Package 'evolved' reference manual

Title:	Open Software for Teaching Evolutionary Biology at Multiple Scales Through Virtual Inquiries
Description:	"Evolutionary Virtual Education" - 'evolved' - provides multiple tools to help educators (especially at the graduate level or in advanced undergraduate level courses) apply inquiry-based learning in general evolution classes. In particular, the tools provided include functions that simulate evolutionary processes (e.g., genetic drift, natural selection within a single locus) or concepts (e.g. Hardy-Weinberg equilibrium, phylogenetic distribution of traits). More than only simulating, the package also provides tools for students to analyze (e.g., measuring, testing, visualizing) datasets with characteristics that are common to many fields related to evolutionary biology. Importantly, the package is heavily oriented towards providing tools for inquiry-based learning - where students follow scientific practices to actively construct knowledge. For additional details, see package's vignettes.
Authors:	Matheus Januario [aut, cph, cre] , Jennifer Auler [aut] , Andressa Viol [aut], Daniel Rabosky [aut]
Maintainer:	Matheus Januario <[email protected]>
License:	GPL (>= 3)
Version:	1.0.0
Built:	2024-12-20 04:26:33 UTC
Source:	https://github.com/mjanuario/evolved

Occurrences of ammonoid fossils

Description

Many ammonoid (Cephalopoda, Mollusca) fossil occurrences from different moments of the geological past. Much information (i.e., extra columns) was removed from the original dataset to make it more compact, but it can be fully accessed by the data URL. This dataset is part of the package and is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Usage

ammonoidea_fossil
ammonoidea_fossil

Format

A data.frame with 58111 rows and 13 columns.

phylum: Organism phylum
class: Organism taxonomic class
order: Organism taxonomic order
family: Organism taxonomic family
genus: Organism genus
species: Organism specific name
early_interval: Earlier known geological period of occurrence
late_interval: Later known geological period of occurrence
max_ma: Occurrence's oldest time boundary in million years
min_ma: Occurrence's newest time boundary in million years
midpoint: Midpoint between max_ma and min_ma
lng: Longitude of place where occurrence was found. Follows decimal degree format.
lat: Latitude of place where occurrence was found. Follows decimal degree format.

Source

The Paleobiology Database (downloaded on 2022-03-11).
Data URL: http://paleobiodb.org/data1.2/occs/list.csv?datainfo&rowcount&base_name=Ammonoidea&show=full,classext,genus,subgenus,acconly,ident,img,etbasis,strat,lith,env,timebins,timecompare,resgroup,ref,ent,entname,crmod

Birds species list

Description

Birds species list

Usage

birds_spp
birds_spp

Format

A vector containing the names of almost all (i.e., 9993) extant bird species, following Jetz et al. (2012) taxonomy. This dataset is part of the package and is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Source

Actual file downloaded from https://vertlife.org/data/

References

Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K., & Mooers, A. O. (2012). The global diversity of birds in space and time. Nature, 491(7424), 444-448.

Calculate paleo diversity curves through different methods

Description

calcFossilDivTT calculates fossil diversity through time using different methods.

Usage

calcFossilDivTT(
  data,
  tax.lvl = "species",
  method = "rangethrough",
  bin.reso = 1
)
calcFossilDivTT(
  data,
  tax.lvl = "species",
  method = "rangethrough",
  bin.reso = 1
)

Arguments

`data`	A `data.frame` containing the columns: `max_ma`, `min_ma` and the name provided in `tax.lvl`. `max_ma` and `min_ma` are respectively the early and late bounds of rock layer's age. `tax.lvl` column is the taxonomic level of the data. Any additional columns are ignored.
`tax.lvl`	A `character` giving the taxonomic in which calculations will be based on (default value is `"species"`). This must refer to the column names in `data`.
`method`	A `character` string setting the method which should be used. Could be either `"rangethrough"` or `"stdmethod"`, which will respectively calculate diversity using the range through or the standard methods (Foote & Miller, 2007)
`bin.reso`	A `numeric` assigning the resolution (length) of the time bin to consider in calculations. Default value is `1` (which in most cases - e.g. those following the Paleobiology Database default timescale - will equate to one million years)

Value

A data.frame containing the diversity (column div) of the chosen taxonomic level through time, with calculation based on method. If "method = rangethrough", the time moments are the layer boundaries given in data. If "method = stdmethod", the time moments are evenly-space bins with length equal to bin.reso, starting at the earliest bound in the dataset.

Author(s)

Matheus Januario, Jennifer Auler

References

Foote, M., Miller, A. I., Raup, D. M., & Stanley, S. M. (2007). Principles of paleontology. Macmillan.

Examples


# Loading data
data("dinos_fossil")

# Using function:
div1 <- calcFossilDivTT(dinos_fossil, method = "stdmethod")
div2 <- calcFossilDivTT(dinos_fossil, method = "stdmethod", bin.reso = 10)

# Comparing different bins sizes in the standard method
plot(x=div1$age, y=div1$div, type="l", 
     xlab = "Time (Mya)", ylab = "Richness", 
     xlim=rev(range(div1$age)), col="red") 
lines(x=div2$age, y=div2$div, col="blue")

# Comparing different methods:
div3 <- calcFossilDivTT(dinos_fossil, method = "rangethrough")
plot(x=div1$age, y=div1$div, type="l", 
     xlab = "Time (Mya)", ylab = "Richness", 
     xlim=rev(range(div1$age)), col="red") 
lines(x=div3$age, y=div3$div, col="blue")

