All functions
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addNoise()
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Add Noise |
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analyseTaxaInA()
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Compute edge statistics for an interaction matrix with taxonomic information |
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assignTaxonLevelsToA()
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Assign Taxon Levels |
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autocorVsTaxonNum()
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Compute slope for increasing number of taxa versus their autocorrelation |
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binByMemory()
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Bin rows in a matrix by memory. |
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binaryToPerturb()
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Given a binary vector, build a perturbation object |
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caporaso_F4FecesL6
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Caporaso Stool Sequencing Data for Subject F4 on level 6 |
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caporaso_F4LPalmL6
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Caporaso Left Palm Sequencing Data for Subject F4 on level 6 |
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caporaso_F4RPalmL6
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Caporaso Right Palm Sequencing Data for Subject F4 on level 6 |
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caporaso_F4TongueL6
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Caporaso Tongue Sequencing Data for Subject F4 on level 6 |
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caporaso_M3FecesL6
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Caporaso Stool Sequencing Data for Subject M3 on level 6 |
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compareTS()
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Community Time Series Comparison |
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david_stoolA_metadata
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David et al. stool sample metadata object of subject A |
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david_stoolA_otus
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David Stool Sequencing Data for Subject A |
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david_stoolB_metadata
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David et al. stool sample metadata object of subject B |
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david_stoolB_otus
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David Stool Sequencing Data for Subject B |
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david_stool_lineages
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David et al. stool OTU lineages |
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doc()
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Dissimilarity-Overlap Curve (DOC) |
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envGrowthChanges()
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Generate growth changes induced by environment |
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filterTaxonMatrix()
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Filter taxa in an abundance matrix |
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generateA()
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Generate an interaction matrix |
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generateAbundances()
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Generate Abundance Vector |
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generateDataSet()
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Generate a dataset |
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generateTS()
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Generate community time series |
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getAStats()
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Analyse an interaction matrix |
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getConnectance()
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Connectance |
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getPep()
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Positive Edge Percentage |
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getTaxonomy()
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Get the taxonomy given OTU names and lineage information |
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glv()
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Simulate time series with the generalized Lotka-Volterra model |
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hill()
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Hill numbers |
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identifyNoisetypes()
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Identify Noise Types |
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interpolate()
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Time Series Interpolation |
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limits()
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LIMITS |
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limitsQuality()
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Quality scores and plot for estimated interaction matrices |
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modifyA()
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Modify the interaction matrix |
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noisetypes()
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Constructor for S3 noisetypes class |
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normalize()
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Normalize a matrix |
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perturbToBinary()
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Given a perturbation object, extract a binary vector |
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perturbation()
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Perturbation |
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plotA()
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Plot an interaction matrix. |
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plotAbundanceVsNoisetypes()
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Plot taxon abundances classified by noise type |
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plotNoisetypes()
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Do a barplot of the noise types |
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plotNoisetypesVsHurst()
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Plot noise types versus their range of Hurst exponents |
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powerspec()
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Report slope of periodogram in log scale |
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rad()
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Rank abundance distribution curve |
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rarefyFilter()
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Rarefaction combined with sample filtering |
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removeLowAbundance()
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Remove lowest abundance species |
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ricker()
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Generate time series with the Ricker model |
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seqtime
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Time Series Analysis of Sequencing Data |
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sheldon()
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Compute evenness using Sheldon's index |
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simCountMat()
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Simulate a count matrix |
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simDecay()
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Plot community similarity decay against selected taxa or metadata |
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simHubbell()
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Hubbell Simulation |
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simNoiseMat()
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Simulate Noise |
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simUntb()
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Run the Unified Neutral Theory of Biodiversity (UNTB) model |
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sliceTS()
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Slice time series |
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soi()
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Self-organized instable model |
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taylor()
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Plot relationship between row mean and row variance |
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testStability()
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Stability test for interaction matrix |
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timeDecay()
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Plot the time decay. |
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tsDiagnostic()
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Diagnostics for Community Simulation |
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tsJumpStats()
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Compute statistics on jumps through community space |
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tsplot()
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Time Series Plot |
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tsubsample()
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Subsample Time Series |
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varEvol()
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Plot mean variance versus the number of time points. |