<!-- Hey Emacs make this -*- mode: XML -*- -->
<CRANTaskView>
  <name>Environmetrics</name>
  <topic>Analysis of Ecological and Environmental Data</topic>
  <maintainer email="ucfagls@gmail.com">Gavin Simpson</maintainer>
  <version>2016-09-09</version>
  
  <info>
    <h4>Introduction</h4>
    <p>This Task View contains information about using R to analyse ecological and environmental data.</p>
    <p>The base version of R ships with a wide range of functions for use within the field of environmetrics.
      This functionality is complemented by a plethora of packages available via CRAN, which provide specialist
      methods such as ordination &amp; cluster analysis techniques. A brief overview of the available packages is 
      provided in this Task View, grouped by topic or type of analysis. As a testament to the popularity of R for the 
      analysis of environmental and ecological data, a <a href="http://www.jstatsoft.org/v22/" >special volume</a> of 
      the <em>Journal of Statistical Software</em> was produced in 2007.</p>
    <p>Those useRs interested in environmetrics should consult the <view>Spatial</view> view. 
      Complementary information is also available in the <view>Multivariate</view>, <view>Phylogenetics</view>, 
      <view>Cluster</view>, and <view>SpatioTemporal</view> task views.</p>
    <p>If you have any comments or suggestions for additions or improvements, then please contact the
      <a href="mailto:ucfagls@gmail.com?subject=Environmetrics%20Task%20View">maintainer</a>.</p>
    <p>A list of available packages and functions is presented below, grouped by analysis type.</p>
  
    <h4>General packages</h4>
    <p>These packages are general, having wide applicability to the environmetrics field.</p>
    <ul>
      <li>Package <pkg>EnvStats</pkg> is the successor to the S-PLUS module <em>EnvironmentalStats</em>, both by Steven Millard. A <a href="http://doi.org/10.1007/978-1-4614-8456-1">user guide in the form of a book</a> has recently be released.</li>
    </ul>
    
    <h4>Modelling species responses and other data</h4>
    <p>Analysing species response curves or modeling other data often involves the fitting of standard statistical models 
      to ecological data and includes simple (multiple) regression, Generalised Linear Models (GLM), extended regression 
      (e.g. Generalised Least Squares [GLS]), Generalised Additive Models (GAM), and mixed effects models, amongst 
      others.</p>
    <ul>
      <li>The base installation of R provides <code>lm()</code> and <code>glm()</code> for fitting linear and generalised
	linear models, respectively.</li>
      <li>Generalised least squares and linear and non-linear mixed effects models extend the simple regression model
	to account for clustering, heterogeneity and correlations within the sample of observations. Package <pkg>nlme</pkg>
	provides functions for fitting these models. The package is supported by Pinheiro &amp; Bates (2000) 
	<em>Mixed-effects Models in S and S-PLUS</em>, Springer, New York. An updated approach to mixed effects models, 
	which also fits Generalised Linear Mixed Models (GLMM) and Generalised non-Linear Mixed Models (GNLMM) is provided 
	by the <pkg>lme4</pkg> package, though this is currently beta software and does not yet allow correlations within 
	the error structure.</li>
      <li>Recommended package <pkg>mgcv</pkg> fits GAMs and Generalised Additive Mixed Models (GAMM) with 
	automatic smoothness selection via generalised cross-validation. The author of <pkg>mgcv</pkg> has
	also written a companion monograph, Wood (2006) <em>Generalized Additive Models; An Introduction with R</em>
	Chapman Hall/CRC, which has an accompanying package <pkg>gamair</pkg>.</li>
      <li>Alternatively, package <pkg>gam</pkg> provides an implementation of the S-PLUS function <code>gam()</code> that 
	includes LOESS smooths.</li>
      <li>Proportional odds models for ordinal responses can be fitted using <code>polr()</code> in the
	<pkg>MASS</pkg> package, of Bill Venables and Brian Ripley.</li>
      <li>A negative binomial family for GLMs to model over-dispersion in count data is available in <pkg>MASS</pkg>.</li>
      <li>Models for overdispersed counts and proportions
	<ul>
	  <li>Package <pkg>pscl</pkg> also contains several functions for dealing with over-dispersed count data. Poisson or 
	    negative binomial distributions are provided for both zero-inflated and hurdle models.</li>
	  <li><pkg>aod</pkg> provides a suite of functions to analyse overdispersed counts or proportions, plus utility 
	    functions to calculate e.g. AIC, AICc, Akaike weights.</li>
	</ul>
      </li>
      <li id="changepoints">Detecting change points and structural changes in parametric models is well catered for in the 
	<pkg>segmented</pkg> package and the <pkg>strucchange</pkg> package respectively. <pkg>segmented</pkg> has recently been 
	the subject of an R News article (<a href="http://CRAN.R-project.org/doc/Rnews/Rnews_2008-1.pdf" 
					     title="Link to segment article in R News">R News, volume 8 issue 1</a>).</li>
    </ul>
    
