measure "inbreeding" for multiple recoding scenarii (dragoon/komodo!108)

this MR is two-fold
- refactor `run.nu` and `plot.nu` from `scripts/inbreeding/` into Nushell modules with `--options` as argument instead of `options.nu` (a7cebb95, 6b72191f and 5f1c4963)
- introduce another level of depth to the measurements (a0e52e95)

>  **Note**  
> in the table below
> - $s$ is the number of recoding scenarii averages together
> - $m$ is the number of measurements per point
> - two iterations of the same experiment are shown side by side for comparison

s   |    m | . | .
:--:|:----:|:-------------------------:|:-------------------------:
1   | 10   | ![inbreeding_1_10.1](/uploads/c593393edb3513c9d77b0fe134c27fd7/inbreeding_1_10.1.png) | ![inbreeding_1_10.2](/uploads/97c85b36833112de51a2b756ade53479/inbreeding_1_10.2.png)
1   | 100  | ![inbreeding_1_100.1](/uploads/af4da1d7cf76ef43fb39c2a3a529b7cd/inbreeding_1_100.1.png) | ![inbreeding_1_100.2](/uploads/e187298709d524437dea503be6ac555f/inbreeding_1_100.2.png)
1   | 1000 | ![inbreeding_1_1000.1](/uploads/394821777baeff9fec589440ba4c554c/inbreeding_1_1000.1.png) | ![inbreeding_1_1000.2](/uploads/1d592b791075f204f8a0ebdd739403dd/inbreeding_1_1000.2.png)
10  | 100  | ![inbreeding_10_100.1](/uploads/3c822e7669e9f0b4d97919e5a3bd4bca/inbreeding_10_100.1.png) | ![inbreeding_10_100.2](/uploads/aa6b54ec64f82ca386dae4e262dcd0b6/inbreeding_10_100.2.png)
100 | 10   | ![inbreeding_100_10.1](/uploads/7ef383143d981717b0ad01dee0359eb0/inbreeding_100_10.1.png) | ![inbreeding_100_10.2](/uploads/ce71a32a7f6e9ba3c4dae563aeefd856/inbreeding_100_10.2.png)
100 | 100  | ![inbreeding_100_100.1](/uploads/e2038273051f959d8be69fef9ba7a493/inbreeding_100_100.1.png) | ![inbreeding_100_100.2](/uploads/0ef30735597e1a6812484d1cac4d34ca/inbreeding_100_100.2.png)

we can see that
- the smaller the $s$, the more different the two figures are on each line -> this is likely due to the fact that, if only one recoding scenario is used, then repeating the same experiment will result in very different results and measurements. Running the same experiment $s$ times and averaging helps reducing the variance along this axis
- the smaller the $m$, the more noisy the measures of each points -> this is simply because, when $m$ is small, the variance of the empirical means measured for each point is higher

## final results
![inbreeding](/uploads/e561f4e4acad8eedbb3ccf1a4666c302/inbreeding.png)
![inbreeding_100_100_1](/uploads/37ab0eacb5159137595579dfbb20250c/inbreeding_100_100_1.png)