Linear binning for 1- to 4-dimensional data.

1 | ```
binning(x, H, h, bgridsize, xmin, xmax, supp=3.7, w, gridtype="linear")
``` |

`x` |
matrix of data values |

`H,h` |
bandwidth matrix, scalar bandwidth |

`xmin,xmax` |
vector of minimum/maximum values for grid |

`supp` |
effective support for standard normal is [-supp,supp] |

`bgridsize` |
vector of binning grid sizes |

`w` |
vector of weights. Default is a vector of all ones. |

`gridtype` |
not yet implemented |

As of ks 1.10.0, binning is available for unconstrained
(non-diagonal) bandwidth matrices. Code is used courtesy of A. &
J. Gramacki, and M.P. Wand. Default `bgridsize`

are
d=1: 401; d=2: rep(151, 2); d=3: rep(51, 3); d=4: rep(21, 4).

Returns a list with 2 fields

`counts` |
linear binning counts |

`eval.points` |
vector (d=1) or list (d>=2) of grid points in each dimension |

Gramacki, A. & Gramacki, J. (2015) *FFT-based fast computation of
multivariate kernel estimators with unconstrained bandwidth
matrices*. URL: arxiv.org/abs/1508.02766.

Wand, M.P. & Jones, M.C. (1995) *Kernel Smoothing*.
Chapman & Hall. London.

1 2 |

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