This should download and install all dependencies and make the spikestim
package importable from anywhere:
git clone [email protected]:dattalab/spikestim.git
cd spikestim
pip install -e .
The main functions are fit_maxlike, get_posterior_samples, make_anscombe_stabilizer,
and make_laubscher_stabilizer.
from spikestim import fit_maxlike
r, p = fit_maxlike(data, r_guess=10.)The implementation is in spikestim/negbin_maxlike.py.
Maximum likelihood can have some defects; for example, there is no maximum
likelihood estimator when np.var(data) > np.mean(data). Furthermore, the
fitting functions may run into local optima (and it must be initialized with
r_guess).
Alternatively, you can do a Bayesian analysis by collecting MCMC samples from a posterior over the negative binomial parameters:
from spikestim import get_posterior_samples
samples = get_posterior_samples(data) # samples is a list [(r1, p1), (r2, p2), ...]The implementation is in spikestim/negbin_bayes.py.
See the example files stabilizer_test.py and stabilizing_data.py.
Try reading the examples in the examples directory.