Set up | Select trees to analyse | Initial appraisal | Detect rogue taxa | Visualize results | References

Getting started | Simulating a practice data set | Setting up a BiSSE model using HiSSE | Setting up a HiSSE model | Setting up a character-independent HiSSE model (i.e., CID-2, CID-4) | Acounting for tip fog | Some considerations regarding optimization | References

Simulating a practice data set under MiSSE | Setting up a MiSSE model | A cautionary tale about empirical trees and what is considered 'surviving' | Using HiSSE and MuHiSSE | Stratigraphic ranges | Plotting simulated data sets | Checking the MiSSE, HiSSE, and MuHiSSE likelihoods | References

The Robinson–Foulds distance | Information-corrected Robinson–Foulds distance | Generalized Robinson–Foulds distances | Constructing a matching | What next? | References

What this vignette is for | 1. Fit a model and get the best joint reconstruction | 2. Sample the joint posterior | 3. A distance between reconstructions | If you want it faster | 4. Consensus clustering | 5. What the axes look like | 6. Where on the tree do they disagree? | 7. Turning axes into numbers | Adapting this to your data | Function reference | Reference

References

1: Introduction | 2: What's new in AnnotationBustR 2.0+ | 2.1: New Dependencies Provide Up To Date Access, New Features, and Stability | 2.2: Other Minor New Features | 3: Installation | 3.1: Installation From CRAN | 3.2: Installation of Development Version From GitHub | 4: Using AnnotationBustR | 4.1:(Optional Step) Finding the Longest Available | 4.2: Load a Data Frame of Search Terms of Gene Synonyms to Search With: | 4.3:(Optional Step) Merge Search Terms If Neccessary | 4.4 Extract sequences with AnnotationBust | 4.5 Troubleshooting | 5: Final Comments

Getting started | Simulating a range-independent process | Setting up the models | Computing Akaike Weights. | Model averaging and plotting. | References

Background | Setting up a MiSSE model | Fixing extinction fraction | Other considerations | MiSSE Greedy | References

Background | Simulating a practice data set | Setting up a MuSSE model using MuHiSSE | Setting up a character-dependent MuHiSSE model | Setting up a character-independent MuHiSSE model | Relationship of MuHiSSE to a three-state GeoSSE without cladogenetic changes | Other considerations | References

Getting started | Setting up the transition rate matrix | A common mistake | Changing the output | Setting up the 2-state character-independent (CID-2) model | References

Timeline | Rendered equation error in original Beaulieu et al. (2012) | Variance-covariance matrix calculation error in OUwie | Details of the issue | General equations | Expectation of the trait value for species i | Covariance between species i and j | A simple example | FAQ | References

How much faster is it? | Where the speedup comes from | A steadier optimization problem | Reliability improvements around the likelihood

Introduction | Preface | Motivation | The Actual Guide | The dataset | Basic usage | Basic usage - model comparison | Advanced usage | Creating a custom model set | Troubleshooting | FAQs | Why does joint modeling matter? | Why doesn't discrete plus continuous likelihood equal total likelihood (lnLDisc + lnLCont = lnLTot)? | How long is my run going to take? | Why is hOUwie so slow?

Bug fixes and deprecated functions | New Features | Idenfiability tests | Contour plots | Ancestral trait reconstruction | Generalized three-point structured algorithm | Estimating tip fog | References

Setup | Simulation | Inference

Introduction | Getting started | Distance based methods | Bootstrap | Parsimony | Branch and bound | Maximum likelihood | Model selection | Conducting a ML tree | Exporting a tree | Molecular dating with a strict clock for ultrametric and tipdated phylogenies | Session info | References

Adding geological timescales | Scales on other axes | Add multiple timescales | Stack multiple scales | Timescales and faceted plots | Resize labels to fit inside interval rectangles | Scales on discrete axes | Custom discrete scales | More advanced topics

Plot occurrences through time | Geological timescale color scales for ggplot | Facetting with the geological timescale

Introduction | Parsimony reconstructions | Likelihood reconstructions | Fitting for discrete comparative data | Session info | References

Load packages | Load data | Parsimony | Branch and bound | Root trees | Plot trees | Consensus tree | Session info | References

Installing R | Installing the phangorn library | Getting started | Set the working directory | Read in the example file datasets: | Viewing the data | Intertwining trees and network functions | 1A: | 1B: | 1C: | Extras... | Figure 4

consensusNet | neighborNet | Adding support values | Estimating edge weights (nnls) | Import and export networks, advanced functions for networx objects | Session Information | References

Changes in phangorn 3.0 | Session info | References

Introduction | User defined data formats | Markov models of character evolution | Estimation of non-standard transition rate matrices | Predefined models for user defined data | Codon substitution models | Session info | References

Model comparison | Bootstrap | Generating trees | Session info | References

Introduction | What is vegan? | How to obtain vegan and R? | What other packages are available for ecologists? | What other documentation is available for vegan? | Is there a Graphical User Interface (GUI) for vegan? | How to cite vegan? | How to build vegan from sources? | Are there binaries for devel versions? | How to report a bug in vegan? | Is it a bug or a feature? | Can I contribute to vegan? | Ordination | I have only numeric and positive data but vegan still complains | Can I use tibbles in ordination? | Can I analyse binary or cover class data? | Why dissimilarities in vegan differ from other sources? | I cannot get repeated solutions in metaMDS | I get zero stress but no repeated solutions in metaMDS | I have heard that you cannot fit environmental vectors or surfaces to NMDS results which only have rank-order scores | Why ordiellipses are much smaller than ggplot2 ellipses? | Where can I find numerical scores of ordination axes? | How the RDA results are scaled? | Variance explained by ordination axes. | Can I have random effects in constrained ordination or in adonis? | Is it possible to have passive points in ordination? | Class variables and dummies | How are environmental arrows scaled? | I cannot see all variables in constrained ordination | Plotting aliased variables | Restricted permutations in vegan | Why restricted permutation does not influence adonis results? | How to use different plotting symbols in ordination graphics? | How to avoid cluttered ordination graphs? | Can I flip an axis in ordination diagram? | Can I zoom into an ordination plot? | Other analysis methods | Is there TWINSPAN?

Plotting ancestral states | Using CLA secsse

Introduction | Set-up | Fitting the model | Comparing with reconstructed trees | References

Secsse introduction | Secsse input files | Note on assigning ambiguity to taxon trait states | Setting up an analysis | ETD | Lambda matrices | Mu vector | Transition matrix | Maximum Likelihood | CTD | CR | Model comparisong using AIC | Further help | References

Diversity indices | Rarefaction | Taxonomic and functional diversity | Species abundance models | Species accumulation and beta diversity | Species pool

Installing | Comparative Methods | AncCond | AncCondFast | GetTipRates | FuzzyMatch | SampleTrees | countTrees | SuperMatrix | Phylogenetic Rate Analysis | scaleTreeRates & plot.phyloscaled | make.simmap2 & fix.simmap | Population Genetics | CalcD & WinCalcD | getNe | pSAF | Utility/Misc. Functions | ReOrderAlignment | Sliding window | ResSel

Loading trees | Calculating distances | Pairs of trees | Multiple comparisons | Pairwise comparison | Trees with different tip labels | Other calculations | What next?

Partition distances | Quartet distances | Normalization | Asymmetric differences | Quartet similarity in a pair of random trees | Independence | Minimum quartet similarity | References

Zero-edge format | Converting to SAtree | Other useful functions

Section 1: Default use of corHMM | 1.1: No hidden rate categories | 1.2: A trait with any number of states and any number of hidden rate categories | 1.3: Estimating "tip fog" | Section 2: How to make and interpret custom models | 2.1: Creating and using custom rate matrices | 2.1.1: One rate category | 2.1.2: Any number of rate categories | 2.2: Biological examples | 2.2.1: Ordered habitat change | 2.2.2: The precursor model | 2.2.3: Ontological relationship of multiple characters | Section 3: Estimating models when node states are fixed | 3.1: Fixing a single node | 3.2: Estimating rates under a parsimony reconstruction | 3.3: Fixing nodes when the model contains hidden states | References

Ordination | Ordination graphics | Fitting environmental variables | Constrained ordination

Shiny app | Scripting at the R command line | Comparing trees' dispersal / hypervolume | Distances from median | Consensus resolution | References

Normalizing | Nye et al. tree similarity | Normalizing to random similarity | Testing similarity to a known tree | Example | What next? | References

Loading trees | Calculating distances | Pairs of trees | Multiple comparisons | Visualizing a matching | What next?

