.. rbartpackages/docs/guide/quickstart.rst
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Quickstart
==========

Each wrapped R package has its own submodule. Import the one you need and call
the wrapper class like the corresponding R function; arguments are converted to
R, and the fitted R object's components become Python attributes (with ``.``
replaced by ``_``).

.. code-block:: python

    import numpy as np
    from rbartpackages import BART3

    x_train = np.random.randn(100, 5)
    y_train = x_train[:, 0] + 0.1 * np.random.randn(100)

    bart = BART3.gbart(x_train=x_train, y_train=y_train, ndpost=200)
    y_pred = bart.predict(x_train)  # shape (ndpost, n)

Argument names use Python underscores in place of R dots: pass ``x_train`` for
the R argument ``x.train``. The same pattern works for the other wrappers, e.g.
`rbartpackages.BART`, `rbartpackages.dbarts`, `rbartpackages.bartMachine`, and
`rbartpackages.missBART`.

Data frames and other array types
---------------------------------

With the matching extra installed, you can pass `pandas` / `polars` data frames
or `jax` arrays directly; they are converted to the appropriate R object. This
is required for `rbartpackages.bartMachine`, whose ``X`` argument must be an R
data frame.

R documentation
---------------

The original R documentation of each function is appended to the corresponding
wrapper class docstring, so :code:`help(BART3.gbart)` shows the upstream
reference.