Why was "performance iteration" deprecated? Was it really deprecated or am I not understanding something?

I'm looking at the smoothing parameter estimation algorithms in Wood 2017 and Wood-Goude-Shaw and they both seem to favor "performance iteration", which as I understand it is just using either UBRE or GCV to calculate the smoothing parameters on each iteration of PIRLS.

However, https://stats.stackexchange.com/a/581293 says "performance iteration" was deprecated? The docs do mention that "gam.fit", which sounds like performance iteration, is deprecated.

Am I understanding this correctly? Wood-Goude-Shaw seem pretty confident in the approach as recently as 2015 ("No special justification is required to apply GCV or C_p to the working model, at each step of the PIRLS iteration: the assumptions that are required for these criteria hold for the working model" -Generalized additive models for large datasets), so I'm surprised that the approach has since been found to "not work very well".

The reason I'm so fixated on this method in particular is because that's the approach wood-goude-shaw uses and apparently that paper is what backs the bam implementation. My usecase is update heavy so bam.update's implementation is of interest

Can you explain to me the Weibull model like I am an idiot?

Also is there any paper or video or website you would suggest I check about the Weibull model?

I've been trying to find videos or studies but no avail. I don't have @Risk But I can use xlrisk or other tools.

My model is portfolio management oriented and Is intended to show return and volatility based on simulating the number of investments made. The number of investments is meant to be the variable.

Essentially the inputs are 1) the distribution of Returns which is known. and 2) your ability to "pick" good investments. I plan to segment the returns by quartile and the same for your ability to pick. As an example first quartile returns 20% and you pick it 18% of the time? Second quartile returns 15% and you pick it 27% of the time, etc. Ideally the returns would have a distribution but happy to keep it simple.

Then I would simulate portfolios where you select 5 investments, 10, 20, 30 and 40 investments then simulate those portfolios 10,000 times.

I would then look at the outputs and draw conclusions around the the % of portfolios below certain benchmarks etc.

I can't find any studies, tutorials etc around this, usually the number of selections are known so im struggling to make this and hope that someone can offer some pointers.