The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

CRAN Task View: Analysis of Ecological and Environmental Data

Introduction

This Task View contains information about using R to analyse ecological and environmental data.

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 & 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 special volume of the Journal of Statistical Software was produced in 2007.

Those interested in environmetrics should consult the Spatial view. Complementary information is also available in the Cluster, and SpatioTemporal task views.

If you have any comments or suggestions for additions or improvements, then please contact the maintainer or submit an issue or pull request in the GitHub repository linked above.

A list of available packages and functions is presented below, grouped by analysis type.

General packages

These packages are general, having wide applicability to the environmetrics field.

• Package EnvStats is the successor to the S-PLUS module EnvironmentalStats, both by Steven Millard. A user guide in the form of a book has been released.

Modelling species responses and other data

Analysing species response curves or modelling other data often involves the fitting of standard statistical models to ecological data and includes simple (multiple) regression, Generalized Linear Models (GLM), extended regression (e.g. Generalized Least Squares [GLS]), Generalized Additive Models (GAM), and mixed effects models, amongst others.

• The base installation of R provides lm() and glm() for fitting linear and generalized linear models, respectively.
• Generalized 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 nlme provides functions for fitting these models. The package is supported by Pinheiro & Bates (2000) Mixed-effects Models in S and S-PLUS, Springer, New York. An updated approach to mixed effects models, which also fits Generalized Linear Mixed Models (GLMM) and Generalized non-Linear Mixed Models (GNLMM) is provided by the lme4 package, though this is currently beta software and does not yet allow correlations within the error structure.
• Recommended package mgcv fits GAMs and Generalized Additive Mixed Models (GAMM) with automatic smoothness selection via generalized cross-validation. The author of mgcv has also written a companion monograph, Wood (2017) Generalized Additive Models; An Introduction with R Second Edition. Chapman Hall/CRC, which has an accompanying package gamair.
• Alternatively, package gam provides an implementation of the S-PLUS function gam() that includes LOESS smooths.
• Proportional odds models for ordinal responses can be fitted using polr() in the MASS package, of Bill Venables and Brian Ripley.
• A negative binomial family for GLMs to model over-dispersion in count data is available in MASS.
• Models for overdispersed counts and proportions
  • Package pscl 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.
  • aod provides a suite of functions to analyse overdispersed counts or proportions, plus utility functions to calculate e.g. AIC, AICc, Akaike weights.
• Detecting change points and structural changes in parametric models is well catered for in the segmented package and the strucchange package respectively. segmented is discussed in an R News article

Tree-based models

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.

Univariate trees

Tree-structured models for regression, classification and survival analysis, following the ideas in the CART book, are implemented in

• recommended package rpart
• party provides an implementation of conditional inference trees which embed tree-structured regression models into a well-defined theory of conditional inference procedures

Multivariate trees

Multivariate trees are available in

• package party can also handle multivariate responses.

Ensembles of trees

Ensemble techniques for trees:

• The Random Forest method of Breiman and Cutler is implemented in randomForest, providing classification and regression based on a forest of trees using random inputs
• Package ipred provides functions for improved predictive models for classification, regression and survival problems.

Graphical tools for the visualization of trees are available in package maptree.

Packages mda and earth 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.

Ordination

R and add-on packages provide a wide range of ordination methods, many of which are specialized techniques particularly suited to the analysis of species data. The two main packages are ade4 and vegan. ade4 derives from the traditions of the French school of “Analyse des Donnees” and is based on the use of the duality diagram. vegan follows the approach of Mark Hill, Cajo ter Braak and others, though the implementation owes more to that presented in Legendre & Legendre (1988) Numerical Ecology, 2^nd English Edition, Elsevier. Where the two packages provide duplicate functionality, the user should choose whichever framework that best suits their background.