Tree space analysis via user interface | Avoiding common pitfalls in tree space analysis | Using a suitable distance metric | Mapping distances | Identifying clusters | Identifying islands | Validating a mapping | Comparing cluster size | Self-organizing maps | What next? | References

Abstract | Introduction | The 'aphid' package | Hidden Markov Models | Deriving HMMs from sequence data | Profile Hidden Markov Models | File I/O | Sequence Simulation | Model Training | Sequence Alignment | Further Reading | Acknowledgements | References

Combining data from OToL and other sources. | Get Open Tree IDs to match your data. | Find a tree with your taxa | Published trees | A part of the synthesis tree | Connect your data to the tips of your tree | Find external data associated with studies, trees and taxa from Open Tree | Get external data from a study | Find a OTT taxon in another taxonomic database | What next

Demonstration of a basic workflow | Step 1: Matching taxonomy to the ott_id | Step 2: Getting the tree corresponding to our taxa | FAQ | How to change the ott ids assigned to my taxa? | How do I know that the taxa I'm asking for is the correct one? | How do I get the tree for a particular taxonomic group? | How do I find trees from studies focused on my favourite taxa? | The tree returned by the API has duplicated tip labels, how can I work around it? | How do I get the higher taxonomy for a given taxa? | Why are OTT IDs discovered with rotl missing from an induced subtree? | Removing the taxa missing from the synthetic tree | Using the full taxonomic names

Phylogenetic Comparative Methods | A phylogenetic meta-analysis | Gather the data | Find the species in OTT | Get a tree | Perform the meta-analysis | What other comparative methods can I use in R?

Running a MCMC | Input data format | Setting up the joint MCMC estimation | Choosing parameters for the MCMC analyses | Checking for convergence | Computing summary statistics from the posterior distribution | Extract the correlation from the model | Using overlap tests | Extracting the posterior samples of rate matrices

General comments | Structure of the starting point list | Order of the regimes in the start point object | Worked example

Priors on the root value | Uniform prior on the root | Informative prior on the root | Priors on the evolutionary rate matrix | Uniformative priors on the evolutionary correlation | Using informative prior distributions on the structure of evolutionary correlation among traits | Final considerations

Timescales and phylogenies | Phylogenies with only fossil taxa | Circular phylogenies | Circular phylogenies with "stacked" timescales | Disclaimer | Axis timescales on radial phylogenies | Tip labels on radial phylogenies | Clade labels on radial phylogenies

coord_trans meets coord_flip | 2D linear transformations

Introduction | Invoking treedater from the command line | Influenza H3N2 HA | Detecting and removing outliers / testing for relaxed clock | Parametric bootstrap | Missing sample times

Stratigraphic columns | Plot stratigraphic column | Use stratigraphic patterns | Further customization

FBD Parameters Statistics and Plots (Mr. Bayes) | 1. Import combined log file from all runs. | 2. Summarize FBD parameters by time bin | 3. Plot the distribution of each FBD parameter | 4. Test for assumptions | 5. Test for significant FBD shifts between time bins | FBD Parameters Statistics and Plots (BEAST2) | References

Analyzing Trees With Offset ages (BEAST2) | 1. Import the posterior tree distribution. | 2. Summarize the modified tree distribution | 3. Restore the final summary tree | Analyzing Trees With Offset ages (Mr.Bayes) | References

