# imputeR: A
General Multivariate Imputation Framework

imputeR is an R package that provides a general framework for missing
values imputation based on automated variable selection.

The main function `impute`

inputs a matrix containing
missing values and returns a complete data matrix using the variable
selection functions provided as part of the package, or written by the
user.

The package also offers many useful tools for imputation research
based on `impute`

. For example, the `Detect`

function can be used to detect the variables’ type in a given data
matrix. `guess`

can be used for naive imputation such as mean
imputation, median imputation, majority imputation (for categorical
variables only) and random imputation. `SimIm`

function
stands for “simulation for imputation”. It accepts a complete matrix and
randomly introduce some percentage of missing values into the matrix so
imputation methods can be employed subsequently to impute this
artificial missing data matrix. Because the true values are actually
know so imputation accuracy can be easily calculated. This calls for the
`SimEval`

function that extends `SimIm`

function,
simulates a number of missing data matrices, applies a imputation method
to these missing matrices and evaluate its performance. This enables the
uncertainty of the imputation method to be obtained.

### Reference

You can cite imputeR the following:

Feng L, Moritz S, Nowak G, Welsh AH, O’Neill TJ (2018). *imputeR:
A General Multivariate Imputation Framework*. R package version 2.1,
<URL: https://CRAN.R-project.org/package=imputeR>.

### Version

**2.1**

### License

GPL-3