• Principal Components (PCA) is available via the prcomp() function. rda() (in package vegan), pca() (in package labdsv) and dudi.pca() (in package ade4), provide more ecologically-orientated implementations.
• Redundancy Analysis (RDA) is available via rda() in vegan and pcaiv() in ade4.
• Canonical Correspondence Analysis (CCA) is implemented in cca() in both vegan and ade4.
• Detrended Correspondence Analysis (DCA) is implemented in decorana() in vegan.
• Principal coordinates analysis (PCO) is implemented in dudi.pco() in ade4, pco() in labdsv, pco() in ecodist, and cmdscale() in package MASS.
• Non-Metric multi-Dimensional Scaling (NMDS) is provided by isoMDS() in package MASS and nmds() in ecodist. nmds(), a wrapper function for isoMDS(), is also provided by package labdsv. vegan provides helper function metaMDS() for isoMDS(), implementing random starts of the algorithm and standardized scaling of the NMDS results. The approach adopted by vegan with metaMDS() is the recommended approach for ecological data.
• Coinertia analysis is available via coinertia() and mcoa(), both in ade4.
• Co-correspondence analysis to relate two ecological species data matrices is available in cocorresp.
• Canonical Correlation Analysis (CCoA - not to be confused with CCA, above) is available in cancor() in standard package stats.
• Procrustes rotation is available in procrustes() in vegan and procuste() in ade4, with both vegan and ade4 providing functions to test the significance of the association between ordination configurations (as assessed by Procrustes rotation) using permutation/randomization and Monte Carlo methods.
• Constrained Analysis of Principal Coordinates (CAP), implemented in capscale() in vegan, fits constrained ordination models similar to RDA and CCA but with any dissimilarity coefficient.
• Fuzzy set ordination (FSO), an alternative to CCA/RDA and CAP, is available in package fso. fso complements a recent paper on fuzzy sets in the journal Ecology by Dave Roberts (2008, Statistical analysis of multidimensional fuzzy set ordinations. Ecology 89(5), 1246-1260).

Model-based multivariate analysis

Multivariate model-based methods follow typical statistical modeling principles, but for multivariate responses. Model-based ordination methods reduce dimensionality of a model component (usually predictor effects of a random-effect covariance matrix), so that they share features with both ordination methods (the ordination) and regression (e.g., information criteria and residual diagnostics). It thus requires specifying a response distribution, and link function, instead of a dissimilarity measure. Unlike “classical” ordination methods, it is usually required to specify the number of ordination axes a priori of fitting the model. The following packages have different features and functionalities, but most support creating ordinations.

