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You have missing data and want to estimate a regression model? Try
`hmlasso`

package! This package provides a simple implementation of
HMLasso (Lasso with High Missing rate).

You can install the released version of hmlasso from CRAN with:

```
install.packages("hmlasso")
```

This is a basic example which shows you how to solve a common problem:

```
library(hmlasso)
```

A typical usage of `hmlasso`

is as follows:

```
head(iris)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
X_incompl <- as.matrix(iris[, 1:3])
X_incompl[1:5,1] <- NA
X_incompl[6:10,2] <- NA
y <- iris[, 4]
cv_fit <- cv.hmlasso(X_incompl, y, nlambda=50, lambda.min.ratio=1e-2)
plot(cv_fit)
```

```
plot(cv_fit$fit)
```

Takada, M., Fujisawa, H., & Nishikawa, T. (2019). “HMLasso: Lasso with High Missing Rate.” IJCAI. \<arXiv:1811.00255>.

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