    <h4>Tree-based models</h4>
    <p>Tree-based models are being increasingly used in ecology, particularly for their ability to fit flexible models to
      complex data sets and the simple, intuitive output of the tree structure. Ensemble methods such as bagging, boosting and
      random forests are advocated for improving predictions from tree-based models and to provide information on uncertainty
      in regression models or classifiers.</p>
    <p>Tree-structured models for regression, classification and survival analysis, following the ideas in the CART book, 
      are implemented in</p>
    <ul>
      <li>recommended package <pkg>rpart</pkg></li>
      <li><pkg>party</pkg> provides an implementation of conditional inference trees which embed tree-structured regression 
	models into a well defined theory of conditional inference procedures</li>
    </ul>
    <p>Multivariate trees are available in</p>
    <ul>
      <li>package <pkg>party</pkg> can also handle multivariate responses.</li>
    </ul>
    <p>Ensemble techniques for trees:</p>
    <ul>
      <li>The Random Forest method of Breiman and Cutler is implemented in <pkg>randomForest</pkg>, providing classification 
	and regression based on a forest of trees using random inputs</li>
      <li>Package <pkg>ipred</pkg> provides functions for improved predictive models for classification, regression and 
	survival problems.</li>
    </ul>
    <p>Graphical tools for the visualization of trees are available in package <pkg>maptree</pkg>.</p>
    <p>Packages <pkg>mda</pkg> and <pkg>earth</pkg> implement Multivariate Adaptive Regression Splines (MARS), a technique
      which provides a more flexible, tree-based approach to regression than the piecewise constant functions used in 
      regression trees.</p>
    
    <h4>Ordination</h4>
    <p>R and add-on packages provide a wide range of ordination methods, many of which are specialised techniques
      particularly suited to the analysis of species data. The two main packages are <pkg>ade4</pkg> and
      <pkg>vegan</pkg>. <pkg>ade4</pkg> derives from the traditions of the French school of 
      <q>Analyse des Donnees</q> and is based on the use of the duality diagram. <pkg>vegan</pkg> follows
      the approach of Mark Hill, Cajo ter Braak and others, though the implementation owes more to that presented in
      Legendre &amp; Legendre (1988) <em>Numerical Ecology, 2<sup>nd</sup> English Edition</em>, Elsevier. Where the
      two packages provide duplicate functionality, the user should choose whichever framework that best suits their 
      background.</p>
    <ul>
      <li>Principal Components (PCA) is available via the <code>prcomp()</code> function. <code>rda()</code> (in package 
	<pkg>vegan</pkg>), <code>pca()</code> (in package <pkg>labdsv</pkg>) and <code>dudi.pca()</code> (in
	package <pkg>ade4</pkg>), provide more ecologically-orientated implementations.</li>
      <li>Redundancy Analysis (RDA) is available via <code>rda()</code> in <pkg>vegan</pkg> and <code>pcaiv()</code> in 
	<pkg>ade4</pkg>.</li>
      <li>Canonical Correspondence Analysis (CCA) is implemented in <code>cca()</code> in both <pkg>vegan</pkg> and
	<pkg>ade4</pkg>.</li>
      <li>Detrended Correspondence Analysis (DCA) is implemented in <code>decorana()</code> in <pkg>vegan</pkg>.</li> 
      <li>Principal coordinates analysis (PCO) is implemented in <code>dudi.pco()</code> in <pkg>ade4</pkg>, <code>pco()</code>
        in <pkg>labdsv</pkg>, <code>pco()</code> in <pkg>ecodist</pkg>, and <code>cmdscale()</code> in package <pkg>MASS</pkg>.</li>
      <li>Non-Metric multi-Dimensional Scaling (NMDS) is provided by <code>isoMDS()</code> in package <pkg>MASS</pkg>
        and <code>nmds()</code> in <pkg>ecodist</pkg>. <code>nmds()</code>, a wrapper function for <code>isoMDS()</code>, 
        is also provided by package <pkg>labdsv</pkg>. <pkg>vegan</pkg> provides helper function <code>metaMDS()</code> for 
        <code>isoMDS()</code>, implementing random starts of the algorithm and standardised scaling of the NMDS results. 
        The approach adopted by <pkg>vegan</pkg> with <code>metaMDS()</code> is the recommended approach for ecological
        data.</li>
      <li>Coinertia analysis is available via <code>coinertia()</code> and <code>mcoa()</code>, both in <pkg>ade4</pkg>.</li>
      <li>Co-correspondence analysis to relate two ecological species data matrices is available in <pkg>cocorresp</pkg>.</li>
      <li>Canonical Correlation Analysis (CCoA - not to be confused with CCA, above) is available in <code>cancor()</code> 
        in standard package stats.</li>
      <li>Procrustes rotation is available in <code>procrustes()</code> in <pkg>vegan</pkg> and <code>procuste()</code> in 
        <pkg>ade4</pkg>, with both <pkg>vegan</pkg> and <pkg>ade4</pkg> providing functions to test the significance of 
        the association between ordination configurations (as assessed by Procrustes rotation) using permutation/randomisation 
        and Monte Carlo methods.</li>
      <li>Constrained Analysis of Principal Coordinates (CAP), implemented in <code>capscale()</code> in <pkg>vegan</pkg>,
        fits constrained ordination models similar to RDA and CCA but with any any dissimilarity coefficient.</li>
      <li>Constrained Quadratic Ordination (CQO; formerly known as Canonical Gaussian Ordination (CGO)) is a maximum likelihood 
        estimation alternative to CCA fit by Quadratic Reduced Rank Vector GLMs. Constrained Additive Ordination (CAO) is a 
        flexible alternative to CQO which uses Quadratic Reduced Rank Vector GAMs. These methods and more are provided in 
        Thomas Yee's <pkg>VGAM</pkg> package.</li>
      <li>Fuzzy set ordination (FSO), an alternative to CCA/RDA and CAP, is available in package <pkg>fso</pkg>. <pkg>fso</pkg>
        complements a recent paper on fuzzy sets in the journal <em>Ecology</em> by Dave Roberts (2008, Statistical analysis of 
        multidimensional fuzzy set ordinations. <em>Ecology</em> <strong>89(5)</strong>, 1246-1260).</li>
      <li>See also the <view>Multivariate</view> task view for complementary information.</li>
    </ul>
    