Character Partitioning (Case 1) | 1. Generate distance matrix | 2. Estimate the optimal number of partitions | 3. Simple Workflow | 4. Complete Workflow | Character Partitioning (Case 2) | 3. Complete Workflow | References

Evolutionary Rates Statistics and Plots | 0. Set your BEAST2 analysis to log multiple clocks | 1. Get rates from the clock tree and create a rate table | 2. Export the rate table and plot tree with node values | Plot tree node labels to customize clade names | 3. Import rate table with custom clade memberships | 4. Get summary stats for each clade/clock partition | 5. Plot rates by clock partition and clade | 6. Rate linear models | Selection Mode | 1. Import posterior log file | 3. Plot selection gradient on the summary tree | 4. Pairwise t-tests of rate values among clades | References

Evolutionary Rates Statistics and Plots | 1. Get rates from the clock tree and create a rate table | 2. Export the rate table and plot tree with node values | Plot tree node labels to customize clade names | 3. Import rate table with custom clade memberships | 4. Get summary stats for each clade/clock partition | 5. Plot rates by clock partition and clade | 6. Rate linear models | ADDENDUM: Example using rates from single clock analysis (no partitioning) | Selection Mode | 1. Import combined log file from all runs. | 3. Plot selection gradient on the summary tree | 4. Pairwise t-tests of rate values among clades | References

Creating Inter-character Distance Matrices | Types of morphological data | Treatment of inapplicable and missing data

Index | tree.merger tool | tree.merger basics | Merging trees: input tree and data | Merging trees: Attaching individual tips to the backbone tree | Merging trees: Attaching clades to the backbone tree | Building a new tree | Guided examples | scaleTree tool | Guided examples | move.lineage tool | cutPhylo tool | fix.poly tool | References

Load raw data | From an Excel spreadsheet | From a text or CSV (comma separated values) file | From a Nexus file | From a TNT file | Processing raw data | Store processed data | What next? | References

Index | swapONE | Swapping species positions | Shifting node ages | Guided examples | resampleTree | References

Index | overfit- functions basics | overfitRR | Guided examples | overfitSS | overfitST | overfitSC | overfitPGLS

Index | plotting RRphylo outputs onto the tree | plotting RRphylo rates | plotting search.trend results | plotting search.shift results | plotting search.conv results

Index | RRphylo Basics | RRphylo phylogenetic ridge regression | Accounting for the effect of a covariate | Multiple regression framework | Setting values at internal nodes | Guided examples | References

Index | search.conv basics | Morphological convergence between clades | Morphological convergence within/between categories | Guided examples | References

Index | search.trend basics | Temporal trends on the entire tree | Temporal trends at clade level | Multivariate data and multiple RRphylo output | Guided examples | References

Index | search.shift basics | Clade condition | Sparse condition | Guided examples | References

What next?

1. Collect and process morphological data | Specimens information | Landmark points | Raw data | A. Carnivores data set | B. Foxes data set | 2. Procrustes alignment | 3. Correct C for population noise and trait correlation | 4. Bayesian inference of divergence times with MCMCtree | 5. Visualise and validate divergence dating results | EXERCISES

Introduction | HiSSE vs. FiSSE | Reanalysis of Rabosky and Goldberg (2017) with CID-4 in the set | Suggestions | References

1: Introduction | 2: Installation | 2.1: Installation From CRAN | 2.2: Installation of Development Version From GitHub | 3: Using dietr | 3.0: Basic Data Necessary to Run dietr for Trophic level Calculations | 3.1: Using dietr to Calculate Trophic Levels From FishBase Diet Data | 3.2: Using dietr to Calculate Trophic Levels from FishBase Food Item Data | 3.3: Using dietr to Calculate Trophic Levels From Your Own Data | 3.4: Using dietr to Calculate Electivity Indices | 3.5: Using dietr to Calculate Composite Diet Indices | 3.5: Using dietr to Calculate the Gastro-somatic Index and Vacuity Index | 4: Final Comments | 5: References

APPENDIX S2

Introduction | Getting data into paleoTS | Fitting models | An Empirical Example: Bell's stickleback data | Joint versus AD parameterization | References

