The basic idea is to have a metrics package, we can start with ROC/AUC (first on GPU, then if necessary on CPU). It should mimic the SKLearn API and results:

http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html

Requirements:

1. GPU implementation. It can use Thrust and other libraries as first step.
2. Bindings for other languages (currently CTypes but soo

The docstrings for e.g. ftest are not present in the generated
documentation but are quite useful. I think the full API should be in
the generated docs.

In most of the other projects I contribute to, we use pydocstyle to check conformity to PEP257. Once #259 is in, we can enable it by simply switching the line $ make flake to $ make pep. It's just a lot of errors, so it can be done when we are bored :)

Please specify whether your issue is about:

• a possible bug
• a question about package functionality
• a suggested code or documentation change, improvement to the code, or feature request

What is the proper way to specify factor values in the at list?

library(margins)
dat <- mtcars
dat$cyl <- as.factor(dat$cyl)
mod <- lm(mpg ~ cyl + hp, dat)
margins(mod, at 

I tried to use the original glm documentation. But since there is about 95% missing in PyGLM, it's not a well working approach. So while I think this is a good project, it's not an option if you have the desire to get something done.

library(mboost)

myiris <- as.list(iris)
myiris$class <- factor(levels(iris$Species)[-nlevels(iris$Species)])

## Now fit the linear array model
mlm <- mboost(Species ~ bols(Sepal.Length, df = 2) %O%
                bols(class, df = 2, contrasts.arg = "contr.dummy"),
              data = myiris,
              family = Multinomial())

# works
predict(mlm)
# gives weird error messa

See lme4 manual (http://lme4.r-forge.r-project.org/book/Ch2.pdf) and Bolker's article.
Nested random effects (1|a/b) are actually equivalent to (1|a) + (1|b), as long as levels in b are clearly specified so they can't be confounded. See also https://m-clark.github.io/mixed-models-with-R/extensions.html#crossed-vs.nested

The tinyobjloader repository seems to have moved to a new location:

https://github.com/tinyobjloader/tinyobjloader/

It would be better to update the git submodule setting as well.