    <h4>Dissimilarity coefficients</h4>
    <p>Much ecological analysis proceeds from a matrix of dissimilarities between samples. A large amount of effort has
      been expended formulating a wide range of dissimilarity coefficients suitable for ecological data. A selection of
      the more useful coefficients are available in R and various contributed packages.</p>
    <p>Standard functions that produce, square, symmetric matrices of pair-wise dissimilarities include:</p>
    <ul>
      <li><code>dist()</code> in standard package stats</li>
      <li><code>daisy()</code> in recommended package <pkg>cluster</pkg></li>
      <li><code>vegdist()</code> in <pkg>vegan</pkg></li>
      <li><code>dsvdis()</code> in <pkg>labdsv</pkg></li>
      <li><code>Dist()</code> in <pkg>amap</pkg></li>
      <li><code>distance()</code> in <pkg>ecodist</pkg></li>
      <li>a suite of functions in <pkg>ade4</pkg></li>
      <li>Package <pkg>simba</pkg> provides functions for the calculation of similarity and multiple plot similarity
	measures with binary data (for instance presence/absence species data)</li>
    </ul>
    <p>Function <code>distance()</code> in package <pkg>analogue</pkg> can be used to calculate dissimilarity between samples 
      of one matrix and those of a second matrix. The same function can be used to produce pair-wise dissimilarity matrices,
      though the other functions listed above are faster. <code>distance()</code> can also be used to generate
      matrices based on Gower's coefficient for mixed data (mixtures of binary, ordinal/nominal and continuous variables).
      Function <code>daisy()</code> in package <pkg>cluster</pkg> provides a faster implementation of Gower's coefficient for
      mixed-mode data than <code>distance()</code> if a standard dissimilarity matrix is required. Function <code>gowdis()</code>
      in package <pkg>FD</pkg> also computes Gower's coefficient and impliments extensions to ordinal variables.</p>
    
    <h4>Cluster analysis</h4>
    <p>Cluster analysis aims to identify groups of samples within multivariate data sets. A large range of 
      approaches to this problem have been suggested, but the main techniques are hierarchical cluster analysis, 
      partitioning methods, such as <em>k</em>-means, and finite mixture models or model-based clustering. In the machine 
      learning literature, cluster analysis is an unsupervised learning problem.</p>
    <p>The <view>Cluster</view> task view provides a more detailed discussion of available cluster analysis methods and 
      appropriate R functions and packages.</p>
    <p>Hierarchical cluster analysis:</p>
    <ul>
      <li><code>hclust()</code> in standard package stats</li>
      <li>Recommended package <pkg>cluster</pkg> provides functions for cluster analysis following the methods 
        described in Kaufman and Rousseeuw (1990) <em>Finding Groups in data: an introduction to cluster analysis</em>,
        Wiley, New York</li>
      <li><code>hcluster()</code> in <pkg>amap</pkg></li>
      <li><pkg>pvclust</pkg> is a package for assessing the uncertainty in hierarchical cluster analysis. It provides 
        approximately unbiased <em>p</em>-values as well as bootstrap <em>p</em>-values.</li>
    </ul>
    <p>Partitioning methods:</p>
    <ul>
      <li><code>kmeans()</code> in stats provides <em>k</em>-means clustering</li>
      <li><code>cmeans()</code> in <pkg>e1071</pkg> implements a fuzzy version of the <em>k</em>-means algorithm</li>
      <li>Recommended package <pkg>cluster</pkg> also provides functions for various partitioning methodologies.</li>
    </ul>
    <p>Mixture models and model-based cluster analysis:</p>
    <ul>
      <li><pkg>mclust</pkg> and <pkg>flexmix</pkg> provide implementations of model-based cluster analysis.</li>
      <li><pkg>prabclus</pkg> clusters a species presence-absence matrix object by calculating an 
        <acronym title="Multi-dimensional Scaling">MDS</acronym> from the distances, and applying maximum likelihood Gaussian 
        mixtures clustering to the MDS points. The maintainer's, Christian Hennig, web site contains several publications in 
        ecological contexts that use <pkg>prabclus</pkg>, especially Hausdorf &amp; Hennig (2007; 
        <a href="http://www.blackwell-synergy.com/doi/abs/10.1111/j.0030-1299.2007.15661.x">Oikos 116 (2007), 818-828</a>).</li>
    </ul>
    
