fixedLassoInf with constrained lasso? #55

We are fitting a lasso model in glmnet as follows:
fit = glmnet(x, y, alpha=1, intercept=F, standardize=F, lower.limits=0)

We were hoping to use selective inference to get p-values on the coefficients. However, there are differences between our model (above) and the given example. For example, we are fitting without an intercept or standardization - but perhaps more importantly, limits on the coefficients. Do you have advice for extending selective inference to the above example? If so, we would really appreciate it. Thank you!