Create beautiful images of source code using
    'carbon.js'<https://carbon.now.sh/about>.

Performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) <doi:10.1111/rssc.12599>). 
  Also models the occurrence of radiocarbon samples as a variable-rate (inhomogeneous) Poisson process, plotting the posterior estimate for the occurrence rate of the samples over calendar time, and providing information about potential change points.

The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability.

Tools that can aid in the assessment of the autonomic regulation 
		of cardiovascular physiology. The aims of this package are to: 1) study 
		electrocardiography (both intervals and morphology) as extensions 
		of signal processing, 2) study circadian rhythms and how it effects 
		autonomic physiology, 3) assess clinical risk of autonomic dysfunction on 
		cardiovascular health through the perspective of epidemiology and causality.
		The analysis of circadian rhythms through cosinor analysis are built upon 
		the methods by Cornelissen (2014) <doi:10.1186/1742-4682-11-16> and 
		Refinetti, Cornelissen, Halberg (2014) <doi:10.1080/09291010600903692>.

Datasets to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Third Edition, Sage (2019).

Identification of cardinal dates
             (begin, time of maximum, end of mass developments)
             in ecological time series using fitted Weibull functions.

A collection of simple simulation datasets designed for
    generating Nonlinear Dimension Reduction representations
    techniques such as t-distributed Stochastic Neighbor Embedding,
    and Uniform Manifold Approximation and Projection. These
    datasets serve as a valuable resource for understanding the
    reliability of Nonlinear Dimension Reduction representations in various contexts.