    <h4>Ecological theory</h4>
    <p>There is a growing number of packages and books that focus on the use of R for theoretical ecological models.</p>
    <ul>
      <li><pkg>vegan</pkg> provides a wide range of functions related to ecological theory, such as diversity indices 
	(including the <q>so-called</q>Hill's numbers [e.g. Hill's N<sup>2</sup>] and rarefaction), ranked abundance diagrams, 
	Fisher's log series, Broken Stick model, Hubbell's abundance model, amongst others.</li>
      <li>The <pkg>vegetarian</pkg> provides the diversity measures suggested by Jost 
	(<a href="http://dx.doi.org/10.1111/j.2006.0030-1299.14714.x">2006, Oikos 113(2), 363-375</a>; 
	<a href="http://dx.doi.org/10.1890/06-1736.1">2007, Ecology 88(10), 2427-2439</a>).</li>
      <li><pkg>untb</pkg> provides a collection of utilities for biodiversity data, including the simulation ecological drift 
	under Hubbell's Unified Neutral Theory of Biodiversity, and the calculation of various diagnostics such as Preston 
	curves.</li>
      <li><pkg>primer</pkg> is a support software for Stevens 
	(<a href="http://www.springer.com/life+sci/ecology/book/978-0-387-89881-0">2009, <em>A Primer of Ecology with R</em>, 
	  Springer</a>). The package provides a variety of functions for modeling ecological data and basic theoretical ecology, 
	including functions related to demographic matrix models, metapopulation and source-sink models, host-parasitoid and 
	disease models, multiple basins of attraction, the storage effect, neutral theory, and diversity partitioning.</li>
      <li>Package <pkg>BiodiversityR</pkg> provides a GUI for biodiversity and community ecology analysis.</li>
      <li>Function <code>betadiver()</code> in <pkg>vegan</pkg> implements all of the diversity indices reviewed in 
	Koleff et al (2003; <a href="http://www.blackwell-synergy.com/doi/abs/10.1046/j.1365-2656.2003.00710.x">Journal of 
	  Animal Ecology 72(3), 367-382</a>).
	<code>betadiver()</code> also provides a <code>plot</code> method to produce the co-occurrence frequency triangle plots 
	of the type found in Koleff et al (2003).</li>
      <li>Function <code>betadisper()</code>, also in <pkg>vegan</pkg>, implements Marti Anderson's distance-based test for 
	homogeneity of multivariate dispersions (PERMDISP, PERMDISP2), a multivariate analogue of Levene's test (Anderson 
	2006; <a href="http://www.blackwell-synergy.com/doi/abs/10.1111/j.1541-0420.2005.00440.x">Biometrics 62, 
	  245-253</a>). Anderson et al (2006; 
	<a href="http://www.blackwell-synergy.com/doi/abs/10.1111/j.1461-0248.2006.00926.x">Ecology Letters 9(6), 683-693</a>) 
	demonstrate the use of this approach for measuring beta diversity.</li>
      <li>The <pkg>FD</pkg> package computes several measures of functional diversity indices from multiple traits.</li>
    </ul>
    
    <h4>Population dynamics</h4>
    <h5>Estimating animal abundance and related parameters</h5>
    <p>This section concerns estimation of population parameters (population size, density, survival probability, site occupancy
      etc.) by methods that allow for incomplete detection. Many of these methods use data on marked animals, variously called 
      'capture-recapture', 'mark-recapture' or 'capture-mark-recapture' data.</p>
    <ul>
      <li><pkg>Rcapture</pkg> fits loglinear models to estimate population size and survival rate from capture-recapture data as 
	described by <a href="http://www.jstatsoft.org/v19/i05">Baillargeon and Rivest (2007)</a>.</li>
      <li><pkg>mra</pkg> estimates survival by fitting the Cormack-Jolly-Seber open population model, using a flexible formula-based
	approach for covariates (see also RMark). <pkg>mra</pkg> also fits Huggin's closed population model and computes Horvitz-Thompson
	estimates of open-population size from CJS models.</li>
      <li><pkg>secr</pkg> estimates population density given spatially explicit capture-recapture data from traps, passive DNA 
	sampling, automatic cameras, sound recorders etc. Models are fitted by maximum likelihood. The detection function may be
	halfnormal, exponential, cumulative gamma etc. Density surfaces may be fitted. Covariates of density and detection parameters are 
	specified via formulae.</li>
      <li><pkg>SPACECAP</pkg> provides a graphical interface for fitting a spatially explicit capture-recapture model to photographic
	'capture' data by the Bayesian method described in Royle et al. (<a href="http://www.esajournals.org/doi/abs/10.1890/08-1481.1">2009,
	  Ecology 90: 3233-3244</a>).</li>
      <li><pkg>DSpat</pkg> provides analyses of line-transect distance sampling data in which the density surface and the detection function 
	are estimated simultaneously (<a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2009.01265.x/abstract">Johnson
	  et al. 2009</a>).</li>
      <li><pkg>unmarked</pkg> fits hierarchical models of occurrence and abundance to data collected on species subject to imperfect detection.
	Examples include single- and multi-season occupancy models, binomial mixture models, and hierarchical distance sampling models. The data
	can arise from survey methods such temporally replicated counts, removal sampling, double-observer sampling, and distance sampling.
	Parameters governing the state and observation processes can be modeled as functions of covariates.</li>
      <li>Package <pkg>RMark</pkg> provides a formula-based R interface for the MARK package which fits a wide variety of capture-recapture
      models. See the <a href="http://www.phidot.org/software/mark/rmark/">RMark website</a> and a <a href="http://www.afsc.noaa.gov/Publications/ProcRpt/PR2013-01.pdf">NOAA report</a> (pdf) for further details.</li>
      <li>Package <pkg>marked</pkg> provides a framework for handling data and analysis for mark-recapture. <pkg>marked</pkg> can fit Cormack-Jolly-Seber (CJS)and Jolly-Seber (JS) models via maximum likelihood and the CJS model via MCMC. Maximum likelihood estimates for the CJS model can be obtained using R or via a link to the Automatic Differentiation Model Builder software. A <a href="http://doi.org/10.1111/2041-210X.12065">description of the package</a> was published in Methods in Ecology and Evolution.</li>
      <li><pkg>mrds</pkg> fits detection functions to point and line transect distance sampling survey data (for both single and double observer surveys). Abundance can be estimated using Horvitz-Thompson-type estimators.</li>
      <li><pkg>Distance</pkg> is a simpler interface to <pkg>mrds</pkg> for single observer distance sampling surveys.</li>
      <li><pkg>dsm</pkg> fits <a href="http://doi.org/10.1111/2041-210X.12105">density surface models</a> to spatially-referenced distance sampling data. Count data are corrected using detection function models fitted using <pkg>mrds</pkg> or <pkg>Distance</pkg>. Spatial models are constructed as in <pkg>mgcv</pkg>.</li>
    </ul>
    <p>Packages <pkg>secr</pkg> and <pkg>DSpat</pkg> can also be used to simulate data from their respective models.</p>
    
