I've looked further into how R handles NA/Nan/Inf (for floating-point data). It has its own logic but I'm not sure it's very intuitive, or something we'd want to emulate. Yes, R does have distinct internal representations for NA and NaN. But in some ways NaNs are treated as a subset of NAs, while in other ways NAs are treated as if they were NaNs. If you do x <- 0/0 # or x <- log(-1) you get a value that gives TRUE for both is.nan(x) and is.na(x). If you do x <- NA you get a value that answers TRUE to is.na(x) but FALSE to is.nan(x). NaNs are treated like NAs in that they are automatically skipped by default when running a linear regression via lm(). Infinities are not treated the same way. That is, if you have data vectors x and y and you define one of the x-values to log(-1) you get a warning, In log(-1) : NaNs produced but the relevant observation is skipped by lm(), as in gretl. If you define an x-value to log(0) (producing -Inf) you get no warning but the regression fails with Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : NA/NaN/Inf in foreign function call (arg 1) And (as we already knew) NAs are treated as NaNs in that 0*NA = NA. Allin