Preliminaries | What is ANOVA? | What is ANOVA in RRPP? | What is MANOVA? | MANOVA in RRPP | Example of ANOVA and MANOVA in RRPP | Dimensionality Warning for MANOVA on high-dimensional data | Conclusions and Suggestions

1 Introduction | 2 Installation | 2.1 CRAN | 2.2 Development Version from GitHub | 3 Use | 3.1 Load Data | 3.1.1 Example Files | 3.1.2 Own Files | 3.2 Run Main Analysis | 3.2.1 Taxonomy Based on Tip Labels | 3.2.2 Taxonomy Based on File | 3.2.3 Taxonomy Downloaded from Web | 3.2.4 Other Options | 3.3 Accessing the Results | 3.3.1 First Impression - Summary and Results Tables | 3.3.2 Further Look - Customized Access to Detailed Results | 3.4 Plotting | 3.4.1 Monophyly Plot | 3.4.2 Taxonomy Plot | 3.4.3 Monophyly-Taxonomy Mirror Plot | 3.4.4 Intruder Plot | 3.4.5 Solutions for Large Trees | 4 Final Notes

1. Getting species names within the family Fringillidae | 2. Search the species names across a chronogram database | 3. Summarizing a datelifeResult object | 3.1 Source chronograms: | To see how different datelife queries are made, check out the next tutorial vignette: Making a datelife query.

What can you get with datelife? | How do you use datelife to get these data? | 1. Processing your organism names | a) From a set of organism taxonomic names | b) From a tree with organism taxonomic names as tip labels | 2. Searching the chronogram database | 3. Summarizing chronogram search results | Citations | MRCA ages | Newick strings | phylo objects | Summary tables | To see an application of these functions in a case study, check out the next tutorial vignette: Case Study: the true finches.

When input is list or vector of taxon names | When input names are not found in the taxonomy | Subspecies can also be searched | Choosing between homonyms | Making a query from an inclusive taxonomic group | The function get_opentree_species() | To see how trees with branch lengths estimated from BOLD (Barcode Of Life Database) data can be made with a datelife workflow, check out the next tutorial vignette: Estimating Branch Lengths.

Shared phylogenetic information | Conflicting splits can nevertheless be instructive | Mutual clustering information | Nye et al. tree similarity metric | Jaccard–Robinson–Foulds metric | Matching Split Distance | Information theoretic alternative | References

Basic tree and metadata queries | Replicating results | Meta-Analysis

Introduction | Package overview | Using the S4 help system | Trees without data | Trees with data | Subsetting | Tree-walking | multiPhylo4 classes | Examples | Constructing a Brownian motion trait simulator | Definitions/slots | phylo4 | phylo4d

apex: Phylogenetic Methods for Multiple Gene Data | Installing apex | Importing data | ape wrappers | phangorn wrappers | New object classes | multidna | multiphyDat | Handling data | Building trees | Distance-based trees | Likelihood-based trees | Exporting data

Arranging plots with deeptime | Combining plots | Other resources for arranging plots

Plot disparity through time | Disparity in base R | Phylomorphospaces

Constant rate birth-death | Time-dependent speciation and extinction | paleobuddy's flexibility | State-dependent speciation, extinction, and fossil sampling | Conclusion

Introduction | First steps | Exploratory data analysis

References

Prep work | Creating parameter structure | Simulating | Conditioning

Estimating the diversity of a community | Community data | Diversity estimation | Estimating the diversity of a meta-community | Meta-community data | Full documentation | References

Manual entry | From a file | Nexus files | Newick files | Trees from TNT | Other formats | Export (write) trees | What next?