    <p>See also the <view>SpatioTemporal</view> task view for analysis of animal tracking data under <em>Moving objects, trajectories</em>.</p>

    <h5>Modelling population growth rates:</h5>
    <ul>
      <li>Package <pkg>popbio</pkg> can be used to construct and analyse age- or stage-specific matrix population models.</li>
    </ul>
    
    <h4>Environmental time series</h4>
    <ul>
      <li>Time series objects in R are created using the <code>ts()</code> function, though see <pkg>tseries</pkg>,
	<pkg>zoo</pkg> and <pkg>its</pkg> below for alternatives.</li>
      <li>Classical time series functionality is provided by the <code>ar()</code>, and <code>arima()</code> functions in 
	standard package stats for autoregressive (AR), moving average (MA), autoregressive moving  average (ARMA) and 
	integrated ARMA (ARIMA) models.</li>
      <li>The <pkg>forecast</pkg> package provides methods and tools for displaying and analysing univariate time series
	forecasts including exponential smoothing via state space models and automatic ARIMA modelling</li>
      <li>The <pkg>dse</pkg> package provide a variety of more advanced estimation methods 
	and multivariate time series analysis.</li>
      <li>Packages <pkg>tseries</pkg> and <pkg>zoo</pkg> provide general handling and analysis of time series data.</li>
      <li>Irregular time series can be handled using packages <pkg>zoo</pkg> and <pkg>its</pkg>, as well as by
	<code>irts()</code> in package <pkg>tseries</pkg>.</li>
      <li><pkg>pastecs</pkg> provides functions specifically tailored for the analysis of space-time ecological series.</li>
      <li><pkg>strucchange</pkg> allows for testing, dating and monitoring of structural change in linear regression 
	relationships.</li>
      <li>Detecting change points in time series data --- see <pkg>segmented</pkg> <a href="#changepoints">above</a>.</li>
      <li>The <pkg>surveillance</pkg> package implements statistical methods for the modeling of and change-point detection
	   in time series of counts, proportions and categorical data. Focus is on outbreak detection in count data time series.</li>
      <li>Package <pkg>dynlm</pkg> provides a convenient interface to fitting time series regressions via ordinary least 
	squares</li>
      <li>Package <pkg>dyn</pkg> provides a different approach to that of <pkg>dynlm</pkg>, which allows time series data to 
	be used with any regression function written in the style of lm such as <code>lm()</code>, <code>glm()</code>, 
	<code>loess()</code>, <code>rlm()</code> and <code>lqs()</code> from <pkg>MASS</pkg>, 
	<code>randomForest()</code> (package <pkg>randomForest</pkg>), <code>rq()</code> (package <pkg>quantreg</pkg>) amongst 
	others, whilst preserving the time series information.</li>
      <li>The <pkg>openair</pkg> provides numerous tools to analyse, interpret and understand air pollution time series data</li>
      <li>The <pkg>bReeze</pkg> package is a collection of widely used methods to analyse, visualise, and interpret wind data. Wind resource analyses can subsequently be combined with characteristics of wind turbines to estimate the potential energy production.</li>
    </ul>
    <p>Additionally, a fuller description of available packages for time series analysis can be found in the <view>TimeSeries</view>
      task view.</p>
    
    <h4>Spatial data analysis</h4>
    <p>See the <view>Spatial</view> CRAN Task View  for an overview of spatial analysis in R.</p>
    
    <a id="extremes"></a>
    <h4>Extreme values</h4>
    <p><pkg>ismev</pkg> provides functions for models for extreme value statistics and is support software for Coles (2001)
      <em>An Introduction to Statistical Modelling of Extreme Values</em>, Springer, New York. Other packages for extreme value
      theory include:</p>
    <ul>
      <li><pkg>evir</pkg></li>
      <li><pkg>evd</pkg></li>
      <li><pkg>evdbayes</pkg>, which provides a Bayesian approach to extreme value theory</li>
      <li><pkg>extRemes</pkg></li>
      <li>The <pkg>SpatialExtremes</pkg> provides several approaches for modelling spatial extreme events.</li>
    </ul>
    
