# Cross-Validation
for Linear & Ridge Regression Models (Rcpp, RcppParallel &
Eigen)

This package provides efficient implementations of cross-validation
techniques for linear and ridge regression models, leveraging C++ code
with Rcpp, RcppParallel, and Eigen libraries. It supports leave-one-out,
generalized, and K-fold cross-validation methods, utilizing Eigen
matrices for high performance.

## Dependencies

- Rcpp: Integration
between R and C++.
- RcppParallel:
Parallel computing support for Rcpp.
- RcppEigen:
Integration between R and Eigen C++ library.

### Requirements

### Acknowledgments

This code is adapted and extended from various sources, leveraging
the capabilities of the following:

- Rcpp by Dirk
Eddelbuettel, Romain Francois, et al., for R and C++ integration.
- RcppParallel
by Romain Francois, et al., for parallel computing support in Rcpp.
- RcppEigen by
Douglas Bates, Romain Francois, et al., for integration between R and
Eigen C++ library.

Please refer to the source files for detailed information and
licenses.

## Contributors

## License

This code is under MIT License.

## Example Usage

```
library(cvLM)
data(mtcars)
n <- nrow(mtcars)
# Formula method
cvLM(
mpg ~ .,
data = mtcars,
K.vals = n, # Leave-one-out CV
lambda = 10 # Shrinkage parameter of 10
)
# lm method
my.lm <- lm(mpg ~ ., data = mtcars)
cvLM(
my.lm,
data = mtcars,
K.vals = c(5L, 8L), # Perform both 5- and 8-fold CV
n.threads = 8L, # Allow up to 8 threads for computation
seed = 1234L
)
# glm method
my.glm <- glm(mpg ~ ., data = mtcars)
cvLM(
my.glm,
data = mtcars,
K.vals = n, generalized = TRUE # Use generalized CV
)
```