CI based on delta method vs sampling method

This might be something that's worth explaining/mentioning in the manuscript.

I saw your CI code in expgrowthfit and implemented it in fitsir as well and noticed that delta method and sampling method give significantly different results for CI.

You can try running the following code to see the difference:
```R
harbin2 <- setNames(harbin, c("times", "count"))
ff <- fitsir(harbin2, type="death", method="BFGS")
plot(ff, level=0.95) ## delta
plot(ff, level=0.95, method="sample") ## sampling method
```
If you use lower level (e.g. `level=0.1`), sampled CI even misses the mean maximum likelihood trajectory.

If you want to see all samples, you can try running
```R
predict(ff, level=0.95, method="sample", debug=TRUE)
```

If you want to see the actual code (should be identical to your code if I didn't make any mistakes), it's inside `predict` function under fitsir-methods.R.

There's something about non-linearity...

Run the supplied R example with fitsir and compare plot(ff, level=0.95) against the sampling method, then use predict(ff, level=0.95, method="sample", debug=TRUE) to inspect the samples. Read the predict function in fitsir-methods.R and determine why the delta and sampling confidence intervals differ; done means the discrepancy is explained and its manuscript documentation need is clear.

Written by the indexing model from the issue text.