    <h4>Phylogenetics and evolution</h4>
    <p>Packages specifically tailored for the analysis of phylogenetic and evolutionary data include:</p>
    <ul>
      <li><pkg>ape</pkg></li>
      <li><pkg>ouch</pkg></li>
    </ul>
    <p>The <view>Phylogenetics</view> task view provides more detailed coverage of the subject area and related functions
      within R.</p>
    <p>UseRs may also be interested in Paradis (2006) <em>Analysis of Phylogenetics and Evolution with R</em>, Springer, 
      New York, a book in the new UseR series from Springer.</p>
    
    <h4>Soil science</h4>
    <p>Several packages are now available that implement R functions for widely-used methods and approaches in pedology.</p>
    <ul>
      <li><pkg>soiltexture</pkg> provides functions for soil texture plot, classification and transformation.</li>
      <li><pkg>aqp</pkg> contains a collection of algorithms related to modeling of soil resources, soil classification,
	soil profile aggregation, and visualization.</li>
      <li>Package <pkg>HydroMe</pkg> estimates the parameters in infiltration and water retention models by curve-fitting
	method.</li>
      <li>The Soil Water project on r-forge.r-project.net provides packages providing soil water retention functions, 
	soil hydraulic conductivity functions and pedotransfer functions to estimate their parameter from easily available soil
	properties. Two packages form the project:
	<ol>
	  <li><rforge>soilwaterfun</rforge></li>
	  <li><rforge>soilwaterptf</rforge></li>
	</ol>
      </li>
    </ul>
    
    <h4>Hydrology and Oceanography</h4>
    <p>A growing number of packages are available that implement methods specifically related to the fields of hydrology and
      oceanography. Also see the <a href="#extremes">Extreme Value</a> and the <a href="#climatology">Climatology</a> sections
      for related packages.</p>
    <ul>
      <li>Package <pkg>HydroMe</pkg> estimates the parameters in infiltration and water retention models by curve-fitting
	method.</li>
      <li><pkg>hydroTSM</pkg> is a package for management, analysis, interpolation and plotting of time series used in hydrology
	and related environmental sciences.</li>
      <li><pkg>hydroGOF</pkg> is a package implementing both statistical and graphical goodness-of-fit measures between
	observed and simulated values, mainly oriented to be used during the calibration, validation, and application of
	hydrological/environmental models. Related packages are <pkg>tiger</pkg>, which allows temporally resolved groups of
	typical differences (errors) between two time series to be determined and visualized, and <pkg>qualV</pkg> which
	provides quantitative and qualitative criteria to compare models with data and to measure similarity of patterns</li>
      <li><pkg>hydroPSO</pkg> is a model-independent global optimization tool for calibration of environmental and other real-world models that need to be executed from the system console. <pkg>hydroPSO</pkg> implements a state-of-the-art <acronym title="Particle Swarm Optimisation">PSO</acronym> (SPSO-2011 and SPSO-2007 capable), with several fine-tuning options. The package is parallel-capable, to alleviate the computational burden of complex models.</li>
      <li><pkg>EcoHydRology</pkg> provides a flexible foundation for scientists, engineers, and policy makers to base
	teaching exercises as well as for more applied use to model complex eco-hydrological interactions.</li>
      <li><pkg>topmodel</pkg> is a set of hydrological functions including an R implementation of the hydrological model
	TOPMODEL, which is based on the 1995 FORTRAN version by Keith Beven. New functionality is being developed as part of
	the <rforge>RHydro</rforge> package on R-Forge.</li>
      <li><pkg>dynatopmodel</pkg> is a native R implementation and enhancement of the Dynamic TOPMODEL, Beven and Freers' (2001) extension to the semi-distributed hydrological model TOPMODEL (Beven and Kirkby, 1979).</li>
      <li><pkg>wasim</pkg> provides tools for data processing and visualisation of results of the hydrological model WASIM-ETH</li>
      <li>Package <pkg>seacarb</pkg> provides functions for calculating parameters of the seawater carbonate system.</li>
      <li>Stephen Sefick's <pkg>StreamMetabolism</pkg> package contains function for calculating stream metabolism 
      characteristics, such as GPP, NDM, and R, from single station diurnal Oxygen curves.</li>
      <li>Package <pkg>oce</pkg> supports the analysis of Oceanographic data, including ADP measurements, CTD measurements,
      sectional data, sea-level time series, and coastline files.</li>
      <li>The <pkg>nsRFA</pkg> package provides collection of statistical tools for objective (non-supervised) applications
        of the Regional Frequency Analysis methods in hydrology.</li>
      <li>The <pkg>boussinesq</pkg> package is a collection of functions implementing the one-dimensional Boussinesq Equation
      (ground-water).</li>
      <li><pkg>rtop</pkg> is a package for geostatistical interpolation of data with irregular spatial support such as runoff
        related data or data from administrative units.</li>
    </ul>
    
    <a id="climatology"></a>
    <h4>Climatology</h4>
    <p>Several packages related to the field of climatology.</p>
    <ul>
      <li><pkg>seas</pkg> implements a number of functions for analysis and graphics of seasonal data.</li>
      <li><pkg>RMAWGEN</pkg> is set of S3 and S4 functions for spatial multi-site stochastic generation of daily
	time series of temperature and precipitation making use of Vector Autoregressive Models.</li>
      <li><pkg>Interpol.T</pkg> makes hourly interpolation of daily minimum and maximum temperature series for example
	when hourly time series must be downscaled from the daily information.</li>
    </ul>

