Last week I pushed an update of my analogue package to CRAN. The last release (0.6-23) was on CRAN sometime in Mar 2010 so an update was well overdue.

This (0.7-0) is a major update to analogue containing lots of new functionality. The main changes are:

• chooseTaxa() is a new utility function for choosing which taxa to select from a species training set. Usual practice is to select the most abundant species (or conversely, not select the rare species) from a data set for plotting or building a transfer function. Whilst this idea might be a bit questionable to an ecologist (surely the rare things are important for something? Or are they? That in itself is an interesting question!) it helps with transfer function model building because to the modelling functions the rare species are often noise. chooseTaxa() allows the user to select species that reach at least a stated abundance (say 2%) and/or are present in at least a stated number of samples (say 5 samples). The distinction allows us to a low abundance taxon that is ubiquitous in a training set.
• timetrack() can overlay data from a sediment core on to an ordination of a training set. Usually the training set contains data on species-environment relationships, whilst the environment is typically not measured (or even available) for samples in a sediment core. We can, however, use the species scores from the ordination of the training data to position the sediment core data into the ordination: in Canoco-speak, these samples are known as supplementary (or passive for older Canoco-aware users). This type of overlay is known as a timetrack. The function has a plot() method with which to visualise the timetrack overlay.
• prcurve() is a user-friendly wrapper function to fit a principal curve to palaeoecological data. We often summarise change in species composition in a sediment core through time using ordination axes, say PCA axes 1 and 2 scores, but standard ordination techniques tend to require two or more axes to capture the dominant gradient in the data. Geometrically, we see this as an arch or horseshoe configuration in the ordination diagram. A principal curve is a generalisation of the first principal component, and is a smooth curve fitted in high dimensions using smoothers. Where there is a dominant gradient (i.e. temporal change) the principal curve will tend to explain more of the variance in the data using a single “component” than 2 or more PCA or CA axes. The underlying fitting is performed using the princurve package, which is now a dependency. The function is named after the standard R function prcomp(), which performs PCA.
• Stratiplot() has gained the ability to draw zones over the stratigraphy, and it can now accommodate mixtures of proportional (e.g % composition) and absolute (e.g. biogeochemical data) data types. The latter functionality is experimental at the moment and the interface is a little clunky.
• The package has a new data set; abernethy. These are the classic Abernethy Forest pollen data of Hilary Birks. These data have been used extensively as an example data set in the development of numerical techniques for the analysis of palaeoecological data. The package uses these data to illustrate the fitting of principal curves using new function prcurve().

There were also many bug fixes and minor enhancements. Full details can be found in the ChangeLog, the relevant portion of which is appended below. Several development releases were made on R-forge after the 0.6-23 release to CRAN. These development versions were not publicly released, but the changes they implemented are all present in 0.7-0 of analogue.

Version 0.7-0

	* timetrack: new function to passively project sediment core
	samples within an ordination of training or reference set
	samples. Both unconstrained and constrained ordinations are
	supported using the Vegan package. 'fitted' and 'plot' methods
	are available.

	* prcurve: new function to fit principal curves to sediment
	core samples. A 'plot' method is also provided. The function uses
	functionality from the princurve package, which is now a
	dependency.

	Several support functions are also provided; 'smoothSpline' is
	a wrapper to 'smooth.spline' for fitting splines to individual
	species in order to fit the principal curve. 'initCurve'
	implements several methods for initialising the principal curve.

	* Stratiplot: if 'zones' are supplied, a legend on the right-hand
	side of the diagram can be drawn by setting argument 'drawLegend'
	to TRUE (the default). Currently, only simple blocks that
	demarcate the zone boundaries are drawn and labelled using
	argument 'zoneNames'.

	First attempt to allow both relative (percentages or proportions)
	and absolute variables, or mixtures thereof, in a single plot. The
	user is free to specify which variables should be treated as relative
	or absolute, and variables marked as absolute will be drawn with
	fixed-width panels, the size of which can be controlled via argument
	'absoluteSize' (default is 0.5 * largest panel width). Consider
	this functionality unstable at the moment.

	* residLen: was not 'join'-ing the training set and passive data
	correctly and would fail if species were found in one but not the
	other data set.

	* tran: improvements to the underlying code.

	* distance: resilience to NA in "gower", "alt.gower", "mixed".

	* cma: added methods for 'mat' and 'predict.mat' objects. These
	allow you to retrieve the k-closest analogues for training set
	and prediction data respectively.

	* dissimilarities: new method for 'mat' objects.

	* datasets: package datasets have been resaved with optimal
	compression determined via resaveRdaFiles(). This has reduced
	the package tarball size considerably. As a result, however,
	analogue now requires R version 2.10.0 or later.

	* predict.wa: bug in bootstrap and k-fold CV methods when
	tolerance down-weighting was used.

	* fixUpTol: erroneous error criterion would cause CV of WA models
	with tolerance down-weighting to stop with an error.

	* waFit: new function that encapsulates the main WA computations.
	This is currently used by wa() and with the intention of being
	used in all functions that computed WA transfer function models.

	* Examples: Streamlined some further examples to use Imbrie &
	Kipp data set, and to not re-run the same code again. Improves
	package check times by a second or two on my PC.

Version 0.6-26

	* abernethy: New data set containing the classic Abernethy Forest
	data of Birks and Mathewes (1978)

	* Stratiplot: Preserves the names component as far as is
	possible, even to the extent of processing the names after the
	manipulations arising from the formula interface.

	Bug in padding of the y-axis now fixed; default is to add 1% of
	the range y-axis to the y-axis limits specified.

	Bug in computing length of variable labels when 'strip = FALSE'
	now fixed.

	* panel.Stratiplot: Add capability to draw zones on stratigraphic
	plots via new argument 'zones' which takes the numeric levels of
	the zone boundaries on the scale of the plot y-axis. How the
	zone markers are drawn can be controlled via several graphical
	parameters. See ?panel.Stratiplot.

	* chooseTaxa: Explicitly preserves row and column names.

	* DESCRIPTION: prematurely added princurve as a dependency in
	previous version.

Version 0.6-25

	* chooseTaxa: new function to select species on basis of number
	of occurrences and maximum abundance. Function is an S3 generic
	with a default method.

Version 0.6-24

	* Dependencies: package now depends on package 'grid'.

	* Stratiplot: gains ability to draw variable labels above the
	plot panels so that the plots conform to common standards. If
	you prefer the 'strips' of Lattice plots, set 'strip = TRUE'
	to get the old behaviour.

	Stratiplot was fixinging the min(ylim) value at 0 and contained
	redundant calls to set the y-axis limits. The behaviour has been
	rationalised and a new 'ylim' argument added. The default
	behaviour uses the range of the y-data for 'ylim'.

	* panel.Stratiplot: fix warning messages (from Grid) due to
	inappropriate colour specification. Reference lines in
	Stratiplot now plot correctly again.

	* plot.roc: was resetting the plotting region at the end of
	plotting even when there was no need to do so.

	* residuals: Residuals were defined as \hat{x}_i - x_i to match
	fitted vs. observed scatterplots. Definition of residuals in wa()
	and related functions has been changed to the more common
	definition of x_i - \hat{x}. Reported by Andreas Plank and Steve
	Juggins.

	* plot.wa: Following changed definition of residuals, plot.wa()
	now plots observed values on the y-axis and fitted values on the
	x-axis for 'which = 1'.

	* summary.predict.mat: print method was incorrectly extracting
	the model estimates for training set samples.

	* predict.wa: fix minor bug with CV when tolerance DW was used.

	* Package: reduced package check time in examples, by using
	the Imbrie & Kipp data.