The goal of `cvwrapr`

is to make cross-validation (CV)
easy. The main function in the package is `kfoldcv`

. It
performs K-fold CV for a hyperparameter, returning the CV error for a
path of hyperparameter values along with other useful information. The
`computeError`

function allows the user to compute the CV
error for a range of loss functions from a matrix of out-of-fold
predictions. See the package vignettes for more examples.

You can install the development version from GitHub with:

```
# install.packages("devtools")
::install_github("kjytay/cvwrapr") devtools
```

This is a basic example showing how to perform cross-validation for
the `lambda`

parameter in the lasso (Tibshirani 1996).

```
# simulate data
set.seed(1)
<- 100; nvars <- 10
nobs <- matrix(rnorm(nobs * nvars), nrow = nobs)
x <- rowSums(x[, 1:2]) + rnorm(nobs)
y
library(cvwrapr)
library(glmnet)
set.seed(1)
<- kfoldcv(x, y, train_fun = glmnet, predict_fun = predict) cv_fit
```

The returned output contains information on the CV procedure and can be plotted.

```
names(cv_fit)
#> [1] "lambda" "cvm" "cvsd" "cvup" "cvlo"
#> [6] "lambda.min" "lambda.1se" "index" "name" "overallfit"
plot(cv_fit)
```