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I have implemented a new statistical model in R and it works in my sandbox, but I would like to make it more standard. A good comparison is lm() , where I can take a model object and: apply the summary() function extract the coefficients of the model extract residuals from the fitted (training) data update the model apply the predict() function apply plot() to pre-selected descriptive plots engage in many other kinds of joy I've looked through the R manuals, searched online, and thumbed through several books, and, unless I'm overlooking something, I can't find a good tutorial on what should go into a new model package. Although I'm most interested in thorough references or guides, I'll keep this post focused on a question with two components: What are the key components that are usually expected to be in a model object? What are typical functions that are usually implemented in a modeling package? Answers could be from the R Core (or package developers) perspective or from the perspective of users, e.g. users expect to be able to use functions like summary, predict, residuals, coefficients, and often expect to pass a formula when fitting a model.

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