Introduction | Installing NCBI BLAST+ Tools | Pipeline | Setup | Running | Restarting | Changing parameters | Cluster selection | Testing output

Getting started | Finches Examples | Anoles example | Citation

Installing treespace | Content overview | Exploring trees with treespace | Identifying clusters of trees | treespaceServer: a web application for treespace | Finding median trees | Emphasising the placement of certain tips or clades | Method: characterising a tree by a vector | References | Authors / Contributors

Introduction | Prerequisites | Acknowledgement | Chaining | A dendrogram is a nested list of lists with attributes | Installation | How to explore a dendrogram's parameters | Taking a first look at a dendrogram | Getting nodes attributes in a depth-first search | How to change a dendrogram | The "set" function | Two simple trees to play with | Setting a dendrogram's labels | Setting a dendrogram's nodes/leaves (points) | Setting a dendrogram's branches | Adjusting all branches | Coloring branches based on clustering | Adjusting branches based on labels | Highlighting branches' different heights using line width and color | Changing a dendrogram's structure | Rotation | Unbranching | Pruning | Collapse branches | Adding extra bars and rectangles | Adding colored rectangles | Adding colored bars | ggplot2 integration | Enhancing other packages | DendSer | gplots | NMF | heatmaply | dynamicTreeCut | pvclust | circlize | Comparing two dendrograms | dendlist | dend_diff | tanglegram | Correlation measures | Global Comparison of two (or more) dendrograms | Distance matrix using dist.dendlist | Correlation matrix using cor.dendlist | Baker's Gamma Index | Cophenetic correlation | The Fowlkes-Mallows Index and the Bk plot | The Fowlkes-Mallows Index | The Bk plot | Session info

Trees and Time series | Phylogenetic trees | Time series | Data and Errors | Simulating data | Optimization methods | Likelihood computation methods | error - X X X X X | Multivariate GLS models and High-Dimensional datasets | Model comparison | Treatment of the root | Ancestral state and primary optimum | Multiple phylogenetic means | Directional trends | Constraints | Overview of the various constraint parameterizations in mvMORPH | "User-defined" constraints | The "param" list | The "decomp" and "decompSigma" options | the "vcv" argument | Functions | The loglikelihood function | The matrix parameterization functions | Fossil data | Overview of the functions | Imputing missing values | Ancestral state reconstructions | Tweak mvMORPH | Make your own model! | Reuse of the returned log-likelihood functions | Bayesian mcmc

Using OpenTree topology:

Install | phylomatic | aot | bladj

nocite: |@Daru2020, @Daru2017 | 1. Installation | 2. Overview and general workflow of phyloregion | 3. Input data | Phylogenies | Distribution data input | Function points2comm | Function polys2comm | Function rast2comm | 4. Analysis | Alpha diversity | Function weighted_endemism | Function PD – phylogenetic diversity | Function phylo_endemism – phylogenetic endemism | Function EDGE – Evolutionary Distinctiveness and Global Endangerment | Analysis of beta diversity (phylogenetic and non-phylogenetic) | Phylogenetic beta diversity | Session Information | REFERENCES

Benchmarking phyloregion with comparable packages | 1. Analysis of alpha diversity | 1.1. Benchmarking phyloregion for analysis of phylogenetic diversity | 1.2. Benchmarking phyloregion for analysis of phylogenetic endemism | 2. Analysis of compositional turnover (beta diversity) | 2.1. Benchmarking phyloregion for analysis of taxonomic beta diversity | 2.2. Benchmarking phyloregion for analysis of phylogenetic beta diversity | Session Information | REFERENCES

Introduction

Splits | Quantifying information | Application to splits | Entropy | References

Introduction | iris - Edgar Anderson's Iris Data | Background | The 3 clusters from the "complete" method vs the real species category | Similarity/difference between various clustering algorithms | Clustering prediction of the 3 species classes | Conclusion | khan - Microarray gene expression data set from Khan et al., 2001. Subset of 306 genes. | Comparing the train vs test dendrograms | votes.repub - Votes for Republican Candidate in Presidential Elections | Heatmap | animals - Attributes of Animals

