Hi,

I am relatively new to R coding (programming in general) and Rcpp, so excuse me if I sound naive or uninformed.

I am working on Markov switching GARCH models in R using the MSGARCH package. I want to iterate the model over different specifications of variances and distributions. In fact, I have written an R code to that effect that I shall attach in the description. I wanted to implement something similar using Rcpp package but was unable to do so.

My question is that whether I can implement R functions from packages such as MSGARCH in a .cpp file and import that code using sourceCpp() inside my main R file. Also, is it possible to use R code chunks inside of nested for-loops written in C++? If not is there any other way to implement something like this in Rcpp?

The R code is as follows:

```
require(MSGARCH)
variances <- c("sGARCH", "gjrGARCH") # list containing model types
distributions <- c("norm", "std", "sstd") # list containing distributions
states <- c(1, 2) # list containing number of states
## pre-define an empty dataframe to append model names and BIC values to
Model <- BIC <- rep(0, times = 12) # empty vectors of length 12 for model and BIC
comparison.df <- data.frame(Model, BIC) # empty data frame of shape 2x12
# iterate model over different specifications
for (distribution in distributions) { # iterate over three distributions
for (variance in variances) { # iterate over two models
for (nstate in states) { # iterate over two states
model.name <- paste("model",
variance, distribution, nstate,
sep = "."
) # variable name for model spec assignment
model.fit.name <- paste(model.name,
"fit",
sep = "."
) # variable name for model fit assignment
model.spec <- CreateSpec(
variance.spec = list(model = rep(variance, times = nstate)), # model spec
distribution.spec = list(distribution = rep(distribution, times = nstate))
)
model.fit <- FitML(spec = model.spec, data = residuals.series)
assign(model.name, value = model.spec)
assign(model.fit.name, value = model.fit)
}
}
}
```

#### System Information:

- RStudio Edition: Desktop
- RStudio Version: 1.2.5042
- OS Version: macOS Catalina 10.15.4
- R Version: 3.6.3

Thanks,

Adi