# Loading data
data("dinos_fossil")

# Using function:
div1 <- calcFossilDivTT(dinos_fossil, method = "stdmethod")
div2 <- calcFossilDivTT(dinos_fossil, method = "stdmethod", bin.reso = 10)

# Comparing different bins sizes in the standard method
plot(x=div1$age, y=div1$div, type="l", 
     xlab = "Time (Mya)", ylab = "Richness", 
     xlim=rev(range(div1$age)), col="red") 
lines(x=div2$age, y=div2$div, col="blue")

# Comparing different methods:
div3 <- calcFossilDivTT(dinos_fossil, method = "rangethrough")
plot(x=div1$age, y=div1$div, type="l", 
     xlab = "Time (Mya)", ylab = "Richness", 
     xlim=rev(range(div1$age)), col="red") 
lines(x=div3$age, y=div3$div, col="blue")

Find and fix small rounding errors in ultrametric trees

Description

checkAndFixUltrametric finds and correct small numerical errors that might appear in ultrametric trees that where created through simulations. This function should never be used as a formal statistical method to make a tree ultrametric, as it was designed just to correct small rounding errors.

Usage

checkAndFixUltrametric(phy)
checkAndFixUltrametric(phy)

Arguments

phy

A phylo object, following terminology from package ape in which function will operate.

Value

A check and fixed phylo object.

Author(s)

Daniel Rabosky, Matheus Januario, Jennifer Auler

References

Paradis, E. (2012). Analysis of Phylogenetics and Evolution with R (Vol. 2). New York: Springer.

Popescu, A. A., Huber, K. T., & Paradis, E. (2012). ape 3.0: New tools for distance-based phylogenetics and evolutionary analysis in R. Bioinformatics, 28(11), 1536-1537.

Examples

S <- 1
E <- 0
set.seed(1)
phy <- simulateTree(pars = c(S, E), max.taxa = 6, max.t = 5)
phy$edge.length[1] <- phy$edge.length[1]+0.1
ape::is.ultrametric(phy)
phy <- checkAndFixUltrametric(phy)
ape::is.ultrametric(phy)

S <- 1
E <- 0
set.seed(1)
phy <- simulateTree(pars = c(S, E), max.taxa = 6, max.t = 5)
phy$edge.length[1] <- phy$edge.length[1]+0.1
ape::is.ultrametric(phy)
phy <- checkAndFixUltrametric(phy)
ape::is.ultrametric(phy)

Counting protein sequence differences

Description

countSeqDiffs counts the number of protein differences among two sequences of proteins within the same "ProteinSeq" object.

Usage

countSeqDiffs(x, taxon1, taxon2)
countSeqDiffs(x, taxon1, taxon2)

Arguments

`x`	A "ProteinSeq" object containing proteins from `taxon1` and `taxon2`.
`taxon1`	A character giving the common name of the first species that will be compared. Must be a name present in `x`.
`taxon2`	A character giving the common name of the second species that will be compared. Must be a name present in `x`.

Value

A integer giving the number of protein differences between taxon1 and taxon2.

Author(s)

Matheus Januario, Dan Rabosky, Jennifer Auler

Examples

countSeqDiffs(cytOxidase, "human", "chimpanzee")

countSeqDiffs(cytOxidase, "human", "cnidaria")

countSeqDiffs(cytOxidase, "chimpanzee", "cnidaria")

countSeqDiffs(cytOxidase, "human", "chimpanzee")

countSeqDiffs(cytOxidase, "human", "cnidaria")

countSeqDiffs(cytOxidase, "chimpanzee", "cnidaria")

Cytochrome Oxidase sequences

Description

cytOxidase is a set of homologous protein sequences from the GENE cytochrome oxidase SUBUNIT 1 gene. This mitochondrial gene, often known as CO1 (“see-oh-one”), plays a key role in cellular respiration. C01 contains approximately 513 aminoacids and has been used by previous studies for reconstructing phylogenetic trees and estimating divergence times in Metazoaria by assuming a molecular clock. Its 5' partition is used for the ‘Barcoding of Life’ initiative, for instance. This dataset is part of the package and is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Usage

cytOxidase
cytOxidase

Format

A object of class "ProteinSeq" with 17 entries, each representing a different animal species

names: Organism's popular name or a taxonomic classification
sequence: Aminoacid sequence of length 513

Details

The object of class "ProteinSeq" is structured as a named vector of 17 different animal species, with each individual component being a sequence of 513 aminoacids.

Source

Amino acid sequences were originally downloaded from genebank and later curated and aligned by Daniel L. Rabosky.

Whale body size and speciation rates

Description

Data on the body size of many cetacean species and species-specific speciation rates. This dataset is part of the package and is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Usage

data_whales
data_whales

Format

A data.frame with 75 rows and 4 columns.

species: Whale species
log_mass: Log of body mass (grams)
S: Species-specific speciation rate
color: Suggested color to be used for the tip's clade

Details

Species follow taxonomy from Steeman et al (2009). Species-specific speciation rates from Rabosky 2014 & Rabosky et al, 2014. Mass data from PanTHERIA (Jones et al, 2009).

Source

Compilation of many primary sources (see details).

References

Jones, K. E., Bielby, J., Cardillo, M., Fritz, S. A., O'Dell, J., Orme, C. D. L., ... & Purvis, A. (2009). PanTHERIA: a species‐level database of life history, ecology, and geography of extant and recently extinct mammals: Ecological Archives E090‐184. Ecology, 90(9), 2648-2648.

Rabosky, D. L. (2014). Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS one, 9(2), e89543.

Rabosky, D. L., Grundler, M., Anderson, C., Title, P., Shi, J. J., Brown, J. W., ... & Larson, J. G. (2014). BAMM tools: an R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods in Ecology and Evolution, 5(7), 701-707.

