document reduction in complexity🥳

R/README.md

@@ -2,8 +2,8 @@
 
 rinfa is an R interface to the Rust machine learning library linfa.
 
-The linfa crate is composed of several different modules, each implementing support for a given kind of model. For module `model1`, the code that "bridges" R and that linfa module is in `src/rust/src/model1.rs`. The `model.rs` module will supply a (non-exported) `fit()` and `predict()` R function for the given model type, with names `fit_model_a()` and `predict_model_a()`, where `model_a` is the name of the "model type" in tidymodels that corresponds to the kind of model implemented in the linfa module. (There's not always a 1-to-1 relationship between `model1` and `model_a`.) `fit_model_a()` takes in a vector of length `X * ncol(X)` that is `c(X)` for some model matrix `X`, and the Rust code in `model.rs` takes care of reshaping the vector into an array. `fit_model1()` is the lowest-level R interface to `model1`, is not exported, and is not intended for use by end-users.
+The linfa crate is composed of several different modules, each implementing support for a given kind of model. For module `model1`, the code that "bridges" R and that linfa module is in `src/rust/src/model1.rs`. The `model1.rs` module will supply a (non-exported) `fit()` and `predict()` R function for the given model type, with names `fit_model_a()` and `predict_model_a()`, where `model_a` is the name of the "model type" in tidymodels that corresponds to the kind of model implemented in the linfa module. (There's not always a 1-to-1 relationship between `model1` and `model_a`.) `fit_model_a()` has an "XY" interface (i.e. numeric model matrix X, vector outcome Y). `fit_model1()` is the lowest-level R interface to `model1`, is not exported, and is not intended for use by end-users.
 
-`fit_model_a()` is wrapped by an exported but `@keywords internal` function `.linfa_model_a()`, which has an "XY" interface (numeric model matrix X, vector outcome Y). `.linfa_model_a()` takes care of reshaping the data to the format expected by `fit_model_a()` and putting together a classed R object. While the XY interface is exported, it should not be considered stable.
+`fit_model_a()` is wrapped by an exported but `@keywords internal` function `.linfa_model_a()`, which also has an XY interface. `.linfa_model_a()` takes care of argument checking, calling the internal function, and putting together a classed R object. While this interface is exported, it should not be considered stable.
 
 The "public" interface to rinfa models is via tidymodels (or, more specifically, parsnip). To use rinfa (and thus `.linfa_model_a()`) as a modeling engine, use the code `model_a(engine = "rinfa")`. Models can be fitted either with an XY interface (which will result in no `model.matrix()` overhead) or the formula interface.