Package: Hmsc 3.4-1

Otso Ovaskainen

Hmsc: Hierarchical Model of Species Communities

Hierarchical Modelling of Species Communities (HMSC) is a model-based approach for analyzing community ecological data. This package implements it in the Bayesian framework with Gibbs Markov chain Monte Carlo (MCMC) sampling (Tikhonov et al. (2020) <doi:10.1111/2041-210X.13345>).

Authors:Gleb Tikhonov [aut], Otso Ovaskainen [aut, cre], Jari Oksanen [aut], Melinda de Jonge [aut], Oystein Opedal [aut], Tad Dallas [aut]

Hmsc_3.4-1.tar.gz
Hmsc_3.4-1.zip(r-4.7)Hmsc_3.4-1.zip(r-4.6)Hmsc_3.4-1.zip(r-4.5)
Hmsc_3.4-1.tgz(r-4.6-any)Hmsc_3.4-1.tgz(r-4.5-any)
Hmsc_3.4-1.tar.gz(r-4.7-any)Hmsc_3.4-1.tar.gz(r-4.6-any)
Hmsc_3.4-1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
Hmsc/json (API)

# Install 'Hmsc' in R:
install.packages('Hmsc', repos = c('https://hmsc-r.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/hmsc-r/hmsc/issues

Datasets:
  • TD - Simulated data and a fitted Hmsc model for a small species community.

On CRAN:

Conda:

10.77 score 119 stars 778 scripts 1.8k downloads 4 mentions 36 exports 45 dependencies

Last updated from:7dd2926f87. Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR184
source / vignettesOK233
linux-release-x86_64ERROR177
macos-release-arm64ERROR156
macos-oldrel-arm64ERROR195
windows-develERROR136
windows-releaseERROR150
windows-oldrelERROR145
wasm-releaseOK141

Exports:alignPosteriorbiPlotcomputeAssociationscomputeDataParameterscomputePredictedValuescomputeSAIRcomputeVariancePartitioningcomputeWAICconstructGradientconstructKnotsconvertToCodaObjectcoralCombinecoralGetRareSpeciesPriorscoralGetXDataExtcoralPlotBetacoralPredictcoralPreprocesscoralSplitToBatchescoralTraincreatePartitionevaluateModelFitgetPostEstimateHmscHmscRandomLevelimportPosteriorFromHPCpcomputePredictedValuesplotBetaplotGammaplotGradientplotVariancePartitioningpoolMcmcChainspredictLatentFactorprepareGradientsampleMcmcsetPriorstaxToPhylo

Dependencies:abindapeBayesLogitclicodacpp11digestdotCall64farverfieldsFNNggplot2gluegtableisobandlabelinglatticelifecyclemapsMASSMatrixMatrixModelsmatrixStatsmcmcMCMCpacknlmennetpracmapROCquantregR6RColorBrewerRcpprlangS7scalesspspamSparseMstatmodsurvivaltruncnormvctrsviridisLitewithr

Testing the performance of Hmsc with simulated data
Introduction | Generating simulated data | Model setup and fitting | Evaluating model performance | References
Rendered fromvignette_5_performance.pdf.asisusingR.rsp::asis

Last update: 2019-12-17
Started: 2019-12-17

Getting started with HMSC-R: spatial models
Introduction | Generating simulated data | A spatially explicit model in Hmsc | Spatial models for big datasets | References
Rendered fromvignette_4_spatial.pdf.asisusingR.rsp::asis

Last update: 2019-08-27
Started: 2019-08-27

Getting started with HMSC-R: high-dimensional multivariate models
Introduction | Generating simulated data for a large community | HMSC analyses of the data with the ``correct'' model | HMSC analyses of misspecified models
Rendered fromvignette_3_multivariate_high.pdf.asisusingR.rsp::asis

Last update: 2019-07-24
Started: 2019-04-23

Getting started with HMSC-R: low-dimensional multivariate models
Introduction | Linear model for a community with five species | A community of four species with a mixture of data types
Rendered fromvignette_2_multivariate_low.pdf.asisusingR.rsp::asis

Last update: 2019-07-24
Started: 2019-04-23

Getting started with HMSC-R: univariate models
Introduction | Linear model | Generalized linear models | Mixed models: fixed and random effects
Rendered fromvignette_1_univariate.pdf.asisusingR.rsp::asis

Last update: 2019-07-24
Started: 2019-04-23

Readme and manuals

Help Manual

Help pageTopics
alignPosterioralignPosterior
biPlotbiPlot
Combine Posterior Samples of Several Hmsc Modelsc.Hmsc
computeAssociationscomputeAssociations
computeDataParameterscomputeDataParameters
computeInitialParameterscomputeInitialParameters
computePredictedValuescomputePredictedValues pcomputePredictedValues
computeSAIRcomputeSAIR
computeVariancePartitioningcomputeVariancePartitioning
computeWAICcomputeWAIC
constructGradientconstructGradient
constructKnotsconstructKnots
convertToCodaObjectconvertToCodaObject
coralCombinecoralCombine
coralGetRareSpeciesPriorscoralGetRareSpeciesPriors
coralGetXDataExtcoralGetXDataExt
coralPlotBetacoralPlotBeta
coralPredictcoralPredict
coralPreprocesscoralPreprocess
coralSplitToBatchescoralSplitToBatches
coralTraincoralTrain
createPartitioncreatePartition
evaluateModelFitevaluateModelFit
getPostEstimategetPostEstimate
HmscHmsc
Create an 'Hmsc' random levelHmscRandomLevel
importPosteriorFromHPCimportPosteriorFromHPC
plotBetaplotBeta
plotGammaplotGamma
plotGradientplotGradient
plotVariancePartitioningplotVariancePartitioning
poolMcmcChainspoolMcmcChains
predictpredict.Hmsc
predictLatentFactorpredictLatentFactor
prepareGradientprepareGradient
sampleMCMCsampleMcmc
samplePriorsamplePrior
setPriorssetPriors
setPriors.HmscsetPriors.Hmsc
setPriors.HmscRandomLevelsetPriors.HmscRandomLevel
Convert Taxonomy to Phylogenetic TreetaxToPhylo
Simulated data and a fitted Hmsc model for a small species community.TD