Nonnegative generalized linear model Is it possible that all the parameters of a generalized linear model are constrained to be non-negative? If so, when? Any examples?
 A: Well, it is certainly possible, but mostly only some coefficients will be restricted. A glm (generalized linear model) have a linear predictor $\eta(x)=\beta^T x$ and the parameter vector $\beta$ then plays the same role as in linear regression, so restrictions can apply in the same way as with linear regression. 

An example is Linear model with constraints on coefficients in terms of ratios, although the method used there with a glm would lead to a nested optimization. Another example is Looking for function to fit sigmoid-like curve, where a monotone spline is fit, which could also be combined with a glm. The non-negativity restrictions there is used to impose monotone increasing, if the same coefficients had been required to be nonpositive the result would have been monotone decreasing. 
This last example is an example of shape-restricted splines, there is now an R package (actually multiple) implementing those ideas. cgam implements them in the setting of gam's (generalized additive models), a generalization of glm's.  An example from that package's helpfile is:
library(cgam)
data(kyphosis, package="gam")

# regress Kyphosis on Age, Number, and Start under the restrictions:
# "concave", "increasing and concave", and "decreasing and concave"

fit <- cgam(Kyphosis ~ conc(Age) + incr.conc(Number) + decr.conc(Start), 
       family = binomial(), data = kyphosis) 

A: You can fit GLMs with nonnegativity constraints on the fitted coefficients using the glm.cons function in the zetadiv package : https://rdrr.io/cran/zetadiv/man/glm.cons.html (the p values and confidence intervals are not calculated correctly though - there is no correct closed form solution for them under nonnegativity constraints; bootstrapping would be the way to go). If you would like only some coefficients to have nonnegativity constraints you can use https://cran.r-project.org/web/packages/restriktor/index.html. Or there is the glmnet package, which fits GLMs with LASSO or elastic net regularization, where you have the options lower.limits and upper.limits with which you can specify box constraints.
Finally, to fit nonnegative Poisson, binomial and negative binomial GLMs there is http://finzi.psych.upenn.edu/R/library/addreg/html/00Index.html.
