predict method for felm from lfe package

Question

Does anyone have a nice clean way to get predict behavior for felm models?

library(lfe)
model1 <- lm(data = iris, Sepal.Length ~ Sepal.Width + Species)
predict(model1, newdata = data.frame(Sepal.Width = 3, Species = "virginica"))
# Works

model2 <- felm(data = iris, Sepal.Length ~ Sepal.Width | Species)
predict(model2, newdata = data.frame(Sepal.Width = 3, Species = "virginica"))
# Does not work

Answer 1

UPDATE (2020-04-02): The answer from Grant below using the new package fixest provides a more parsimonious solution.

As a workaround, you could combine felm, getfe, and demeanlist as follows:

library(lfe)

lm.model <- lm(data=demeanlist(iris[, 1:2], list(iris$Species)), Sepal.Length ~ Sepal.Width)
fe <- getfe(felm(data = iris, Sepal.Length ~ Sepal.Width | Species))
predict(lm.model, newdata = data.frame(Sepal.Width = 3)) + fe$effect[fe$idx=="virginica"]

The idea is that you use demeanlist to center the variables, then lm to estimate the coefficient on Sepal.Width using the centered variables, giving you an lm object over which you can run predict. Then run felm+getfe to get the conditional mean for the fixed effect, and add that to the output of predict.