Steeman, M. E., Hebsgaard, M. B., Fordyce, R. E., Ho, S. Y., Rabosky, D. L., Nielsen, R., ... & Willerslev, E. (2009). Radiation of extant cetaceans driven by restructuring of the oceans. Systematic biology, 58(6), 573-585.

Occurrence of dinosaur fossils

Description

Many dinosaur (including avian species) fossil occurrences from different moments of the geological past. Much information (i.e., extra columns) was removed from the original dataset to make it more compact, but it can be fully accessed by the data URL. This dataset is part of the package and is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Usage

dinos_fossil
dinos_fossil

Format

A data.frame containing 15527 rows and 13 columns

phylum: Organism phylum
class: Organism taxonomic class
order: Organism taxonomic order
family: Organism taxonomic family
genus: Organism genus
species: Organism specific name
early_interval: Earlier known geological period of occurrence
late_interval: Later known geological period of occurrence
max_ma: Occurrence's oldest time boundary in million years
min_ma: Occurrence's newest time boundary in million years
midpoint: Midpoint between max_ma and min_ma
lng: Longitude of place where occurrence was found. Follows decimal degree format.
lat: Latitude of place where occurrence was found. Follows decimal degree format.

Source

The Paleobiology Database (downloaded on 2022-03-11).
Data URL: http://paleobiodb.org/data1.2/occs/list.csv?datainfo&rowcount&base_name=Dinosauria&show=full,classext,genus,subgenus,acconly,ident,img,etbasis,strat,lith,env,timebins,timecompare,resgroup,ref,ent,entname,crmod

Estimate speciation assuming a pure-birth process

Description

estimateSpeciation Estimates the speciation rate assuming a constant-rate, pure-birth model.

Usage

estimateSpeciation(phy)
estimateSpeciation(phy)

Arguments

phy

A phylo object, following terminology from package ape in which function will operate.

Value

A numeric with the estimated speciation rate.

Author(s)

Daniel Rabosky, Matheus Januario, Jennifer Auler

References

Yule G.U. 1925. A mathematical theory of evolution, based on the conclusions of Dr. JC Willis, FRS. Philosophical Transactions of the Royal Society of London. Series B, Containing Papers of a Biological Character. 213:21–87.

Examples

S <- 1
E <- 0
set.seed(1)
phy <- simulateTree(pars = c(S, E), max.taxa = 6, max.t = 5)
estimateSpeciation(phy)

S <- 1
E <- 0
set.seed(1)
phy <- simulateTree(pars = c(S, E), max.taxa = 6, max.t = 5)
estimateSpeciation(phy)

Fit a constant-rate birth-death process to a phylogeny

Description

fitCRBD fits a constant-rate birth-death process to a phylogeny in the format of ape package's phylo object. Optimization is based on likelihood functions made with diversitree. This function is basically a wrapper for the diversitree's make.bd function.

Usage

fitCRBD(phy, n.opt = 5, l.min = 0.001, l.max = 5, max.bad = 200)
fitCRBD(phy, n.opt = 5, l.min = 0.001, l.max = 5, max.bad = 200)

Arguments

`phy`	A `phylo` object, following terminology from package `ape`, in which function will operate.
`n.opt`	Number of optimizations that will be tried by function.
`l.min`	Lower bound for optimization. Default value is `0.001`.
`l.max`	Upper bound for optimization. Default value is `5`.
`max.bad`	Maximum number of unsuccessful optimization attempts. Default value is `200`.

Value

A numeric with the best estimates of speciation S and extinction E rates.

Author(s)

Daniel Rabosky, Matheus Januario, Jennifer Auler

References

Paradis, E. (2012). Analysis of Phylogenetics and Evolution with R (Vol. 2). New York: Springer.

Popescu, A. A., Huber, K. T., & Paradis, E. (2012). ape 3.0: New tools for distance-based phylogenetics and evolutionary analysis in R. Bioinformatics, 28(11), 1536-1537.

FitzJohn, R. G. (2010). Analysing diversification with diversitree. R Package. ver, 9-2.

FitzJohn, R. G. (2012). Diversitree: comparative phylogenetic analyses of diversification in R. Methods in Ecology and Evolution, 3(6), 1084-1092.

Examples


S <- 0.1
E <- 0.1
set.seed(1)
phy <- simulateTree(pars = c(S, E), max.taxa = 30, max.t = 8)
fitCRBD(phy)

S <- 0.1
E <- 0.1
set.seed(1)
phy <- simulateTree(pars = c(S, E), max.taxa = 30, max.t = 8)
fitCRBD(phy)

Make a lineage through time (LTT) plot

Description

lttPlot plots the lineage through time (LTT) of a phylo object. It also adds a reference line connecting the edges of the graph.

Usage

lttPlot(
  phy,
  lwd = 1,
  col = "red",
  plot = TRUE,
  rel.time = FALSE,
  add = FALSE,
  knitr = FALSE
)
lttPlot(
  phy,
  lwd = 1,
  col = "red",
  plot = TRUE,
  rel.time = FALSE,
  add = FALSE,
  knitr = FALSE
)

Arguments

`phy`	A `phylo` object, as specified by the `ape` package.
`lwd`	Line width.
`col`	Line color.
`plot`	A `logical` indicating with calculations should be plotted. If `FALSE`, function returns a list of the calculated points.
`rel.time`	A `logical` indicating how the time scale should be shown. If `FALSE` (default), plots the absolute time since `phy`'s crown age. If `TRUE`, plots time as a relative proportion between crown age and furthest tip from root.
`add`	A `logical` indicating if plot should be added to pre-existing plot. Default is `FALSE`.
`knitr`	Logical indicating if plot is intended to show up in RMarkdown files made by the `Knitr` R package.