• VGAM package implements ordination based on fixed effects. Ordination plots are constructed with the biplot() or lvplot() functions. Implemented ordination methods include,
  • Unconstrained ordination with the rcim() or grc() functions. grc() implements Goodman’s RC association model, and rcim() generically fits row-column interaction models.
  • Constrained ordination with linear responses using the rrvglm() function.
  • Constrained Quadratic Ordination (CQO; formerly known as Canonical Gaussian Ordination (CGO)), which is a maximum likelihood estimation alternative to Canonical Correspondence Analysis (CCA) fit by Quadratic Reduced Rank Vector GLMs, with the cqo() function.
  • Constrained Additive Ordination (CAO), an extension of CQO to flexible response curves, with the cao() function.
• mvabund does not perform ordination, but fits multivariate models following GLM principles, potentially with a residual correlation structure for species. This is implemented with the manylm(), manyglm(), traitglm(), and manyany(), functions. The coefplot() function plots species responses to predictors with confidence intervals, and hypothesis testing based on resampling strategies is available via the anova() function.
• boral, which stands for “Bayesian Ordination and Regression AnaLysis” fits Joint Species Distribution Models (JSDMs) with a latent variable formulation for a variety of responses in a Bayesian framework where the model is fitted through R2jags. The package supports residual and unconstrained ordinations, potentially with spatially structured latent variables, and stacked models (i.e., GLMs), with site random effects. It is possible to fit models with species observations in different types, e.g., to combine binary and count data in a single model. With functional traits, boral fits a fourth-corner model. Boral allows to perform automatic variable selection via stochastic search variable selection, i.e., by using a spike-and-slab prior. Since boral fits the models with only one MCMC chain, it can be difficult to assess convergence of the model, though the Geweke diagnostic statistic is available to help with that. The ordination can be visualized with the lvsplot() function.
• Hmsc fits Joint Species Distribution Models (JSDMs) with a latent variable formulation for normal responses, binary responses, and counts in a Bayesian framework. “HMSC” stands for “Hierarchical modeling of species communities”, and consequently the package allows to fit hierarchical models for multivariate responses. Its main function is Hmsc(), latent variables can be visualized with the biPlot() function. It has many different tools, including the option to separately formulate models for different sampling levels, to include spatial effects, to include additional random effects, to phylogenetically structure species responses to environmental predictors, or to hierarchically model species associations with predictors. HMSC determines the number of latent variables from the data, which thus do not need to be specified.
• gllvm fits latent variable models for ordination and JSDM purposes to a range of responses, with random-effects, in a relatively fast manner using TMB. Its main function gllvm() allows to fit unconstrained, constrained, and concurrent ordinations. Unlike in VGAM, unconstrained ordination is based on a random effects formulation. Constrained ordination is supported both as fixed and random effects formulation. Concurrent ordination by definition always includes random effects, but is also supported as a fully random effects specification with random slopes. Fourth-corner models with latent variables and random slopes are also supported. The ordination can be visualized with the ordiplot() function, which also allows visualization of statistical uncertainty of the site scores.
• ecoCopula with main functions cgr() and cord() uses a Gaussian copula approach to fit multivariate models. Both functions first require fitting a secondary model, from which residuals are extracted to which the package fits its method. cgr() fits a graphical model, with the purpose of visualizing pairwise associations of species. The resulting network graph can be visualized using the plot() method. The cord() function fits a model-based ordination with Gaussian copulas, which can be visualized using plot(, biplot = TRUE). No estimates of statistical uncertainties are available.
• glmmTMB generically fits random-effects models using TMB, and can thus fit model-based unconstrained ordination with additional random-effects. Its main function is glmmTMB(), and model-based ordination is fitted using the rr() covariance structure in the model. There is no function to visualize the ordination at present.
• sjSDM fits JSDMs to gaussian, Bernoulli, or Poisson responses, not with a latent variable formulation but with an elastic net penalty approach. As such, it not a model-based ordination method. It allows to include spatial effects, and few other extensions. It allows computation with both CPU and GPU resources, which makes for a very fast fitting method that scales well for large datasets.
• gjam fits generalized joint attribute models in a Bayesian framework to a variety of response types. The package can post-hoc perform ordination with PCA or NMDS, and perform dimension reduction by setting parameters for the covariance matrix in gjam(), but does not have the ability to fit a model-based ordination. It is possible fit trait response models, and to include a measure of sampling effort in the models. It is possible to fit models with species observations in different types, and to include some additional random effect to account for clustering of observations. Output can be plotted with gjamPlot() or an ordination with gjamOrdination().
• spOccupancy fits occupancy models for single- and multi-species responses, and can be used to fit JSDMs that account for imperfect detection.
  • mvgam fits Bayesian State-Space Generalized Additive Models for many data types including counts, binary observations, proportions, non-negative real values and unbounded real values. mvgam can fit a wide range of hierarchical ecological models including N-mixture models, Latent Variable-based Joint Species Distribution Models, and multivariate time series models with imperfect detection.

Dissimilarity coefficients

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.

