Nothing

```
#' Model selection for Gaussian mixture models
#'
#' Calculate the AIC, corrected AIC, or BIC for a curve fit with a Gaussian
#' mixture model by nonlinear least squares optimization. This function
#' permits the calculation of the AIC/AICc/BIC after rejecting some Gaussians
#' in the model, for example because their centres are outside the bounds of
#' the profile.
#'
#' @param coefs the coefficients of the Gaussian mixture model, output by
#' \code{\link{fit_gaussians}}
#' @param chromatogram the raw elution profile
#'
#' @return the AIC, corrected AIC, or BIC of the fit model
#'
#' @name aic
NULL
#' @rdname aic
gaussian_aic <- function(coefs, chromatogram) {
# first, calculate log likelihood
N <- length(chromatogram)
indices <- seq_len(N)
fit <- fit_curve(coefs, indices)
res <- chromatogram - fit
w <- rep_len(1, N)
zw <- w == 0
loglik <- -N * (log(2 * pi) + 1 - log(N) - sum(log(w + zw)) +
log(sum(w * res^2)))/2
# next, calculate parameters
k <- length(unlist(coefs)) + 1
AIC <- 2 * k - 2 * loglik
return(AIC)
}
#' @rdname aic
gaussian_aicc <- function(coefs, chromatogram) {
# first, calculate AIC
AIC <- gaussian_aic(coefs, chromatogram)
# second, calculate AICc
N <- length(chromatogram)
k <- length(unlist(coefs)) + 1
AICc <- AIC + (2 * k * (k + 1)) / (N - k - 1)
return(AICc)
}
#' @rdname aic
gaussian_bic <- function(coefs, chromatogram) {
# first, calculate log likelihood
N <- length(chromatogram)
indices <- seq_len(N)
fit <- fit_curve(coefs, indices)
res <- chromatogram - fit
w <- rep_len(1, N)
zw <- w == 0
loglik <- -N * (log(2 * pi) + 1 - log(N) - sum(log(w + zw)) +
log(sum(w * res^2)))/2
# next, calculate parameters
k <- length(unlist(coefs)) + 1
BIC <- log(N) * k - 2 * loglik
return(BIC)
}
```

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