R code for peak fitting

Here is an example R code for peak fitting using the "peaks" package:

library(peaks)

# create example data
x <- seq(0,10,length.out=100)
y <- dnorm(x,5,1) + rnorm(100,0,0.1)

# plot the data
plot(x,y)

# define the model
model <- "Gaussian(x, A, mu, sigma) + Gaussian(x, B, mu, sigma)"

# set initial parameter values
init <- list(A=max(y), B=0.5*max(y), mu=mean(x), sigma=1)

# fit the model to the data
fit <- peakfit(x, y, model=model, init=init)

# plot the fit
lines(x, fit$fit, col="red", lwd=2)

# print the parameter estimates
print(fit$params)

In this example, we create a data set with a single Gaussian peak, add some noise, and then fit a model consisting of two Gaussian peaks to the data using the "peakfit" function from the "peaks" package. The initial parameter values are set based on some heuristics (e.g., the maximum value of the data for the amplitude parameter), and the "fit" object contains the fitted parameters as well as other information such as the residuals and the goodness-of-fit statistics. The fitted curve is then plotted on top of the original data, and the parameter estimates are printed to the console. Note that the "peaks" package supports a variety of different peak models, including Lorentzian, Voigt, and pseudo-Voigt peaks, as well as multiple peaks with different shapes and widths