    <h4>Palaeoecology and stratigraphic data</h4>
    <p>Several packages now provide speciailist functionality for the import, analysis, and plotting of
    palaeoecological data.</p>
    <ul>
      <li>Transfer function models including weighted averaging (WA), modern analogue technique (MAT), Locally-weighted WA, &amp;
      maximum likelihood (aka Gaussian logistic) regression (GLR) are provided by some or all of the <pkg>rioja</pkg>,
      and <pkg>analogue</pkg> packages.</li>
      <li>Import of common, legacy, palaeodata formats are provided by package <pkg>vegan</pkg> (cornell format) and <pkg>rioja</pkg>
      (cornell and Tilia format). In addition, <pkg>rioja</pkg> also allows for import of C2 model files.</li>
      <li>Stratigraphic data plots can be drawn using <code>Stratiplot()</code> function in <pkg>analogue</pkg> and functions
      <code>strat.plot()</code> and <code>strat.plot.simple</code> in the <pkg>rioja</pkg> package.</li>
      <li><pkg>analogue</pkg> provides extensive support for developing and interpretting MAT transfer function models,
      including ROC curve analysis. Summary of stratigraphic data is supported via principal curves in the <code>prcurve()</code>
      function.</li>
      <li>Constrained clustering of stratigraphic data is provided by function <code>chclust()</code> in the form of constrained
      hierarchical clustering in <pkg>rioja</pkg>.</li>
    </ul>
    
