### futuremice Usage Example

Source: https://github.com/amices/mice/blob/master/docs/reference/futuremice.html

Demonstrates the basic usage of the futuremice function. Ensure the 'mice' and 'furrr' packages are installed and loaded.

```R
library(mice)
library(furrr)

# Example data with missing values
data <- nhanes2

# Run futuremice with default settings

# Set a seed for reproducibility
set.seed(123)

# Run futuremice

# Use a specific number of cores (e.g., 4)
# futuremice(data, m = 10, n.core = 4)

# Use default number of cores
# futuremice(data, m = 10)

# Example with specific packages and globals
# futuremice(data, m = 5, packages = "mice", globals = "my_impute_function")

# Example with a different future plan
# futuremice(data, m = 5, future.plan = "sequential")

# Example with logical CPUs
# futuremice(data, m = 5, use.logical = TRUE)

# Example with physical CPUs
# futuremice(data, m = 5, use.logical = FALSE)

# Example with a specific seed for the mice algorithm
# futuremice(data, m = 5, seed = 42)

# Example with a seed for parallel execution
# futuremice(data, m = 5, parallelseed = 12345)

# Example passing additional arguments to mice
# futuremice(data, m = 5, maxit = 10, printFlag = FALSE)
```

--------------------------------

### Mi4p Devel Installation: Binary vs Source Packages

Source: https://github.com/amices/mice/blob/master/revdep/failures.md

This output shows that binary versions of some packages are available, but their source versions are newer and require compilation. The system will proceed with installing the binary versions.

```bash
There are binary versions available but the source versions are later:
          binary source needs_compilation
MsCoreUtils 1.21.0 1.22.1              TRUE
S4Arrays    1.10.0 1.10.1              TRUE
SparseArray 1.10.2 1.10.6              TRUE

  Binaries will be installed

The downloaded binary packages are in
	/var/folders/5_/g85d42yj50b6lrjq4rzjzg8w0000gn/T//Rtmp8AWm9i/downloaded_packages

trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/affy_1.88.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/affyio_1.80.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/AnnotationDbi_1.72.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/AnnotationFilter_1.34.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/BiocBaseUtils_1.12.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/BiocFileCache_3.0.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/biomaRt_2.66.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/Biostrings_2.78.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/DAPAR_1.42.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/impute_1.84.0.tgz'
...
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/QFeatures_1.20.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/Spectra_1.20.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/vsn_3.78.0.tgz'
installing the source package ‘DAPARdata’

trying URL 'https://bioconductor.org/packages/3.22/data/experiment/src/contrib/DAPARdata_1.40.0.tar.gz'
Content type 'application/x-gzip' length 30447560 bytes (29.0 MB)
==================================================
downloaded 29.0 MB
```

--------------------------------

### Install Latest Mice Package from GitHub

Source: https://github.com/amices/mice/blob/master/README.md

Install the most recent version of the 'mice' package directly from its GitHub repository using the 'devtools' package.

```r
install.packages("devtools")
devtools::install_github(repo = "amices/mice")
```

--------------------------------

### Install mice Package

Source: https://github.com/amices/mice/blob/master/docs/index.html

Install the 'mice' package from CRAN or the latest version from GitHub using devtools.

```r
install.packages("mice")
```

```r
install.packages("devtools")
devtools::install_github(repo = "amices/mice")
```

--------------------------------

### Install mice Package from GitHub

Source: https://github.com/amices/mice/blob/master/docs/articles/overview.html

Install the latest development version of the mice package directly from its GitHub repository. Requires the devtools package.

```r
install.packages("devtools")
devtools::install_github("amices/mice")
```

--------------------------------

### Name formulas using dot notation

Source: https://github.com/amices/mice/blob/master/docs/reference/name.formulas.html

Shows how to use name.formulas with dot notation for specifying models, which is equivalent to the previous example but more concise.

```R
form2 <- list(bmi ~ ., hyp ~ ., chl ~ .)
form2 <- name.formulas(form2)
imp2 <- mice(nhanes, formulas = form2, print = FALSE, m = 1, seed = 12199)
identical(complete(imp1), complete(imp2))
```

--------------------------------

### Devel Package Installation Log

Source: https://github.com/amices/mice/blob/master/revdep/failures.md

This log details the attempt to install development versions of R packages. It shows the URLs being queried for packages like 'broman' and 'synthpop'.

```text
trying URL 'https://cloud.r-project.org/bin/macosx/big-sur-arm64/contrib/4.5/broman_0.92.tgz'
trying URL 'https://cloud.r-project.org/bin/macosx/big-sur-arm64/contrib/4.5/synthpop_1.9-2.tgz'
```

