coenocliner 0.2-2
=================
* `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
coenocliner 0.2-1
=================
* Minor package update to fix issues under `R CMD check` in the
development version of R.
coenocliner 0.2-0
=================
* 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.
coenocliner 0.1-0
=================
* 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.