Standard functions that produce, square, symmetric matrices of pair-wise dissimilarities include:

• dist() in standard package stats
• daisy() in recommended package cluster
• vegdist() in vegan
• dsvdis() in labdsv
• Dist() in amap
• distance() in ecodist
• a suite of functions in ade4

Function distance() in package analogue 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. distance() can also be used to generate matrices based on Gower’s coefficient for mixed data (mixtures of binary, ordinal/nominal and continuous variables). Function daisy() in package cluster provides a faster implementation of Gower’s coefficient for mixed-mode data than distance() if a standard dissimilarity matrix is required. Function gowdis() in package FD also computes Gower’s coefficient and implements extensions to ordinal variables.

Cluster analysis

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 k -means, and finite mixture models or model-based clustering. In the machine learning literature, cluster analysis is an unsupervised learning problem.

The Cluster task view provides a more detailed discussion of available cluster analysis methods and appropriate R functions and packages.

Hierarchical cluster analysis:

• hclust() in standard package stats
• Recommended package cluster provides functions for cluster analysis following the methods described in Kaufman and Rousseeuw (1990) Finding Groups in data: an introduction to cluster analysis, Wiley, New York
• hcluster() in amap
• pvclust is a package for assessing the uncertainty in hierarchical cluster analysis. It provides approximately unbiased p -values as well as bootstrap p -values.

Partitioning methods:

• kmeans() in stats provides k -means clustering
• cmeans() in e1071 implements a fuzzy version of the k -means algorithm
• Recommended package cluster also provides functions for various partitioning methodologies.

Mixture models and model-based cluster analysis:

• mclust and flexmix provide implementations of model-based cluster analysis.
• prabclus clusters a species presence-absence matrix object by calculating an MDS from the distances, and applying maximum likelihood Gaussian mixtures clustering to the MDS points. The maintainer’s, Christian Hennig, website contains several publications in ecological contexts that use prabclus, especially Hausdorf & Hennig (2007; Oikos 116 (2007), 818-828).

Ecological theory

There is a growing number of packages and books that focus on the use of R for theoretical ecological models.

• vegan provides a wide range of functions related to ecological theory, such as diversity indices (including the “so-called” Hill’s numbers [e.g. Hill’s N ² ] and rarefaction), ranked abundance diagrams, Fisher’s log series, Broken Stick model, Hubbell’s abundance model, amongst others.
• untb 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.
• Package BiodiversityR provides a GUI for biodiversity and community ecology analysis.
• Function betadiver() in vegan implements all the diversity indices reviewed in Koleff et al (2003; Journal of Animal Ecology 72(3), 367-382 ). betadiver() also provides a plot method to produce the co-occurrence frequency triangle plots of the type found in Koleff et al (2003).
• Function betadisper(), also in vegan, implements Marti Anderson’s distance-based test for homogeneity of multivariate dispersions (PERMDISP, PERMDISP2), a multivariate analogue of Levene’s test (Anderson 2006; Biometrics 62, 245-253 ). Anderson et al (2006; Ecology Letters 9(6), 683-693 ) demonstrate the use of this approach for measuring beta diversity.
• The FD package computes several measures of functional diversity indices from multiple traits.

Population dynamics

Estimating animal abundance and related parameters

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.

• Rcapture fits loglinear models to estimate population size and survival rate from capture-recapture data as described by Baillargeon and Rivest (2007).
• secr 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 half-normal, exponential, cumulative gamma etc. Density surfaces may be fitted. Covariates of density and detection parameters are specified via formulae.
• unmarked 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 modelled as functions of covariates.
• Package RMark provides a formula-based R interface for the MARK package which fits a wide variety of capture-recapture models. See the RMark website and a NOAA report (PDF) for further details.
• Package marked provides a framework for handling data and analysis for mark-recapture. marked 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 description of the package was published in Methods in Ecology and Evolution.
• mrds 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.
• Distance is a simpler interface to mrds for single observer distance sampling surveys.
• dsm fits density surface models to spatially-referenced distance sampling data. Count data are corrected using detection function models fitted using mrds or Distance. Spatial models are constructed as in mgcv.
• singleRcapture provides methods for estimating the population size of hard-to-reach populations using single-source capture-recapture methods. It implements zero-truncated, zero-one-truncated, zero-truncated one-inflated, and one-inflated zero truncated count regression models, as well as the Chao and Zelterman models. It provides fit assessment functions, diagnostic plots and four methods for estimating the variance of the population size.

