# misaem package

### Introduction

`misaem`

is a package to perform linear regression and
logistic regression with missing data, under MCAR (Missing completely at
random) and MAR (Missing at random) mechanisms. The covariates are
assumed to be continuous variables. The methodology implemented is based
on maximization of the observed likelihood using EM-types of algorithms.
The package includes:

- Parameters estimation.
- Estimation of standard deviation for estimated parameters.
- Model selection procedure based on BIC.

### Installation of package

Now you can install the package **misaem** from
CRAN.

`install.packages("misaem")`

### Using the misaem package

Basically,

`miss.glm`

is the main function performing logistic
regression with missing values.
`miss.lm`

is the main function performing linear
regression with missing values.

For more details, You can find the vignette, which illustrate the
basic and further usage of misaem package:

```
library(misaem)
vignette('misaem')
```

## Reference

Logistic Regression with Missing Covariates – Parameter Estimation,
Model Selection and Prediction (2020, Jiang W., Josse J., Lavielle M.,
TraumaBase Group), Computational
Statistics & Data Analysis.