statsmodel fractional logit model
I assume fractional Logit in the question refers to using the Logit model to obtain the quasi-maximum likelihood for continuous data within the interval (0, 1) or [0, 1].
The discrete models in statsmodels like GLM, GEE, and Logit, Probit, Poisson and similar in statsmodels.discrete, do not impose an integer condition on the response or endogenous variable. So those models can be used for fractional or positive continuous data. 
The parameter estimates are consistent if the mean function is correctly specified. However, the covariance for the parameter estimates are not correct under quasi-maximum likelihood. The sandwich covariance is available with the fit argument, cov_type='HC0'. Also available are robust sandwich covariance matrices for cluster robust, panel robust or autocorrelation robust cases.
eg.
result = sm.Logit(y, x).fit(cov_type='HC0')
Given that the likelihood is not assumed to be correctly specified, the reported statistics based on the resulting maximized log-likelihood, i.e. llf, ll_null and likelihood ratio tests are not valid.
The only exceptions are multinomial (logit) models which might impose the integer constraint on the explanatory variable, and might or might not work with compositional data. (The support for compositional data with QMLE is still an open question because there are computational advantages to only support the standard cases.)
can anyone let me know what is the method of estimating the parameters in fractional logit model in statsmodel package of python? 
And can anyone refer me the specific part of the source code of fractional logit model?