Packages secr can also be used to simulate data from the respective models.

See also the SpatioTemporal task view for analysis of animal tracking data under Moving objects, trajectories.

Modelling population growth rates:

• Package popbio can be used to construct and analyse age- or stage-specific matrix population models.

• Package biogrowth implements functions for modelling the growth of populations. It includes functions for model fitting and prediction under either constant or dynamic environmental conditions. It also includes functions for model comparison/selection.

Environmental time series

• Time series objects in R are created using the ts() function, though see tseries or zoo below for alternatives.
• Classical time series functionality is provided by the ar(), and arima() functions in standard package stats for autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) and integrated ARMA (ARIMA) models.
• The forecast package provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling
• The dse (archived) package provide a variety of more advanced estimation methods and multivariate time series analysis.
• Packages tseries and zoo provide general handling and analysis of time series data.
• Irregular time series can be handled using package zoo as well as by irts() in package tseries.
• pastecs provides functions specifically tailored for the analysis of space-time ecological series.
• strucchange allows for testing, dating and monitoring of structural change in linear regression relationships.
• Detecting change points in time series data — see segmented above.
• The surveillance package implements statistical methods for the modelling of and change-point detection in time series of counts, proportions and categorical data. Focus is on outbreak detection in count data time series.
• Package dynlm provides a convenient interface to fitting time series regressions via ordinary least squares
• Package dyn provides a different approach to that of dynlm, which allows time series data to be used with any regression function written in the style of lm such as lm(), glm(), loess(), rlm() and lqs() from MASS, randomForest() (package randomForest), rq() (package quantreg) amongst others, whilst preserving the time series information.
• The openair provides numerous tools to analyse, interpret and understand air pollution time series data
• The bReeze package is a collection of widely used methods to analyse, visualize, and interpret wind data. Wind resource analyses can subsequently be combined with characteristics of wind turbines to estimate the potential energy production.
• The Rbeast package provides a Bayesian model averaging method to decompose time series into abrupt changes, trend, and seasonality and can be used for changepoint detection, time series decomposition, and nonlinear trend analysis.

Additionally, a fuller description of available packages for time series analysis can be found in the TimeSeries task view.

Spatial data analysis

See the Spatial CRAN Task View for an overview of spatial analysis in R.

Extreme values

ismev provides functions for models for extreme value statistics and is support software for Coles (2001) An Introduction to Statistical Modelling of Extreme Values , Springer, New York. Other packages for extreme value theory include

• evir
• evd
• evdbayes, which provides a Bayesian approach to extreme value theory
• extRemes

See also the ExtremeValue task view for further information.

Phylogenetics and evolution

Packages specifically tailored for the analysis of phylogenetic and evolutionary data include:

• ape
• ouch

UseRs may also be interested in Paradis (2006) Analysis of Phylogenetics and Evolution with R, Springer, New York, a book in the “Use R!” book series from Springer.

Soil science

Several packages are now available that implement R functions for widely-used methods and approaches in pedology.

• soiltexture provides functions for soil texture plot, classification and transformation.
• aqp contains a collection of algorithms related to modelling of soil resources, soil classification, soil profile aggregation, and visualization.
• The Soil Water project on R-Forge.R-project.org 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: soilwaterfun and soilwaterptf.

Hydrology and Oceanography

A growing number of packages are available that implement methods specifically related to the fields of hydrology and oceanography. Also see the Extreme Value and the Climatology sections for related packages.

