Fixing power-law + peak model and adding a chi_eff distribution to gwpopulation.py

[sharanbngr]

This PR does two things.

1. First the powerlaw needs to be normalized in the powerlaw + peak model for correct inference. The Gaussian in the peak is already normalized.

2. Adding a model for chi_eff distribution

[bfarr]

Sorry it's taken us so long to get to the PR @sharanbngr! Unsurprisingly, a lot has changed since you initiated your PR in February!

1. has been addressed in a separate PR; powerlaw_pdf is now properly normalized, so nothing special should need to be done regarding normalization in plpeak_primary_pdf().
2. adding a truncated normal distribution for effective spins would be a welcome addition. Could you move it down to the bottom of the spin models section, and use a non-logged standard deviation to keep things simple, i.e.,

def chi_eff_truncnorm(chi_eff, mu=0, sig=1):
    return truncnorm_pdf(chi_eff, mu, sig, -1, 1)

We've moved the parametric models to parametric/parametric.py. If you merge the main branch into yours things should go smoothly. Alternatively, I have an updated version of your branch locally that I would be happy to push if you would like me to.