--------------------------------

### Conventional mice workflow example

Source: https://github.com/amices/mice/blob/master/docs/reference/pool.table.html

Demonstrates a typical workflow using the mice package: performing imputation, fitting a model to the imputed data, pooling the results, and accessing the pooled estimates.

```r
imp <- mice(nhanes2, m = 2, maxit = 2, seed = 1, print = FALSE)
fit <- with(imp, lm(chl ~ age + bmi + hyp))
pld1 <- pool(fit)
pld1$pooled
```

--------------------------------

### CRAN Package Installation Log

Source: https://github.com/amices/mice/blob/master/revdep/failures.md

This log shows the process of downloading and installing R packages from CRAN and Bioconductor. It indicates when binary versions are available but source versions are later, and lists the URLs being tried for package downloads.

```text
There are binary versions available but the source versions are later:
            binary source needs_compilation
MsCoreUtils 1.21.0 1.22.1              TRUE
S4Arrays    1.10.0 1.10.1              TRUE
SparseArray 1.10.2 1.10.6              TRUE

  Binaries will be installed

The downloaded binary packages are in
	/var/folders/5_/g85d42yj50b6lrjq4rzjzg8w0000gn/T//Rtmp8AWm9i/downloaded_packages


trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/affy_1.88.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/affyio_1.80.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/AnnotationDbi_1.72.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/AnnotationFilter_1.34.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/BiocBaseUtils_1.12.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/BiocFileCache_3.0.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/biomaRt_2.66.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/Biostrings_2.78.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/DAPAR_1.42.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/impute_1.84.0.tgz'
...
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/QFeatures_1.20.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/Spectra_1.20.0.tgz'
trying URL 'https://bioconductor.org/packages/3.22/bioc/bin/macosx/big-sur-arm64/contrib/4.5/vsn_3.78.0.tgz'
installing the source package ‘DAPARdata’

trying URL 'https://bioconductor.org/packages/3.22/data/experiment/src/contrib/DAPARdata_1.40.0.tar.gz'
Content type 'application/x-gzip' length 30447560 bytes (29.0 MB)
==================================================
downloaded 29.0 MB
```

--------------------------------

### version

Source: https://github.com/amices/mice/blob/master/docs/reference/version.html

Echoes the package version number. It takes an optional package name and returns a character vector with the package name, version number, and installed directory.

```APIDOC
## version

### Description
Echoes the package version number.

### Usage

```R
version(pkg = "mice")
```

### Arguments

*   **pkg** (character vector) - The package name.

### Value

A character vector containing the package name, version number and installed directory.

### Examples

```R
version()
#> [1] "mice 3.19.9 2026-06-01 /private/var/folders/7c/sgz0tntx09542ldfdgl73bbc0000gn/T/RtmpyYz78z/temp_libpath10d6b7aa3b8a5"
version("base")
#> [1] "base 4.5.3  /Library/Frameworks/R.framework/Resources/library"
```
```

--------------------------------

### Example of using jomoImpute within mice

Source: https://github.com/amices/mice/blob/master/docs/reference/mice.impute.jomoImpute.html

This example demonstrates how to use jomoImpute for imputation within the mice function. It shows setting up imputation blocks, specifying imputation methods, and customizing prediction matrices. Requires mitml version 0.3-5.7.

```R
if (FALSE) { # \dontrun{
# Note: Requires mitml 0.3-5.7
blocks <- list(c("bmi", "chl", "hyp"), "age")
method <- c("jomoImpute", "pmm")
ini <- mice(nhanes, blocks = blocks, method = method, maxit = 0)
pred <- ini$pred
pred["B1", "hyp"] <- -2
imp <- mice(nhanes, blocks = blocks, method = method, pred = pred, maxit = 1)
} # }
```

--------------------------------

### Predict MI Example

Source: https://github.com/amices/mice/blob/master/docs/reference/predict_mi.html

This example demonstrates how to use the predict_mi function to pool predictions from multiply imputed datasets. It involves creating an imputation object, splitting data into training and testing sets, fitting linear models on the imputed training data, and then pooling predictions on the test data.

```R
# Dataframe with missings in X
# Create Imp and Lm object
dat <- mice::nhanes

# add indicator training and test
# first 20 for training, last 5 for testing
dat$set <- c(rep("train", 20), rep("test", 5))

# Make prediction matrix and ensure that set is not used as a predictor
predmat <- mice::make.predictorMatrix(dat)
predmat[, "set"] <- 0

# Impute missing values based on the train set
imp <- mice(dat, m = 5, maxit = 5 , seed = 1, predictorMatrix = predmat,
  ignore = ifelse(dat$set == "test", TRUE, FALSE), print = FALSE)
impdats <- complete(imp, "all")

# extract the training and test data sets
traindats <- lapply(impdats, function(dat) subset(dat, set == "train", select = -set))
testdats <- lapply(impdats, function(dat) subset(dat, set == "test", select = -c(set)))

# Fit the prediction models, based on the imputed training data
fits <- lapply(traindats, function(dat) lm(age ~ bmi + hyp + chl, data = dat))

# pool the predictions with function
pool_preds <- mice::predict_mi(object = fits, newdata = testdats,
  pool = TRUE, interval = "prediction", level = 0.95)

```

--------------------------------

### Generate synthetic values for mpg

Source: https://github.com/amices/mice/blob/master/docs/reference/matchindex.html

This example demonstrates how to use `matchindex` to borrow values from a training set to create synthetic values for a test set, simulating observed outcomes.

```R
train <- mtcars[1:20, ]
test <- mtcars[21:32, ]
fit <- lm(mpg ~ disp + cyl, data = train)
d <- fitted.values(fit)
t <- predict(fit, newdata = test)  # note: not using mpg
idx <- matchindex(d, t)

# Borrow values from train to produce 12 synthetic values for mpg in test.
# Synthetic values are plausible values that could have been observed if
# they had been measured.
train$mpg[idx]
#>  [1] 22.8 16.4 15.2 14.3 14.7 30.4 22.8 22.8 14.3 21.0 17.3 24.4

```

--------------------------------

### Imputation using predictorMatrix

Source: https://github.com/amices/mice/blob/master/docs/reference/name.formulas.html

This example shows a standard imputation process using the predictorMatrix, serving as a baseline for comparison with formula-based imputation.

```R
imp4 <- mice(nhanes, print = FALSE, m = 1, seed = 12199, auxiliary = TRUE)
identical(complete(imp1), complete(imp4))
```

--------------------------------

### Interactive Program Notice

Source: https://github.com/amices/mice/blob/master/docs/LICENSE.html

Display this short notice when an interactive program starts to inform users about its version, copyright, warranty, and redistribution conditions.

```text
Gnomovision version 69, Copyright (C) year name of author
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
```

--------------------------------

### Get Mice Package Version

Source: https://github.com/amices/mice/blob/master/cran-comments.md

Use this R code to check the currently installed version of the mice package.

```r
packageVersion("mice")
```

--------------------------------

### Get version for another package

Source: https://github.com/amices/mice/blob/master/docs/reference/version.html

Specify the package name as a character string argument to the version() function to retrieve version information for other installed R packages, such as 'base'.

```r
version("base")
#> [1] "base 4.5.3  /Library/Frameworks/R.framework/Resources/library"
```

--------------------------------

### make.post()

Source: https://github.com/amices/mice/blob/master/docs/llms.txt

Creates a `post` argument.

```APIDOC
## make.post()

### Description
Creates a `post` argument.

### Method
N/A (Function Call)

### Parameters
N/A (Function Call)

### Request Example
N/A (Function Call)

### Response
N/A (Function Call)
```

--------------------------------

### Get all six colors using mdc

Source: https://github.com/amices/mice/blob/master/docs/reference/mdc.html

This example demonstrates how to retrieve all six default colors for observed, missing, and combined data using the mdc function with numerical arguments.

```r
mdc(1:6)
```

--------------------------------

### Imputation Model on Train Data

Source: https://github.com/amices/mice/blob/master/docs/reference/mice.html

Fits an imputation model on training data and applies it to impute test data. This example is within a conditional block and requires specific setup.

```R
if (FALSE) { # \dontrun{
# example where we fit the imputation model on the train data
# and apply the model to impute the test data
set.seed(123)
ignore <- sample(c(TRUE, FALSE), size = 25, replace = TRUE, prob = c(0.3, 0.7))

# scenario 1: train and test in the same dataset
imp <- mice(nhanes2, m = 2, ignore = ignore, print = FALSE, seed = 22112)
imp.test1 <- filter(imp, ignore)
imp.test1$data
complete(imp.test1, 1)
complete(imp.test1, 2)

# scenario 2: train and test in separate datasets
traindata <- nhanes2[!ignore, ]
testdata <- nhanes2[ignore, ]
imp.train <- mice(traindata, m = 2, print = FALSE, seed = 22112)
imp.test2 <- mice.mids(imp.train, newdata = testdata)
complete(imp.test2, 1)
complete(imp.test2, 2)
} # } 
```

--------------------------------

### Get line colors for observed and missing data using mdc

Source: https://github.com/amices/mice/blob/master/docs/reference/mdc.html

This example shows how to specify line colors for observed and missing data by using string abbreviations for 'observed' and 'missing' and setting the 's' argument to 'lin' for lines.

```r
mdc(c("obs", "mis"), "lin")
```

--------------------------------

### make.visitSequence()

Source: https://github.com/amices/mice/blob/master/docs/llms.txt

Creates a `visitSequence` argument.

```APIDOC
## make.visitSequence()

### Description
Creates a `visitSequence` argument.

### Method
N/A (Function Call)

### Parameters
N/A (Function Call)

### Request Example
N/A (Function Call)

### Response
N/A (Function Call)
```

--------------------------------

### R Vignette Execution Error with 'rjags' Library

Source: https://github.com/amices/mice/blob/master/revdep/problems.md

This error occurs when running R code from vignettes, specifically failing to load the 'rjags' library due to an incompatible architecture or missing dependency ('libjags.4.dylib'). This indicates a problem with the installation or environment setup for 'rjags' on the system.

```R
  ‘IPD-meta-analysis-with-missing-data.Rmd’ using ‘UTF-8’... failed
  ‘IPD-meta-analysis.Rmd’ using ‘UTF-8’... failed
  ‘Imputing-missing-values-in-IPD.Rmd’ using ‘UTF-8’... OK
 Error
Errors in running code in vignettes:
when running code in ‘IPD-meta-analysis-with-missing-data.Rmd’
  ... 
+     X <- with(current.data, apply(current.data[, c(" ..." ... [TRUNCATED] 

  When sourcing ‘IPD-meta-analysis-with-missing-data.R’:
...
+     n.iter = 5000)

  When sourcing ‘IPD-meta-analysis.R’:
Error: .onLoad failed in loadNamespace() for 'rjags', details:
  call: dyn.load(file, DLLpath = DLLpath, ...)
  error: unable to load shared object '/Users/buurensv/Dropbox/Package/mice/mice/revdep/library.noindex/bipd/rjags/libs/rjags.so':
  dlopen(/Users/buurensv/Dropbox/Package/mice/mice/revdep/library.noindex/bipd/rjags/libs/rjags.so, 0x000A): Library not loaded: /usr/local/lib/libjags.4.dylib
  Referenced from: <337070A2-BC15-3117-B643-96612554E437> /Users/buurensv/Dropbox/Package/mice/mice/revdep/library.noindex/bipd/rjags/libs/rjags.so
  Reason: tried: '/usr/local/lib/libjags.4.dylib' (mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64e' or 'arm64e.v1' or 'arm64' or 'arm64')), '/System/Volumes/Preboot/Cryptexes/OS/usr/local/lib/libjags.4.dylib' (no such file), '/usr/local/lib/libjags.4.dylib' (mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64e' or 'arm64e.v1' or 'arm64' or 'arm64')), '/Library/Frameworks/R.framework/Resources/
Execution halted
```

--------------------------------

### Dynr Installation Failure: Missing GSL

Source: https://github.com/amices/mice/blob/master/revdep/failures.md

This error indicates that the GSL (GNU Scientific Library) is not installed, which is required for the 'dynr' package installation. Ensure GSL is installed and accessible in your system's PATH.

```bash
checking for gsl-config... no
configure: error: gsl-config not found, is GSL installed?
ERROR: configuration failed for package ‘dynr’
* removing ‘/Users/buurensv/Dropbox/Package/mice/mice/revdep/checks.noindex/dynr/new/dynr.Rcheck/dynr’
```

```bash
checking for gsl-config... no
configure: error: gsl-config not found, is GSL installed?
ERROR: configuration failed for package ‘dynr’
* removing ‘/Users/buurensv/Dropbox/Package/mice/mice/revdep/checks.noindex/dynr/old/dynr.Rcheck/dynr’
```

--------------------------------

### Construct blocks from formulas and predictor matrix

Source: https://github.com/amices/mice/blob/master/docs/reference/construct.blocks.html

Demonstrates how to use `construct.blocks` with both `formulas` and `predictorMatrix` arguments. The output shows the identified blocks based on the provided specifications.

```R
form <- list(bmi + hyp ~ chl + age, chl ~ bmi)
pred <- make.predictorMatrix(nhanes[, c("age", "chl")])
construct.blocks(formulas = form, pred = pred)
```

--------------------------------

### make.method()

Source: https://github.com/amices/mice/blob/master/docs/llms.txt

Creates a `method` argument.

```APIDOC
## make.method()

### Description
Creates a `method` argument.

### Method
N/A (Function Call)

### Parameters
N/A (Function Call)

### Request Example
N/A (Function Call)

### Response
N/A (Function Call)
```

--------------------------------

### rmsb Source Package Installation Error

Source: https://github.com/amices/mice/blob/master/revdep/failures.md

This log shows the error encountered during the installation of the 'rmsb' source package. The installation halts due to a missing dependency, 'rstantools', which is required for loading the namespace.

```text
installing *source* package ‘rmsb’ ...
** this is package ‘rmsb’ version ‘1.1-2’
** package ‘rmsb’ successfully unpacked and MD5 sums checked
** using staged installation
Error in loadNamespace(x) : there is no package called ‘rstantools’
Calls: loadNamespace -> withRestarts -> withOneRestart -> doWithOneRestart
Execution halted
ERROR: configuration failed for package ‘rmsb’
* removing ‘/Users/buurensv/Dropbox/Package/mice/mice/revdep/checks.noindex/rmsb/new/rmsb.Rcheck/rmsb’
```

--------------------------------

### rmsb CRAN Source Package Installation Error

Source: https://github.com/amices/mice/blob/master/revdep/failures.md

This log details the error during the installation of the 'rmsb' CRAN source package. Similar to the development version, the installation fails because the 'rstantools' package is not found, preventing the namespace from loading.

```text
installing *source* package ‘rmsb’ ...
** this is package ‘rmsb’ version ‘1.1-2’
** package ‘rmsb’ successfully unpacked and MD5 sums checked
** using staged installation
Error in loadNamespace(x) : there is no package called ‘rstantools’
Calls: loadNamespace -> withRestarts -> withOneRestart -> doWithOneRestart
Execution halted
ERROR: configuration failed for package ‘rmsb’
* removing ‘/Users/buurensv/Dropbox/Package/mice/mice/revdep/checks.noindex/rmsb/old/rmsb.Rcheck/rmsb’
```

--------------------------------

### Using quickpred within mice for Imputation

Source: https://github.com/amices/mice/blob/master/docs/reference/quickpred.html

Demonstrates how to use the output of quickpred directly as the 'pred' argument in the mice function for performing multiple imputation. This example also filters predictors based on minimum usable cases and includes 'age'.

```R
imp <- mice(nhanes, pred = quickpred(nhanes, minpuc = 0.25, include = "age"))
```

--------------------------------

### Install mice Package from CRAN

Source: https://github.com/amices/mice/blob/master/docs/articles/overview.html

Use this command to install the mice package from the Comprehensive R Archive Network (CRAN).

```r
install.packages("mice")
```

--------------------------------

### Get mice package version

Source: https://github.com/amices/mice/blob/master/docs/reference/version.html

Call the version() function without arguments to get the version information for the mice package.

```r
version()
#> [1] "mice 3.19.9 2026-06-01 /private/var/folders/7c/sgz0tntx09542ldfdgl73bbc0000gn/T/RtmpyYz78z/temp_libpath10d6b7aa3b8a5"
```

--------------------------------

### Compare pooled table outputs

Source: https://github.com/amices/mice/blob/master/docs/reference/pool.table.html

This example checks if the output from summary(pld2, type = "all") is identical to the output directly from pool.table(tbl). It confirms that both methods yield the same results when generating all numerical outputs.

```R
class(all1) <- "data.frame"
identical(all1, all2)
```

--------------------------------

### Generate Synthetic Data, Analyze, and Pool

Source: https://github.com/amices/mice/blob/master/docs/reference/pool.html

This example demonstrates generating fully synthetic data using `mice` with a specified `where` matrix. The synthetic data is then analyzed with a linear model and the results are pooled using `pool.syn`.

```r
imp <- mice(cars,
  maxit = 2, m = 2,
  where = matrix(TRUE, nrow(cars), ncol(cars))
)
fit <- with(data = imp, exp = lm(speed ~ dist))
summary(pool.syn(fit))
```

--------------------------------

### Random Forest Imputation Example

Source: https://github.com/amices/mice/blob/master/docs/reference/mice.impute.rf.html

Demonstrates how to perform random forest imputation using the mice function with the 'rf' method. It also shows how to plot the imputation results. This example is intended to be run in an R environment.

```r
if (FALSE) {
imp <- mice(nhanes2, meth = "rf", ntree = 3)
plot(imp)
}
```

--------------------------------

### Initialize Imputation Model

Source: https://github.com/amices/mice/blob/master/docs/reference/mice.impute.2lonly.pmm.html

Sets up the initial imputation object to extract predictor matrices and imputation methods. This is a preparatory step before defining the multilevel imputation strategy.

```R
# empty mice imputation
imp0 <- mice(as.matrix(dfr), maxit = 0)
predM <- imp0$predictorMatrix
impM <- imp0$method
```

--------------------------------

### Create Sample Data

Source: https://github.com/amices/mice/blob/master/docs/reference/mice.impute.quadratic.html

Generates a sample dataset with quadratic relationships and missing values for imputation demonstration.

```R
B1 <- .5
B2 <- .5
X <- rnorm(1000)
XX <- X^2
e <- rnorm(1000, 0, 1)
Y <- B1 * X + B2 * XX + e
dat <- data.frame(x = X, xx = XX, y = Y)

# Impose 25 percent MCAR Missingness
dat[0 == rbinom(1000, 1, 1 - .25), 1:2] <- NA
```

--------------------------------

### Standard Copyright Notice and License Disclaimer

Source: https://github.com/amices/mice/blob/master/docs/LICENSE.html

Include this block at the beginning of each source file to state copyright, redistribute terms, and disclaim warranty. Ensure a copy of the GNU GPL is available.

```text
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year>  <name of author>

This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
```

--------------------------------

### rmsb Installation Failure (In Both Devel and CRAN)

Source: https://github.com/amices/mice/blob/master/revdep/failures.md

This error message indicates that the 'rmsb' package failed to install. The failure occurred in both development and CRAN versions, suggesting a common underlying issue. Details can be found in the specified R check output file.

```text
checking whether package ‘rmsb’ can be installed ... ERROR
Installation failed.
See ‘/Users/buurensv/Dropbox/Package/mice/mice/revdep/checks.noindex/rmsb/new/rmsb.Rcheck/00install.out’ for details.
```

--------------------------------

### Generate all numerical output from a pooled table

Source: https://github.com/amices/mice/blob/master/docs/reference/pool.table.html

This example demonstrates how to obtain all numerical outputs, including confidence intervals, from a pooled table using the summary function with type = "all". This provides a comprehensive set of statistics for analysis.

```R
all1 <- summary(pld2, type = "all", conf.int = TRUE)
all1
```

--------------------------------

### make.blocks()

Source: https://github.com/amices/mice/blob/master/docs/llms.txt

Creates a `blocks` argument.

```APIDOC
## make.blocks()

### Description
Creates a `blocks` argument.

### Method
N/A (Function Call)

### Parameters
N/A (Function Call)

### Request Example
N/A (Function Call)

### Response
N/A (Function Call)
```

--------------------------------

### Get R Version

Source: https://github.com/amices/mice/blob/master/cran-comments.md

Use this R code to retrieve the version string of the R environment.

```r
R.Version()$version.string
```

--------------------------------

### Run revdepcheck details for a package

Source: https://github.com/amices/mice/blob/master/revdep/failures.md

Use this command to get more detailed information about reverse dependency checks for a specific package.

```R
revdepcheck::revdep_details(, "brms")
```

--------------------------------

### Perform Multiple Imputations in Parallel with futuremice

Source: https://github.com/amices/mice/blob/master/docs/reference/futuremice.html

Demonstrates how to perform multiple imputations using futuremice. It shows basic usage with default settings and an example specifying imputation methods. Requires the mice package for imputation and future package for parallelization.

```R
# 150 imputations in dataset nhanes, performed by 3 cores
if (FALSE) { # \dontrun{
imp1 <- futuremice(data = nhanes, m = 150, n.core = 3)
# Making use of arguments in mice.
imp2 <- futuremice(data = nhanes, m = 100, method = "norm.nob")
imp2$method
fit <- with(imp2, lm(bmi ~ hyp))
pool(fit)
} # }
```