    <h4>Other packages</h4>
    <p>Several other relevant contributed packages for R are available that do not fit under nice headings.</p>
    <ul>
      <li><pkg>adehabitat</pkg> complements <pkg>ade4</pkg> and provides a collection of tools for the analysis of habitat 
      selection by animals.</li>
      <li><pkg>diveMove</pkg> provides tools to represent, visualize, filter, analyse, and summarize time-depth recorder (TDR) data
      for research on animal diving and movement behaviour.</li>
      <li><pkg>latticeDensity</pkg> implements methods for density estimation and nonparametric regression on irregular regions.
      A useful alternative to kernel density estimation for e.g. estimating animal densities and home ranges in regions with
      irregular boundaries or holes.</li>
      <li>Andrew Robinson's <pkg>equivalence</pkg> package provides some statistical tests and graphics for assessing 
      tests of equivalence. Such tests have similarity as the alternative hypothesis instead of the null. The package 
      contains functions to perform two one-sided t-tests (TOST) and paired t-tests of equivalence.</li>
      <li>Thomas Petzoldt's <pkg>simecol</pkg> package provides an object oriented framework and tools to simulate 
      ecological (and other) dynamic systems within R. See the <a href="http://www.simecol.de/">simecol website</a> and a
      <a href="http://CRAN.R-project.org/doc/Rnews">R News</a> 
      <a href="http://CRAN.R-project.org/doc/Rnews/Rnews_2003-3.pdf">article</a> on the package for further information.</li>
      <li>Functions for circular statistics are found in <pkg>CircStats</pkg> and <pkg>circular</pkg>.</li>
      <li>Package <pkg>eco</pkg> fits Bayesian models of ecological inference to 2 x 2 contingency tables.</li>
      <li>Package <pkg>e1071</pkg> provides functions for latent class analysis, short time Fourier transform, fuzzy clustering, 
      support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, and more...</li>
      <li>Package <pkg>pgirmess</pkg> provides a suite of miscellaneous functions for data analysis in ecology.</li>
      <li><pkg>mefa</pkg> provides functions for handling and reporting on multivariate count data in ecology and 
      biogeography.</li>
      <li>Sensitivity analysis of models is provided by packages <pkg>sensitivity</pkg> and <pkg>fast</pkg>.
      <pkg>sensitivity</pkg> contains a collection of functions for factor screening and global sensitivity analysis of model
      output. <pkg>fast</pkg> is an implementation of the Fourier Amplitude Sensitivity Test (FAST), a method to determine
      global sensitivities of a model on parameter changes with relatively few model runs.</li>
      <li>Functions to analyze coherence, boundary clumping, and turnover following the pattern-based metacommunity analysis of
      <a href="http://dx.doi.org/10.1034/j.1600-0706.2002.970210.x">Leibold and Mikkelson (2002)</a> are provided in the
      <pkg>metacom</pkg> package.</li>
      <li>Growth curve estimation via noncrossing and nonparametric regression quantiles is implemented in package
        <pkg>quantregGrowth</pkg>. A supporting paper is <a href="http://dx.doi.org/10.1007/s10651-012-0232-1">Muggeo et al.
        (2013)</a>.
      </li>
      <li>The <pkg>siplab</pkg> package provides an R platform for experimenting with spatially explicit individual-based vegetation models. A supporting paper is <a href="http://www.mcfns.com/index.php/Journal/article/view/6_36">García, O. (2014)</a>.</li>
    </ul>
  </info>
  <packagelist>
    <pkg priority="core">ade4</pkg>
    <pkg>adehabitat</pkg>
    <pkg>amap</pkg>
    <pkg>analogue</pkg>
    <pkg>aod</pkg>
    <pkg>ape</pkg>
    <pkg>aqp</pkg>
    <pkg>BiodiversityR</pkg>
    <pkg>boussinesq</pkg>
    <pkg>bReeze</pkg>
    <pkg>CircStats</pkg>
    <pkg>circular</pkg>
    <pkg priority="core">cluster</pkg>
    <pkg>cocorresp</pkg>
    <pkg>Distance</pkg>
    <pkg>diveMove</pkg>
    <pkg>dse</pkg>
    <pkg>dsm</pkg>
    <pkg>DSpat</pkg>
    <pkg>dyn</pkg>
    <pkg>dynatopmodel</pkg>
    <pkg>dynlm</pkg>
    <pkg>EcoHydRology</pkg>
    <pkg>EnvStats</pkg>
    <pkg>e1071</pkg>
    <pkg>earth</pkg>
    <pkg>eco</pkg>
    <pkg>ecodist</pkg>
    <pkg>equivalence</pkg>
    <pkg>evd</pkg>
    <pkg>evdbayes</pkg>
    <pkg>evir</pkg>
    <pkg>extRemes</pkg>
    <pkg>fast</pkg>
    <pkg>FD</pkg>
    <pkg>flexmix</pkg>
    <pkg>forecast</pkg>
    <pkg>fso</pkg>
    <pkg>gam</pkg>
    <pkg>gamair</pkg>
    <pkg>hydroGOF</pkg>
    <pkg>HydroMe</pkg>
    <pkg>hydroPSO</pkg>
    <pkg>hydroTSM</pkg>
    <pkg>Interpol.T</pkg>
    <pkg>ipred</pkg>
    <pkg>ismev</pkg>
    <pkg>its</pkg>
    <pkg priority="core">labdsv</pkg>
    <pkg>latticeDensity</pkg>
    <pkg>lme4</pkg>
    <pkg>maptree</pkg>
    <pkg>marked</pkg>
    <pkg priority="core">MASS</pkg>
    <pkg>mclust</pkg>
    <pkg>mda</pkg>
    <pkg>mefa</pkg>
    <pkg>metacom</pkg>
    <pkg priority="core">mgcv</pkg>
    <pkg>mra</pkg>
    <pkg>mrds</pkg>
    <pkg>nlme</pkg>
    <pkg>nsRFA</pkg>
    <pkg>oce</pkg>
    <pkg>openair</pkg>
    <pkg>ouch</pkg>
    <pkg>party</pkg>
    <pkg>pastecs</pkg>
    <pkg>pgirmess</pkg>
    <pkg>popbio</pkg>
    <pkg>pscl</pkg>
    <pkg>prabclus</pkg>
    <pkg>primer</pkg>
    <pkg>pvclust</pkg>
    <pkg>qualV</pkg>
    <pkg>quantreg</pkg>
    <pkg>quantregGrowth</pkg>
    <pkg>randomForest</pkg>
    <pkg>Rcapture</pkg>
    <pkg>rioja</pkg>
    <pkg>rpart</pkg>
    <pkg>rtop</pkg>
    <pkg>RMark</pkg>
    <pkg>RMAWGEN</pkg>
    <pkg>seacarb</pkg>
    <pkg>seas</pkg>
    <pkg>secr</pkg>
    <pkg>segmented</pkg>
    <pkg>sensitivity</pkg>
    <pkg>simba</pkg>
    <pkg>simecol</pkg>
    <pkg>siplab</pkg>
    <pkg>soiltexture</pkg>
    <pkg>SPACECAP</pkg>
    <pkg>SpatialExtremes</pkg>
    <pkg>StreamMetabolism</pkg>
    <pkg>strucchange</pkg>
    <pkg>surveillance</pkg>
    <pkg>tiger</pkg>
    <pkg>topmodel</pkg>
    <pkg>tseries</pkg>
    <pkg>unmarked</pkg>
    <pkg>untb</pkg>
    <pkg priority="core">vegan</pkg>
    <pkg>vegetarian</pkg>
    <pkg>VGAM</pkg>
    <pkg>wasim</pkg>
    <pkg>zoo</pkg>
  </packagelist>
  <links>
    <view>Spatial</view>
    <view>Multivariate</view>
    <view>Cluster</view>
    <view>Phylogenetics</view>
    <rforge>soilwaterfun</rforge>
    <rforge>soilwaterptf</rforge>
    <rforge>RHydro</rforge>
    <a href="http://vegan.r-forge.r-project.org/">The vegan development site on R-Forge</a>
    <a href="http://ecology.msu.montana.edu/labdsv/R/labs/">Dave Roberts' LabDSV pages</a>
    <a href="http://www.stat.auckland.ac.nz/~yee/VGAM/">Thomas Yee's VGAM package for R</a>
    <a href="http://cocorresp.r-forge.r-project.org/">The cocorresp development site on R-Forge</a>
    <a href="http://analogue.r-forge.r-project.org/">The analogue development site on R-Forge</a>        
    <a href="http://www.brodgar.com/">Brodgar</a>
    <a href="http://pbil.univ-lyon1.fr/ADE-4/">More information on the ade4 package can be found on the ADE4 website</a>
    <a href="http://ordination.okstate.edu/">Mike Palmer's Ordination web site</a>
    <a href="http://tu-dresden.de/Members/thomas.petzoldt/downloads">Thomas Petzoldt's page about ecological modelling with R</a>
    <a href="http://www.flr-project.org/">The FLR project web page for Fisheries Science in R.</a>
  </links>
</CRANTaskView>
