Test on 10 scenarios !

(it works!)

We don't include newborns (age class 1) into the population count.

models : change sensitivity of lambda approximation (claibration) to speed up the simulation (it really improved the speed !)

Added observe(param) in server to make sure param used in simulations keep up to date !

Check run issue with run analysis (works again...)

Espèce générique moved at end of list
This version work well.

Added choice of model_demo inside the run_simul function using the set of decisions rules based on :
* lambda (growth vs decline) for DD
* lambda and N0 (small vs large pop) for StochDemo

Updated other functions accrodingly

1- Change ref pop size min = 0 by min = 30
Thus modified
  N_ref[N_ref == 0] <- NaN
   by
  N_ref[N_ref < 30] <- NaN

2- added warning message (if too many iterations with N_ref < 30)

3- updated description

Updated plot_impact accordingly

Added imports (magrittr, popbio) to models M3 and M4 functions

Added piece "%>% sapply(min, 0.999)" in M3 and M4 to avoid survival parameter > 1.
Also added this in 'init_calib' function.

All models tested and work.

Made Model 3 (with DD) simulations faster by changing the calibrate_params function (based on optim) by an analytical approximation (using match_lam_delta function).

Tests reveals that the approx is largely good enough. Makes no differences in the results.

It works well but it's slow because of the optim procedure : lam_Nt > calibrate vr > Leslie matrix as f(Nt)

updated variables names in the run_analysis file