Introduction | How to colour the labels of a dendrogram by an additional factor variable | How to color a dendrogram's labels according to defined groups? (in R) | How to color a dendrogram's branches/labels based on cluster (i.e.: cutree result) | Change dendrogram's labels | Larger font for leaves in a dendrogram | How to view attributes of a dendrogram | How to color the branches in heatmap.2? | For package developers - how to call imported calls from dendextend 0.18.3? | How to plot a fan (Polar) Dendrogram in R? | A way to calculate lowest value of h in cut that produces groupings of a given minimum size? | Coloring dendrogram's end branches (or leaves) based on column number of data frame in R | Color side bar dendrogram plot | Plot a "mirror" (labels on the left) horizontal dendrogram

Introduction

Introduction | Demonstration | Cleanup

Introduction | Getting started | Example #1: all default | Example #2: JC69 site model | Example #3: Relaxed clock log normal | Example #4: Birth-Death tree prior | Example #5: Yule tree prior with a normally distributed birth rate | Example #6: HKY site model with a non-zero proportion of invariants | Example #7: Strict clock with a known clock rate | Example #8: Use MRCA prior | Example #9: Use MRCA prior to specify a close-to-fixed crown age | Cleanup

Introduction | Getting started | Default inference model | Site models | Clock models | Tree prior | (optional) MRCA prior: a 'Most Recent Common Ancestor' | MCMC: the Markov Chain Monte Carlo setup | (optional) BEAUti options: version-specific options | (experimental) a tipdates filename: a filename for tip-dating

Additional functions for manipulating data

Manipulando Datos

Manipulating Data | Coindexing | Running functions on treedata.table objects | Dropping and extracting taxa from treedata.table objects

Partially matching trait data and tree(s)

Working with multiphylo objects

General parameters | annotation.files.dir | output.dir | dataset.info | x.column | short.name.column | group.column | ontology | dict.path | column | denominator.column | tree.path | tree.type | type | MHT.method | cores | Cutoff values | spearman.qvalue.cutoff | pearson.qvalue.cutoff | kendall.qvalue.cutoff | linear_model.qvalue.cutoff | spearman.cor.lower.cutoff / spearman.cor.upper.cutoff | pearson.cor.lower.cutoff / pearson.cor.upper.cutoff | kendall.cor.lower.cutoff / kendall.cor.upper.cutoff | sd.cutoff | cv.cutoff | annotation_size.cutoff | prevalence.cutoff | heterogeneity.cutoff | Advanced configurations | raw_data_sd_filter

Introduction | Figuring out parameters and what they mean | Simulating a set of host and symbiont pairs | Examining the result & plotting some trees | Simulating locus trees on host and symbiont trees | Load in other datasets

Contents | Background and motivation | Installing the package | Package dependencies | Calling functions from FossilSim and other packages | Quick start | FossilSim input | FossilSim output | What next?

The Relaxed Phylip Format | Why building supermatrix? | Commonly used DNA barcoding markers | The gene marker's mutation rate | Missing Data | Similar software | Features of Phylotools | The Installation | Functions in phylotools | Creating supermatrix with phylip files | Generate supermatrix with fasta files | Citation | Further reading

Examples | Comparing three simple trees | Comparing many trees using an MDS plot | Median trees

Acknowledgement -0.3cm | 1. Introduction | 2. Sequence Data Input and Output | 3. The `_12`1̃2`$12=-1 ms+seqgen Approach | 4. Phylogenetic Clustering (Phyloclustering) | 5. Phylogenetic Analysis by Maximum Likelihood (PAML) | 6. Using the `_12`1̃2`$12=-1 haplo.post.prob() function for Hap-Clustering -0.3cm | References

Loading treespace and data: | Creating neighbour-joining and maximum likelihood trees: | Using treespace to compare trees | treespace analysis | Using treespace to analyse the BEAST trees in more detail: | References

Related tree distance | Concordance

Loading trees | Calculating distances | Comparing multiple trees | Citation

Start

I. Installing PHRAPL

Advanced annotation | Taxonomic identifiers | Working with character data | Extracting character data

Extending the NeXML standard through metadata annotation.

