Activity Index Calculation using Raw 'Accelerometry' Data
Reads raw 'accelerometry' from 'GT3X+' data and
plain table data to calculate Activity Index from 'Bai et al.' (2016)
<doi:10.1371/journal.pone.0160644>.  The Activity Index refers to the
square root of the second-level average variance of the three
'accelerometry' axes.

Computes the probability density function, hazard rate
function, integrated hazard rate function and the quantile
function for 44 commonly used survival models

A Package for Displaying Visual Scenes as They May Appear to an
Animal with Lower Acuity
Eleanor Caves [aut, cre],
Sönke Johnsen [aut]
This code provides a simple method for representing a visual scene as it may be seen by an animal with less acute vision. When using (or for more information), please cite the original publication.
imager (>= 0.40.1), fftwtools (>= 0.9-7), plotrix (>= 3.2.3),
tools, grid, grDevices, graphics

Adaptive Mixture of Student-t distributions
David Ardia, Lennart F. Hoogerheide and Herman K. van Dijk
This package provides functions to perform the fitting of an
adaptive mixture of Student-t distributions to a target density through its kernel function.
The mixture approximation can then be used as the importance density
in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to
obtain quantities of interest for the target density itself.

This package provides functions to perform the fitting of
an adaptive mixture of Student-t distributions to a target
density through its kernel function. The mixture approximation
can then be used as the importance density in importance
sampling or as the candidate density in the Metropolis-Hastings
algorithm to obtain quantities of interest for the target
density itself.

This package provides functions to perform the fitting of
an adaptive mixture of Student-t distributions to a target
density through its kernel function.  The mixture approximation
can then be used as the importance density in importance
sampling or as the candidate density in the Metropolis-Hastings
algorithm to obtain quantities of interest for the target
density itself.