• topmodel (archived) 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 RHydro package on R-Forge.
• Package seacarb provides functions for calculating parameters of the seawater carbonate system.
• Stephen Sefick’s StreamMetabolism package contains function for calculating stream metabolism characteristics, such as GPP, NDM, and R, from single station diurnal Oxygen curves.
• Package oce supports the analysis of Oceanographic data, including ADP measurements, CTD measurements, sectional data, sea-level time series, and coastline files.
• The nsRFA package provides collection of statistical tools for objective (non-supervised) applications of the Regional Frequency Analysis methods in hydrology.
• The boussinesq package is a collection of functions implementing the one-dimensional Boussinesq Equation (ground-water).
• rtop is a package for geostatistical interpolation of data with irregular spatial support such as runoff related data or data from administrative units.
• A related package is qualV which provides quantitative and qualitative criteria to compare models with data and to measure similarity of patterns

Climatology

Several packages related to the field of climatology.

• seas implements a number of functions for analysis and graphics of seasonal data.
• RMAWGEN 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.

Palaeoecology and stratigraphic data

Several packages now provide specialist functionality for the import, analysis, and plotting of palaeoecological data.

• Transfer function models including weighted averaging (WA), modern analogue technique (MAT), Locally-weighted WA, & maximum likelihood (aka Gaussian logistic) regression (GLR) are provided by the rioja and analogue packages.
• Import of common, legacy, palaeo-data formats is provided by package vegan (Cornell format).
• Stratigraphic data plots can be drawn using Stratiplot() function in analogue and functions strat.plot() and strat.plot.simple in the rioja package. Also see the tidypaleo package, which provides tools to produce stratigraphic plots using ggplot(). A blog post by the maintainer of the tidypaleo package, Dewey Dunnington, shows how to use the package to create stratigraphic plots.
• analogue provides extensive support for developing and interpreting MAT transfer function models, including ROC curve analysis. Summary of stratigraphic data is supported via principal curves in the prcurve() function.

Other packages

Several other relevant contributed packages for R are available that do not fit under nice headings.

• Andrew Robinson’s equivalence 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.
• Thomas Petzoldt’s simecol package provides an object oriented framework and tools to simulate ecological (and other) dynamic systems within R. See the simecol website and a R News article on the package for further information.
• Functions for circular statistics are found in CircStats and circular.
• Package e1071 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…
• Package pgirmess provides a suite of miscellaneous functions for data analysis in ecology.
• mefa provides functions for handling and reporting on multivariate count data in ecology and biogeography.
• Sensitivity analysis of models is provided by package sensitivity. sensitivity contains a collection of functions for factor screening and global sensitivity analysis of model output.
• Functions to analyse coherence, boundary clumping, and turnover following the pattern-based metacommunity analysis of Leibold and Mikkelson (2002) are provided in the metacom package.
• Growth curve estimation via noncrossing and nonparametric regression quantiles is implemented in package quantregGrowth. A supporting paper is Muggeo et al. (2013).
• The siplab package provides an R platform for experimenting with spatially explicit individual-based vegetation models. A supporting paper is García, O. (2014).
• PMCMRplus provides parametric and non-parametric many-to-one and all-pairs multiple comparison procedures for continuous or at least interval based variables. The package provides implementations of a wide range of tests involving pairwise multiple comparisons.

CRAN packages

Related links

• The vegan development site on GitHub
• Thomas Yee’s VGAM package for R
• The cocorresp development site on GitHub
• Brodgar
• More information on the ade4 package can be found on the ADE4 website
• Mike Palmer’s Ordination website
• Thomas Petzoldt’s page about ecological modelling with R
• The FLR project web page for Fisheries Library in R.
• The gllvm website with vignettes

Other resources

• CRAN Task View: Cluster
• CRAN Task View: ExtremeValue
• CRAN Task View: Spatial
• CRAN Task View: SpatioTemporal
• CRAN Task View: TimeSeries
• R-Forge Project: RHydro
• R-Forge Project: soilwaterfun
• R-Forge Project: soilwaterptf
• GitHub Project: tidypaleo

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.