NEWS

coenocliner 0.2-3 (2021-02-14)

Fix an issue causing vignette compiling to fail due to an update to the LaTeX package *hyperref*.

'NegBin()' and 'ZINB()' incorrectly specified the gamma part of the distribution. The 'shape' argument to 'rgamma()' should have been '1/alpha' where 'alpha' was used previously. Also clarified the paramterization of the negative binomial used by 'NegBin()' and 'ZINB' as the NB2 version.
'NegBin()' and 'ZINB()' allow for vector 'alpha' inputs. #25

Minor package update to fix issues under 'R CMD check' in the development version of R.

Jari Oksanen is now listed as a contributor to the package having added several new stochastic distributions.
The object returned by 'coenocline()' now has S3 class '"coenocline"' and inherits from the '"matrix"' class.
A 'print()' method has been added for 'coenocline()' which displays some summary information and the first 'n' lines of the simulated counts. The 'print()' method uses a new internal function modelled on the way **dplyr** prints data frames.
A ‘stack()' method for 'coenocline()' was added. This makes it much easier to reshape the simulated count data into a format suitable for use with **ggplot** or **lattice** graphics, or R’s modelling functions.
An enhanced 'plot()' method for 'coenocline()' objects is provided, which can draw 1-d plots of single gradient simulations.
A 'persp()' method is now provided which can produced 3-d perspective plots od simulations with 2 gradients.
Two new stochastic distributions were added by Jari Oksanen - Zero-inflated Binomial - Zero-inflated Beta-binomial
A new extractor function is provided, 'locations()', which extracts the gradient locations at which counts were simulated. Bug fixes
Jari Oksanen noticed an annoying but important bug in the 2D Beta response function; the 'gamma' parameter for the second gradient was being ignored, and the value of 'gamma' for the first gradient was used instead.

An R package for coenocline simulation; generating simulated species abundance or occurence data along one or two gradients
First public release of coenocliner on CRAN
Species response can be parameterised using either the classic Gaussian response model or the generalise beta response model
Random count or occurence data can be simulated from species responses using random draws from a Poisson, Negative Binomial, Binomial, Beta-binomial, ZIP, ZINB, or Bernoulli distribution with the parameterised response curve taken as the mean or expectation of the distribution to draw from
The main user-facing function is 'coenocline()'. See '?coenocliner' and '?coenocline' for further details and examples of usage
A basic overview and introductory tutorial for coenocliner is available. Run 'browseVignettes("coenocliner")' in R to access the PDF, R code and sources.