Package: Hmsc 3.1-3

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_3.1-3.tar.gz
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Hmsc.pdf |Hmsc.html
Hmsc/json (API)
NEWS

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

Peer review:

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:

10.28 score 103 stars 424 scripts 1.0k downloads 4 mentions 27 exports 53 dependencies

Last updated 29 days agofrom:d7dacec0db. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 25 2024
R-4.5-winWARNINGOct 25 2024
R-4.5-linuxWARNINGOct 25 2024
R-4.4-winWARNINGOct 25 2024
R-4.4-macWARNINGOct 25 2024
R-4.3-winWARNINGOct 25 2024
R-4.3-macWARNINGOct 25 2024

Exports:alignPosteriorbiPlotcomputeAssociationscomputeDataParameterscomputePredictedValuescomputeSAIRcomputeVariancePartitioningcomputeWAICconstructGradientconstructKnotsconvertToCodaObjectcreatePartitionevaluateModelFitgetPostEstimateHmscHmscRandomLevelimportPosteriorFromHPCpcomputePredictedValuesplotBetaplotGammaplotGradientplotVariancePartitioningpoolMcmcChainspredictLatentFactorprepareGradientsampleMcmcsetPriors

Dependencies:abindapeBayesLogitclicodacolorspacedigestdotCall64fansifarverfieldsFNNggplot2gluegtableisobandlabelinglatticelifecyclemagrittrmapsMASSMatrixMatrixModelsmatrixStatsmcmcMCMCpackmgcvmunsellnlmennetpillarpkgconfigplyrpracmapROCquantregR6RColorBrewerRcpprlangscalesspspamSparseMstatmodsurvivaltibbletruncnormutf8vctrsviridisLitewithr

Getting started with HMSC-R: high-dimensional multivariate models

Rendered fromvignette_3_multivariate_high.pdf.asisusingR.rsp::asison Oct 25 2024.

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

Getting started with HMSC-R: low-dimensional multivariate models

Rendered fromvignette_2_multivariate_low.pdf.asisusingR.rsp::asison Oct 25 2024.

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

Getting started with HMSC-R: spatial models

Rendered fromvignette_4_spatial.pdf.asisusingR.rsp::asison Oct 25 2024.

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

Getting started with HMSC-R: univariate models

Rendered fromvignette_1_univariate.pdf.asisusingR.rsp::asison Oct 25 2024.

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

Testing the performance of Hmsc with simulated data

Rendered fromvignette_5_performance.pdf.asisusingR.rsp::asison Oct 25 2024.

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

Readme and manuals

Help Manual

Help pageTopics
alignPosterioralignPosterior
biPlotbiPlot
Combine Posterior Samples of Several Hmsc Modelsc.Hmsc
computeAssociationscomputeAssociations
computeDataParameterscomputeDataParameters
computeInitialParameterscomputeInitialParameters
computePredictedValuescomputePredictedValues pcomputePredictedValues
computeVariancePartitioningcomputeVariancePartitioning
computeWAICcomputeWAIC
constructGradientconstructGradient
constructKnotsconstructKnots
convertToCodaObjectconvertToCodaObject
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
Simulated data and a fitted Hmsc model for a small species community.TD