Description Usage Arguments Details Value References See Also Examples

Fit probability distributions, via discrete kernel smoothing over integer-indexed frequency data.

NOTE THAT THESE OBJECTS ARE LIKELY TO BE CONVERTED TO S4 OBJECTS, IN THE NEAR FUTURE.

ALSO, NOTE THAT THEIR INTERNAL STRUCTURE (THAT IS, THEIR ATTRIBUTES/SLOTS), IS SUBJECT TO CHANGE.

IN PRINCIPLE, YOU SHOULD NOT ACCESS ATTRIBUTES/SLOTS, DIRECTLY.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
pmfuv.dks (x = 1:length (h), h=1, ...,
bw, smoothness=1,
kernel=BIWEIGHT.CKERNEL,
bw.method="ph.default",
Xlim = c (a, b),
a = min1 (x), b=Inf)
cdfuv.dks (x = 1:length (h), h=1, ...,
bw, smoothness=1,
kernel=BIWEIGHT.CKERNEL,
bw.method="ph.default", tail="lower",
Xlim = c (a, b),
a = min1 (x), b=Inf)
qfuv.dks (x = 1:length (h), h=1, ...,
bw, smoothness=1,
kernel=BIWEIGHT.CKERNEL,
bw.method="ph.default",
Xlim = c (a, b),
a = min1 (x), b=Inf)
``` |

`x` |
Integer vector of integer-indexed discrete observations, or bins of such observations. |

`h` |
Positive numeric vector of frequencies (or weights), which can be fractional. |

`bw` |
Odd positive integer value, giving the bandwidth parameter. |

`smoothness` |
Positive numeric value, giving the relative bandwidth. |

`kernel` |
A (continuous) kernel object. |

`bw.method` |
String, the bandwidth selection method. |

`tail` |
String, either "lower" or "upper". |

`Xlim` |
In principle, a length-two integer vector, giving the limits of X. |

`a, b` |
In principle, integer values. |

`...` |
Additional arguments not allowed. |

PLEASE SET NOTES IN DESCRIPTION FIELD.

Refer to the vignette for more information.

Note that if x has non-unique values, then duplicated x (and their h) values are aggregated.

And currently, any row names will be ignored.

Also note that the truncation method may change in future updates.

Self-referencing function objects.

Refer to Runtime Function Objects

Refer to the vignette for an overview, references and better examples.

Kernels

Succinct Constructors

Continuous Kernel Smoothing, Categorical Distributions, Empirical-Like Distributions

is.dks, ph.printf.phmodel, ph.plotf.dksuv

Bandwidth Selection

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