weibull | Weibull analysis , test design | Code Analyzer library

I am trying to plot the prediction error curve from pec package but I can't change the legend position and size. There's an example from pec package:

I have an std::vector or random numbers [0, 1]. (How) Can I use the standard library to convert it to a specific (e.g. Weibull) distribution (using the distribution's cumulative distribution)?

To be clear: I don't have what the standard library considers a "generator" (I don't have a class whose operator() returns an integer). I already have a list of random doubles [0, 1] and want to just use the standard library's implementation of the cumulative density function of the different distributions (e.g. Weibull).

I´m trying to estimate the parameters of a 3-parameter weibull distribution (translation parameter beta= -0.5). The problem is that I have to fit two sets of data simultaneously. Using nlc (see code below) i was able to estimate the parameters of the distribution for each set of data individually, but not simultaneously. GAMMA is something like a shared parameter (the estimated GAMMA has to be the same in both nlc estimations).

My data looks like this:

I wanted to create a weibull probability plot using Bokeh. Based on the reference (linked below),

https://www.itl.nist.gov/div898/handbook/eda/section3/weibplot.htm

The y-axis of a weibull probability plot has an axes with scale: ln(-ln(1-p)). Let's say that I have defined a function (with it's inverse function),

I have been trying to estimate the two-parameter Weibull distribution with a Newton method. As I was reading a bit about using Newton-Raphson algorithm, I found it challenging to understand some aspects.

I've tried to implement it in Python and tbh I see no wrong in my approach. But since I was struggling to understand the algorithm itself, I assume I am missing something. My code runs, the problem is that it doesn't find the correct estimates (1.9 and 13.6):

latex() function not giving rendered output between the code chunks with R "rms" package. How can I solve this?

CODE

Assuming I have a series of hourly measured values, such as the mean wind speed. A start and end date is used to limit the data in terms of time. From these data I can calculate the frequency of the values for individual categories. The first category includes all values between 0 and < 0.5 km/h. The second all values between 0.5 and < 1.5 km/h, the third all values between 1.5 and < 2.5 km/h and so on. Counting all values results in the following total distribution:

I am plotting curves for different distribution functions and I need to know the highest y-value for each curve. Later I will plot only the one curve, which is selected as the best fitting.

This is the function (it is a bit hard-coded, I am working on it):

I'm trying to replicate R's fitdist() results (reference, cannot modify R code) in Python using scipy.stats. The results are quite close but still different (difference is at not acceptable level). Does anybody know why the results are different? How can I reduce the difference between the results?

scipy_stats.weibull_min definition (https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.weibull_min.html) seems to be the same as R's weibull (https://stat.ethz.ch/R-manual/R-devel/library/stats/html/Weibull.html.

Data example: