- Update to Release 3.0.5
  + Bugs fixed
    * No longer allow installation if Python 3 but < 3.5.
    * Fixed error `undefined symbol: DATAPTR` if R < 3.5.
- Update to Release 3.0.4
  + Bugs fixed
    * Fixed conversion of `pandas` :class:`Series` of dtype `pandas.Int32Dtype`,
      or `pandas.Int64Dtype`.
- Update to Release 3.0.3
  + Bugs fixed
    * Fixed the evaluation of R code using the "R magic" was delaying all
      output to the end of the execution of that code, independently of
      whether the attribute `cache_display_data` was `True` or `False`.
    * Fixed conversion of :class:`pandas.Series` of `dtype` "object" when
      all items are either all of the same type or are :obj:`None`.
- Update to Release 3.0.2
  + Bugs fixed
    * Failing to import `pandas` or `numpy` when loading the "R magic" extension
      for jupyter/ipython was hiding the cause of the error in the `ImportError`
      exception.
    * Fallback when an R `POSIXct` vector does not had an attribute `"tzone"`.
    * Callback for console reset was not set during R initialization.
    * Fixed rternalized function returning rpy2 objects.
    * `--vanilla` is no longer among the default options used to initialize R.
- Update to Release 3.0.1
  + Bugs fixed
    * Script to install R packages for docker image never made it to version
      control.
    * Conversion of R arrays/matrices into numpy object trigged a segfault
      during garbage collection.
- Update to Release 3.0.0
  + New features
    * rpy2 can be installed without a development environment.
    * Unit tests are now relying on the Python module `pytest`.
    * :attr:`rpy2.rinterface.NA_Integer` is now only defined when the embedded R
      is initialized.
  + Changes
    * complete rewrite of :mod:`rpy2.rinterface`.
      :mod:`cffi` is now used to interface with the R compiled shared library.
      This allows ABI calls and removes the need to compile binaries. However, if
      compilation is available (when installing or preparing pre-compiled binaries)
      faster implementations of performance bottlenecks will be available.
    * calling :func:`rpy2.rinterface.endr` multiple times is now only ending R
      the first time it is called (note: an ended R cannot successfully be
      re-initialized).
    * The conversion system in the mod:`rpy2.robjects.conversion` now has only
      two conversions `py2rpy` and rpy2py`. `py2rpy` tries to convert any
      Python object into an object rpy2 can use with R and `rpy2py` tries
      to convert any rpy2 object into a either a non-rpy2 Python object or
      a mod:`rpy2.robjects` level object.
    * The method `get` for R environments is now called `find()` to avoid
      confusion with the method of the same name in Python (:meth:`dict.get`).
    * :class:`rpy2.robjects.vectors.Vector`, :class:`rpy2.robjects.vectors.Matrix`,
      and :class:`rpy2.robjects.vectors.Array` can no longer be used to create
      R arrays of unspecified type. New type-specific classes (for example for
      vectors :class:`rpy2.robjects.vectors.IntVector`,
      :class:`rpy2.robjects.vectors.BoolVector`,
      :class:`rpy2.robjects.vectors.FloatVector`,
      :class:`rpy2.robjects.vectors.ComplexVector`, or
      :class:`rpy2.robjects.vectors.StrVector`) should be used instead.
    * mod:`rpy2.rpy_classic`, an implementation of the `rpy` interface using
      :mod:`rpy2.rinterface` is no longer available.
    * :class:`rpy2.robjects.ParsedCode` and
      :class:`rpy2.robjects.SourceCode` are moved to
      :class:`rpy2.robjects.packages.ParsedCode` and
      :class:`rpy2.robjects.packages.SourceCode`.
  + Bugs fixed
    * Row names in R data frames were lost when converting to pandas data frames.
  + Known issues
    * Mismatch between R's POSIXlt `wday` and Python time struct_time's `tm_wday`.
- Update to Release 2.9.6
  + Bugs fixed
    * Latest release of :mod:`pandas` deprecated :meth:`DataFrame.from_items`.
    * Latest release of :mod:`pandas` requires categories to be a list
      (not an other sequence).
  + Known issues
    * The numpy buffer implemented by R arrays is broken for complex numbers

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