SPARQL Queries

Writing NeXML metadata | Taxonomic identifiers | Custom metadata extensions | Reading NeXML metadata | Merging metadata tables

Understanding the nexml S4 object

1. Sampling uncertainty | 1.1 Sensitivity analysis for influential species | 1.1.1 influ_phylm(): Phylogenetic linear regression | 1.1.2. influ_physig(): Phylogenetic signal | 1.1.3 influ_continuous() and influ_discrete: Trait Evolution Continuous and Discrete Characters | 1.2. Sensitivity analysis for influential clades | 1.2.1 clade_phylm(): Phylogenetic linear regression | 1.2.2 clade_physig(): Phylogenetic signal | 1.2.3 clade_continuous() and clade_discrete: Trait Evolution Continuous and Discrete Characters | 1.3. Sensitivity analysis for sample size | 1.3.1 samp_phylm: Phylogenetic Linear regression | 1.3.2 samp_physig: Phylogenetic signal (k or lambda) | 1.3.3 samp_continuous() and samp_discrete: Trait Evolution Continuous and Discrete Characters | 2. Phylogenetic uncertainty | 2.1 tree_phylm: tree method for phylogenetic linear regression | 2.2 tree_physig(): tree method for phylogenetic signal | 2.3 tree_continuous() and tree_discrete: tree method for trait evolution of continuous and discrete characters | 3. Data uncertainty | 3.1 intra_phylm(): intra method for phylogenetic linear regression | 3.2 intra_physig(): intra method for phylogenetic signal | 4. Interacting uncertainties | Examples: | 4.1 tree_influ_phylm(): tree_influ method for phylogenetic linear regression | 4.2 tree_clade_phylm(): tree_clade method for phylogenetic linear regression | 4.3 tree_samp_phylm(): tree_samp method for phylogenetic linear regression | 4.4 tree_intra_phylm(): tree_intra method for phylogenetic linear regression | 5. Additional functions | 5.1 Combine data and phylogeny | 5.2 Phylogenetic signal for missing data | Background | Example: | 5.3 Diversification and speciation rates | 6. Using sensiPhy with other packages (caper, phytools, gls) | 6.1 PGLS regression | 6.2 phylogenetic signal | 7. How long does it take? | 8. How to support sensiPhy | 8.1 Report bugs or errors | 8.2 Small changes in sensiPhy documentation | 8.3 Contribute with code | 8.4 New ideas or suggestions? | 8.5 Code of Conduct

sim.fbd functions | Simulating trees from a time-homogeneous fossilized-birth death process | Simulating trees from an episodic fossilized-birth death process

Data | Visualizing the data | Measuring and testing the signal for each trait and different methods | Assessing the behavior of these methods with this phylogeny along a Brownian-Motion influence gradient | Assessing the signal depth with correlograms | Locating the signal with LIPA

Data | Basics | Colors | Advanced tuning

Contents | The fossils object | Constant fossil recovery | Simulating fossils for a tree object | Simulating fossils for a taxonomy object | Interval-dependent fossil recovery | Simulating fossils for a fixed number of intervals | Simulating fossils for a set of non-uniform intervals | Simulating fossils using per-interval probabilities | Incorporating stratigraphic age uncertainty | Environment-dependent fossil recovery | Lineage-dependent fossil recovery | The autocorrelated fossil recovery model | The independent fossil recovery model | Environment and lineage-dependent fossil recovery | Extant species and tip sampling | See also | References

Contents | The taxonomy object | See also | References

Compatibility with paleotree

Abstract | Introduction | The phylogram package | Importing and exporting trees | Example 1: Import and export a tree from a Newick string | Converting tree objects | Example 2: Convert a "phylo" object to a dendrogram | Tree editing/manipulation | Example 3: Building and manipulating dendrograms | Tree visualization | Summary | Acknowledgements | Author Contributions | References

Base Tree | Most Recent Common Ancestor | Multiple MRCA

Stylize nodes | Collapse Branches | Rotate Branches | Select Branches | Highlight Nodes | Use Plugins

Parallel processing | Nestedness and Null models | Scaling in redundancy analysis | Weighted average and linear combination scores

Example 1: Models with distinct selective regimes | Type I 0.213 0.22 | Example 2: Tests for evolutionary correlations between two species groups | References: