max_depth = 3, subsample = 0.7, nthread = 1, tree_method = "gpu_hist",
    gpu_id = 0, predictor = "auto")

mixgb.data <- mixgb(data = withNA.df, m = 5, xgb.params = params)
```

#### Notice

- For multithreading, users can set the XGBoost nthread parameter with
  OpenMP support. Be advised, OpenMP support is currently disabled on
  MacOS.

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