Gee models

[shah-in-boots]

This should allow for linear and logistic models to be run using the original gee::gee() function. I've added appropriate tests as well.

I haven't yet put together poisson, but wanted to see if this seemed like a reasonable addition thus far. Also, not sure if poissonreg would be a better home for that. When we're discussion models based on distribution, they really start melting together at the edges.

[topepo]

Sorry for the delay. It looks great. One request, please add gee to Suggests.

[topepo]

Also, yes, an engine for Poisson models would be better in poissonreg. That shouldn't be too bad since gee_fit() is exported here.

[topepo]

(For posterity) There are some significant differences in how gee() operates and most other modeling functions, especially glm(). None of these are documented in ?gee:

• gee() inherits from glm so, theoretically, predict() should work. It does not since there are no rank or qr elements to gee objects. These are required by predict.lm(), which is called by predict.glm(). This is ironic since gee() does an initial glm() computation up-front for starting values and these are not attached. To solve this, gee_fit() computes another glm() fit and adds the needed objects.

• Even though predict.glm() calls predict.lm(), it will issue a warning "calling predict.lm(<fake-lm-object>) ...". We subclass the model fit with lm to avoid this.

  • For binomial models, gee() requires 0/1 data (as opposed to a factor). We do the conversion in gee_fit() now.

[topepo]

The GH actions failures are due to the nloptr package failing to build from source. I'll merge.

Thanks for the help @asshah4. If you add another PR to poissonreg, it should be pretty similar to the linear regression work here.

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