Value

Plots the sum of alive lineages per point in time, and adds a red line as a reference of expectation under pure birth. If plot = FALSE, a list the richness of each point in time, and phy's crown age.

Author(s)

Daniel Rabosky, Matheus Januario, Jennifer Auler

References

Paradis, E. (2012). Analysis of Phylogenetics and Evolution with R (Vol. 2). New York: Springer.

Examples


S <- 1
E <- 0
set.seed(1)
phy <- simulateTree(pars = c(S, E), max.taxa = 20, max.t = 5)
lttPlot(phy, knitr = TRUE)
lttPlot(phy, plot = FALSE, knitr = TRUE)

S <- 1
E <- 0
set.seed(1)
phy <- simulateTree(pars = c(S, E), max.taxa = 20, max.t = 5)
lttPlot(phy, knitr = TRUE)
lttPlot(phy, plot = FALSE, knitr = TRUE)

Occurrences of mammal fossils

Description

Many mammal fossil occurrences from different moments of the geological past. Much information (i.e., extra columns) was removed from the original dataset to make it more compact, but it can be fully accessed by the data URL. This dataset is part of the package and is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Usage

mammals_fossil
mammals_fossil

Format

A data.frame containing 69463 rows and 13 columns

phylum: Organism phylum
class: Organism taxonomic class
order: Organism taxonomic order
family: Organism taxonomic family
genus: Organism genus
species: Organism specific name
early_interval: Earlier known geological period of occurrence
late_interval: Later known geological period of occurrence
max_ma: Occurrence's oldest time boundary in million years
min_ma: Occurrence's newest time boundary in million years
midpoint: Midpoint between max_ma and min_ma
lng: Longitude of place where occurrence was found. Follows decimal degree format.
lat: Latitude of place where occurrence was found. Follows decimal degree format.

Source

The Paleobiology Database (downloaded on 2022-03-11).
Data URL: http://paleobiodb.org/data1.2/occs/list.csv?datainfo&rowcount&base_name=Mammalia&show=full,classext,genus,subgenus,acconly,ident,img,etbasis,strat,lith,env,timebins,timecompare,resgroup,ref,ent,entname,crmod

Mammals species list

Description

Mammals species list

Usage

mammals_spp
mammals_spp

Format

A vector containing the names of almost all (i.e., 4099) extant mammal species, following Upham et al (2019) taxonomy. This dataset is part of the package and is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Source

Actual file downloaded from https://vertlife.org/data/

References

Upham, N. S., Esselstyn, J. A., & Jetz, W. (2019). Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS biology, 17(12), e3000494.

Simulating natural selection through time in a bi-allelic gene

Description

NatSelSim simulates natural selection in a bi-allelic gene through n.gen generations.

Usage

NatSelSim(
  w11 = 1,
  w12 = 1,
  w22 = 0.9,
  p0 = 0.5,
  n.gen = 10,
  plot.type = "animateall",
  print.data = FALSE,
  knitr = FALSE
)
NatSelSim(
  w11 = 1,
  w12 = 1,
  w22 = 0.9,
  p0 = 0.5,
  n.gen = 10,
  plot.type = "animateall",
  print.data = FALSE,
  knitr = FALSE
)

Arguments

`w11`	Number giving the fitness of genotype A1A1. Values will be normalized if any genotype fitness exceeds one.
`w12`	Number giving the fitness of genotype A1A2. Values will be normalized if any genotype fitness exceeds one.
`w22`	Number giving the fitness of genotype A2A2. Values will be normalized if any genotype fitness exceeds one.
`p0`	Initial (time = 0) allelic frequency of A1. A2's initial allelic frequency is `1-p0`.
`n.gen`	Number of generation that will be simulated.
`plot.type`	String indicating if plot should be animated. The default, "animateall" animate all possible panels. Other options are "static" (no animation), "animate1", "animate3", or "animate4". Users can animate each panel individually (using `plot.type = "animateX"`, with X being the panel which one wants to animate (so options are "animate1", "animate3", and "animate4" (see return for more info).
`print.data`	Logical indicating whether all simulation results should be returned as a `data.frame`. Default value is `FALSE`.
`knitr`	Logical indicating if plot is intended to show up in RMarkdown files made by the `Knitr` R package.

Details

If any value of fitness (i.e., w11, w12, w22) is larger than one, fitness is interpreted as absolute fitness and values are re-normalized.

Value

If print.data = TRUE, it returns a data.frame containing the number of individuals for each genotype through time. The plots done by the function shows (1) Allele frequency change through time. (2) The adaptive landscape (which remains static during the whole simulation, so can't be animated), (3) Time series of mean population fitness, and (4) Time series of genotypic population frequencies.

Author(s)

Matheus Januario, Jennifer Auler, Dan Rabosky

References

Fisher, R. A. (1930). The Fundamental Theorem of Natural Selection. In: The genetical theory of natural selection. The Clarendon Press

Plutynski, A. (2006). What was Fisher’s fundamental theorem of natural selection and what was it for?. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 37(1), 59-82.

Examples


#using the default values (w11=1, w12=1, w22=0.9, p0=0.5, n.gen=10)
NatSelSim()

# Continuing a simulation for extra time:
# Run the first simulation
sim1=NatSelSim(w11 = .4, w12 = .5, w22 = .4, p0 = 0.35, 
n.gen = 5, plot.type = "static", print.data = TRUE, knitr = TRUE)

# Then take the allelic frequency form the first sim:
new_p0 <- (sim1$AA[nrow(sim1)] + sim1$Aa[nrow(sim1)]*1/2) 
# and use as p0 for a second one:

NatSelSim(w11 = .4, w12 = .5, w22 = .4, p0 = new_p0, n.gen = 5, plot.type = "static", knitr = TRUE)


#using the default values (w11=1, w12=1, w22=0.9, p0=0.5, n.gen=10)
NatSelSim()

# Continuing a simulation for extra time:
# Run the first simulation
sim1=NatSelSim(w11 = .4, w12 = .5, w22 = .4, p0 = 0.35, 
n.gen = 5, plot.type = "static", print.data = TRUE, knitr = TRUE)

# Then take the allelic frequency form the first sim:
new_p0 <- (sim1$AA[nrow(sim1)] + sim1$Aa[nrow(sim1)]*1/2) 
# and use as p0 for a second one:

NatSelSim(w11 = .4, w12 = .5, w22 = .4, p0 = new_p0, n.gen = 5, plot.type = "static", knitr = TRUE)

Simulating one generation of genotypes under Hardy-Weinberg equilibrium

Description

OneGenHWSim creates n.sim simulations of one generation of genotypes under Hardy-Weinberg equilibrium for a bi-allelic loci.

Usage

OneGenHWSim(n.ind = 50, p = 0.5, n.sim = 100)
OneGenHWSim(n.ind = 50, p = 0.5, n.sim = 100)

Arguments

`n.ind`	Integer indicating the census size of the simulated populations. If decimals are inserted, they will be rounded.
`p`	Numerical between zero and one that indicates A1's allele frequency. A2's allele frequency is assumed to be `1-p`.
`n.sim`	Number of simulations to be made. If decimals are inserted, they will be rounded.

Value

A data.frame containing the number of individuals for each genotype.

Author(s)

Matheus Januario, Dan Rabosky, Jennifer Auler

References

Hardy, G. H. (1908). Mendelian proportions in a mixed population. Science, 28, 49–50.

Weinberg, W. (1908). Uber den Nachweis der Vererbung beim Menschen. Jahreshefte des Vereins fur vaterlandische Naturkunde in Wurttemberg, Stuttgart 64:369–382. [On the demonstration of inheritance in humans]. Translation by R. A. Jameson printed in D. L. Jameson (Ed.), (1977). Benchmark papers in genetics, Volume 8: Evolutionary genetics (pp. 115–125). Stroudsburg, PA: Dowden, Hutchinson & Ross.

Mayo, O. (2008). A century of Hardy–Weinberg equilibrium. Twin Research and Human Genetics, 11(3), 249-256.

Examples


#using the default values (n.ind = 50, p = 0.5, n.sim = 100):
OneGenHWSim()

#Simulating with a already fixed allele:
OneGenHWSim(n.ind = 50, p = 1)

# Testing if the simulation works:
A1freq <- .789 #any value could work
n.simul <- 100
simulations <- OneGenHWSim(n.ind = n.simul, n.sim = n.simul, p = A1freq)

#expected:
c(A1freq^2, 2*A1freq*(1-A1freq), (1-A1freq)^2)

#simulated:
apply(X = simulations, MARGIN = 2, FUN = function(x){mean(x)/n.simul})

#using the default values (n.ind = 50, p = 0.5, n.sim = 100):
OneGenHWSim()

#Simulating with a already fixed allele:
OneGenHWSim(n.ind = 50, p = 1)

# Testing if the simulation works:
A1freq <- .789 #any value could work
n.simul <- 100
simulations <- OneGenHWSim(n.ind = n.simul, n.sim = n.simul, p = A1freq)

#expected:
c(A1freq^2, 2*A1freq*(1-A1freq), (1-A1freq)^2)

#simulated:
apply(X = simulations, MARGIN = 2, FUN = function(x){mean(x)/n.simul})

Plot NatSelSim output

Description

Plot NatSelSim output

Usage

plotNatSel(
  gen.HW = gen.HW,
  p.t = p.t,
  w.t = w.t,
  t = t,
  W.gntp = c(w11, w12, w22),
  plot.type = "animateall",
  knitr = FALSE
)
plotNatSel(
  gen.HW = gen.HW,
  p.t = p.t,
  w.t = w.t,
  t = t,
  W.gntp = c(w11, w12, w22),
  plot.type = "animateall",
  knitr = FALSE
)

Arguments

`gen.HW`	Dataframe with A1A1, A1A2 and A2A2 genotypic frequencies in each generation (nrows = NGen)
`p.t`	Allelic frequency through time
`w.t`	Mean population fitness through time
`t`	time
`W.gntp`	Initial genotypic fitness
`plot.type`	String indicating if plot should be animated. The default, "animateall", animate all possible panels. Other options are "static", "animate1", "animate3", or "animate4".
`knitr`	Logical indicating if plot is intended to show up in RMarkdown files made by the `Knitr` R package.

Value

Plot of NatSelSim's output (see NatSelSim's help page for details).

Plotting the whale phylogeny and coloring its clades

Description

plotPaintedWhales plots the phylogeny from Steeman et al (2011), coloring the Dolphins (Delphinidae), porpoises (Phocoenidae), the Mysticetes, the baleen whales (Balaenopteridae), and the Beaked whales (Ziphiidae).

Usage

plotPaintedWhales(
  show.legend = TRUE,
  direction = "rightwards",
  knitr = FALSE,
  ...
)
plotPaintedWhales(
  show.legend = TRUE,
  direction = "rightwards",
  knitr = FALSE,
  ...
)

Arguments

`show.legend`	Logical indicating if clade legend should be shown.
`direction`	Phylogeny plotting direction. Should be set to "rightwards"
`knitr`	Logical indicating if plot is intended to show up in RMarkdown files made by the `Knitr` R package. (the default) or "leftwards".
`...`	other arguments to be passed to `phytools::plotSimmap`

Value

The whale phylogeny, with branch lengths being colored by a major whale taxonomic group.

Author(s)

Matheus Januario, Jennifer Auler

References

Examples

plotPaintedWhales(knitr = TRUE)

plotPaintedWhales(knitr = TRUE)

Plot protein sequence(s)

Description

plotProteinSeq draws the sequences of proteins within the same "ProteinSeq" object. In this format, more similar sequences will have similar banding patterns.

Usage

plotProteinSeq(x, taxon.to.plot, knitr = FALSE)
plotProteinSeq(x, taxon.to.plot, knitr = FALSE)

Arguments

`x`	A "ProteinSeq" object containing proteins from `taxon1` and `taxon2`.
`taxon.to.plot`	A character vector providing the common name of the species that will be plotted. Must be a name present in `x`.
`knitr`	Logical indicating if plot is intended to show up in RMarkdown files made by the `Knitr` R package.

Value

A draw of the protein sequence(s) provided. Colors refer to specific amino acids ("R", "W", "I", "F", "S", "T", "N", "H", "K", "D", "G", "L", "Y", "V", "M", "A", "E", "P", "Q", "C")", "gaps/space in the sequence ("-"), ambiguous amino acid ("B" - often representing either asparagine ("N") or aspartic acid ("D")), or another marker for ambiguous amino acid ("X").

Author(s)

Matheus Januario, Jennifer Auler

Examples

data(cytOxidase)
plotProteinSeq(cytOxidase, c("human", "chimpanzee", "cnidaria"), knitr = TRUE)

data(cytOxidase)
plotProteinSeq(cytOxidase, c("human", "chimpanzee", "cnidaria"), knitr = TRUE)

Plot a literal interpretation of a fossil record

Description

plotRawFossilOccs calculates and plots the early and late boundaries associated with each taxa in a dataset.

Usage

plotRawFossilOccs(
  data,
  tax.lvl = NULL,
  sort = TRUE,
  use.midpoint = TRUE,
  return.ranges = FALSE,
  knitr = FALSE
)
plotRawFossilOccs(
  data,
  tax.lvl = NULL,
  sort = TRUE,
  use.midpoint = TRUE,
  return.ranges = FALSE,
  knitr = FALSE
)

Arguments

`data`	A `data.frame` containing fossil data on the age (early and late bounds of rock layer, respectively labeled as `max_ma` and `min_ma`) and the taxonomic level asked in `tax_lv`.
`tax.lvl`	A `character` giving the taxonomic in which calculations will be based on, which must refer to the column names in `data`. If `NULL` (default value), the function plots every individual occurrences in `data`.
`sort`	`logical` indicating if taxa should be sorted by their `max_ma` values (default value is `TRUE`). Otherwise (i.e., if `FALSE`), function will follow the order of taxa (or occurrences) inputted in `data`.
`use.midpoint`	`logical` indicating if function should use occurrence midpoints (between `max_ma` and `min_ma`) as occurrence temporal boundaries, a method commonly employed in paleobiology to remove noise related to extremely coarse temporal resolution due to stratification. This argument is only used if a `tax.lvl` is provided.
`return.ranges`	`logical` indicating if ranges calculated by function should be return as a `data.frame`. If `tax.lvl` is `NULL`, the function don't calculate ranges and so it has nothing to return.
`knitr`	Logical indicating if plot is intended to show up in RMarkdown files made by the `Knitr` R package.

Value

Plots a pile of the max-min temporal ranges of the chosen tax.lvl. This usually will be stratigraphic ranges for occurrences (so there is no attempt to estimate "true" ranges), and if tax.lvl = NULL (the default), occurrences are drawn as ranges of stratigraphic resolution (= the fossil dating imprecision). If return.ranges = TRUE, it returns a data.frame containing the diversity (column div) of the chosen taxonomic level, through time.

Author(s)

Matheus Januario, Jennifer Auler

Examples


data("dinos_fossil")
oldpar <- par(no.readonly = TRUE) 
par(mfrow=c(1,2))
plotRawFossilOccs(dinos_fossil, tax.lvl = "species", knitr = TRUE)
plotRawFossilOccs(dinos_fossil, tax.lvl = "genus", knitr = TRUE)
par(oldpar)

data("dinos_fossil")
oldpar <- par(no.readonly = TRUE) 
par(mfrow=c(1,2))
plotRawFossilOccs(dinos_fossil, tax.lvl = "species", knitr = TRUE)
plotRawFossilOccs(dinos_fossil, tax.lvl = "genus", knitr = TRUE)
par(oldpar)

Plot WFDriftSim output

Description

Plot WFDriftSim output

Usage

plotWFDrift(p.through.time, plot.type = plot, knitr = FALSE)
plotWFDrift(p.through.time, plot.type = plot, knitr = FALSE)

Arguments

`p.through.time`	Matrix with n.gen columns and n.sim lines
`plot.type`	String. Options are "static" or "animate"
`knitr`	Logical indicating if plot is intended to show up in RMarkdown files made by the `Knitr` R package.

Value

A static or animated plot of populations under genetic drift through time

Examples

store_p = WFDriftSim(Ne = 5, n.gen = 10, p0=.2, n.sim=5, plot = "none", print.data = TRUE)
plotWFDrift(store_p, "static")
store_p = WFDriftSim(Ne = 5, n.gen = 10, p0=.2, n.sim=5, plot = "none", print.data = TRUE)
plotWFDrift(store_p, "static")

Details, generics, and methods for the `ProteinSeq` class

Description

The ProteinSeq class is an input for the functions countSeqDiffs and is.ProteinSeq. It consists of a character vector. Each entry in this vector represents the aminoacid (the protein components coded by a gene) sequence, for a given aligned protein sequence. The object must be a character, named vector, with the names typically corresponding to the species (name could be scientific or common name) from which every sequence came. The characters within the vector must correspond to valid aminoacid symbols (i.e. capitalized letters or deletion "_" symbols). Particularly, the following symbols relate to amino acids: "A", "C", "D", "E", "F", "G", "H", "I", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y".

Importantly, the symbol "_" means an indel (insertion or deletion), and the symbols "X", "B", "Z", "J" should be considered as ambiguous site readings.

Usage

is.ProteinSeq(x)

## S3 method for class 'ProteinSeq'
print(x, ...)

## S3 method for class 'ProteinSeq'
summary(object, ...)

## S3 method for class 'ProteinSeq'
head(x, n = 20, ...)

## S3 method for class 'ProteinSeq'
tail(x, n = 20, ...)
is.ProteinSeq(x)

## S3 method for class 'ProteinSeq'
print(x, ...)

## S3 method for class 'ProteinSeq'
summary(object, ...)

## S3 method for class 'ProteinSeq'
head(x, n = 20, ...)

## S3 method for class 'ProteinSeq'
tail(x, n = 20, ...)

Arguments

`x`	an object of the class `ProteinSeq`
`...`	arguments to be passed to or from other methods. #' @return Shows the last `n` elements of a `ProteinSeq` object.
`object`	an object of the class `ProteinSeq`
`n`	number of aminoacids to be shown

Details

is.ProteinSeq A ProteinSeq must be a list containing multiple vectors made of characters (usually letters that code to Amino Acids, deletions, etc). All of these must have the correct length (i.e. same as all the others) and their relative positions should match (i.e., the object must contain alligned Amino acide sequences).

Value

A logical indicating if object x is of class ProteinSeq

print.ProteinSeq Prints a brief summary of a print.ProteinSeq containing the number of sequences and the length of the alignment. See more details of the format in ??ProteinSeq.

Same as print.ProteinSeq.

Shows the first n elements of a ProteinSeq object.

Author(s)

Daniel Rabosky, Matheus Januario, Jennifer Auler

Simulating richness through birth-death processes

Description

simulateBirthDeathRich calculates the number of species at a certain point in time, following a birth-death process.

Usage

simulateBirthDeathRich(t, S = NULL, E = NULL, K = NULL, R = NULL)
simulateBirthDeathRich(t, S = NULL, E = NULL, K = NULL, R = NULL)

Arguments

`t`	Point in time which richness will be simulated.
`S`	A numeric representing the per-capita speciation rate (in number of events per lineage per million years). Must be larger than `E`.
`E`	A numeric representing the per-capita extinction rate (in number of events per lineage per million years). Must be smaller than `S`.
`K`	A numeric representing the extinction fraction (i.e., `K` = `E` / `S`). Must be either zero or a positive which is number smaller than one.
`R`	A numeric representing the per-capita Net Diversification rate (i.e., `R` = `S` - `E`). Must be a positive number.

Details

The function only accepts as inputs S and E, or K and R.

Value

The number of simulated species (i.e., the richness).

Author(s)

Matheus Januario, Daniel Rabosky, Jennifer Auler

References

Raup, D. M. (1985). Mathematical models of cladogenesis. Paleobiology, 11(1), 42-52.

Examples

# running a single simulation:
SS <- 0.40
EE <- 0.09
tt <- 10 #in Mya
simulateBirthDeathRich(t = tt, S = SS, E = EE)

#running many simulations and graphing results:
nSim <- 1000
res <- vector()
for(i in 1:nSim){
  res <- c(res, 
  simulateBirthDeathRich(t = tt, S = SS, E = EE))
}
plot(table(res)/length(res),
     xlab="Richness", ylab="Probability")
# running a single simulation:
SS <- 0.40
EE <- 0.09
tt <- 10 #in Mya
simulateBirthDeathRich(t = tt, S = SS, E = EE)

#running many simulations and graphing results:
nSim <- 1000
res <- vector()
for(i in 1:nSim){
  res <- c(res, 
  simulateBirthDeathRich(t = tt, S = SS, E = EE))
}
plot(table(res)/length(res),
     xlab="Richness", ylab="Probability")

Simulating a phylogenetic trees through the birth-death process

Description

simulateTree uses a birth-death process to simulate a phylogenetic tree, following the format of ape package's phylo object. The function is basically a wrapper for the diversitree's tree.bd function.

Usage

simulateTree(
  pars,
  max.taxa = Inf,
  max.t,
  min.taxa = 2,
  include.extinct = FALSE
)
simulateTree(
  pars,
  max.taxa = Inf,
  max.t,
  min.taxa = 2,
  include.extinct = FALSE
)

Arguments

`pars`	`numeric` vector with the simulation parameters: speciation (first slot) and extinction (second slot) rates, respectively. Should follow any formats stated in the function `tree.bd` from the `diversitree` package.
`max.taxa`	Maximum number of taxa to include in the tree. If `Inf`, then the tree will be evolved until `max.t` time has passed.
`max.t`	Maximum length to evolve the phylogeny over. If equal to `Inf`, then the tree will evolve until `max.taxa` extant taxa are present.
`min.taxa`	Minimum number of taxa to include in the tree.
`include.extinct`	A `logical` indicating if extinct taxa should be included in the final phylogeny.

Details

see help page from diversitree::tree.bd

Value

A phylo object

Author(s)

Daniel Rabosky, Matheus Januario, Jennifer Auler

References

Paradis, E. (2012). Analysis of Phylogenetics and Evolution with R (Vol. 2). New York: Springer.

Popescu, A. A., Huber, K. T., & Paradis, E. (2012). ape 3.0: New tools for distance-based phylogenetics and evolutionary analysis in R. Bioinformatics, 28(11), 1536-1537.

FitzJohn, R. G. (2010). Analysing diversification with diversitree. R Packag. ver, 9-2.

FitzJohn, R. G. (2012). Diversitree: comparative phylogenetic analyses of diversification in R. Methods in Ecology and Evolution, 3(6), 1084-1092.

Examples


S <- 1
E <- 0
set.seed(1)
phy <- simulateTree(pars = c(S, E), max.taxa = 6, max.t=Inf)
ape::plot.phylo(phy)
ape::axisPhylo()

# alternatively, we can stop the simulation using time:
set.seed(42)
phy2 <- simulateTree(pars = c(S, E), max.t=7)
ape::plot.phylo(phy2)
ape::axisPhylo()

S <- 1
E <- 0
set.seed(1)
phy <- simulateTree(pars = c(S, E), max.taxa = 6, max.t=Inf)
ape::plot.phylo(phy)
ape::axisPhylo()

# alternatively, we can stop the simulation using time:
set.seed(42)
phy2 <- simulateTree(pars = c(S, E), max.t=7)
ape::plot.phylo(phy2)
ape::axisPhylo()

Fossil Time series

Description

Values of clade diversity for many clades of organisms (note some clades are nested within other clades in the dataset). This dataset is part of the package and is licensed =under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Usage

timeseries_fossil
timeseries_fossil

Format

A data.frame with 598 rows and 6 columns.

clade: Time series clade
source: Primary source of the Time series
stem_age: Stem age of clade
rel_time: Geological relative time (in Million years ago relative to present)
time_ma: Geological time in million years since clade stem age
richness: Number of species at given geological time

Details

Legend:
anth = Anthozoa (Cnidaria);
art = Articulata (Crinoidea, Echinodermata);
biv = Bivalvia (Mollusca);
bryo = Bryozoa (Lophotrochozoa, Ectoprocta);
ceph = Cephalopoda (Mollusca);
chon = Chondrocytes (Chordata);
crin = Crinoidea (Echinodermata);
dinosauria = Dinosauria (Chordata);
ech = Echinoidea (Echinodermata);
foram = Foraminifera (Retaria);
gast = Gastropoda (Mollusca);
graptoloids = Graptolites (Graptolithina);
ling = Ligulata (Brachiopoda);
ostr = Ostracoda (Crustacea, Arthropoda);
tril = Trilobita (Arthropoda).

Source

Data originally compiled from many primary sources. Organized, curated by, and downloaded from, Rabosky & Benson (2021).

References

Rabosky, D. L., & Benson, R. B. (2021). Ecological and biogeographic drivers of biodiversity cannot be resolved using clade age-richness data. Nature Communications, 12(1), 2945.

Occurrence of trilobite fossils

Description

Many trilobite fossil occurrences from different moments of the geological past. Much information (i.e., extra columns) was removed from the original dataset to make it more compact, but it can be fully accessed by the data URL. This dataset is part of the package and is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Usage

trilob_fossil
trilob_fossil

Format

A data.frame containing 24965 rows and 13 columns

phylum: Organism phylum
class: Organism taxonomic class
order: Organism taxonomic order
family: Organism taxonomic family
genus: Organism genus
species: Organism specific name
early_interval: Earlier known geological period of occurrence
late_interval: Later known geological period of occurrence
max_ma: Occurrence's oldest time boundary in million years
min_ma: Occurrence's newest time boundary in million years
midpoint: Midpoint between max_ma and min_ma
lng: Longitude of place where occurrence was found. Follows decimal degree format.
lat: Latitude of place where occurrence was found. Follows decimal degree format.

Source

The Paleobiology Database (downloaded on 2022-03-11).
Data URL: http://paleobiodb.org/data1.2/occs/list.csv?datainfo&rowcount&base_name=Trilobita&show=full,classext,genus,subgenus,acconly,ident,img,etbasis,strat,lith,env,timebins,timecompare,resgroup,ref,ent,entname,crmod

Simulating generations of genetic drift in a Wright–Fisher (WF) population

Description

WFDriftSim simulates genetic drift of diploid Wright–Fisher populations with a given effective population size through a certain number of generations.

Usage

WFDriftSim(
  Ne,
  n.gen,
  p0 = 0.5,
  n.sim = 1,
  plot.type = "animate",
  print.data = FALSE,
  knitr = FALSE
)
WFDriftSim(
  Ne,
  n.gen,
  p0 = 0.5,
  n.sim = 1,
  plot.type = "animate",
  print.data = FALSE,
  knitr = FALSE
)

Arguments

`Ne`	Number giving the effective population size of the population
`n.gen`	Number of generations to be simulated.
`p0`	Initial frequency of a given allele. As the simulated organism is diploid, the other alleles frequency will be `1-(p0)`. Default value is `0.5`.
`n.sim`	Number of simulations to be made. If decimals are inserted, they will be rounded. Default value is `1`.
`plot.type`	Character indicating if simulations should be plotted as colored lines. Each color represents a different population. If `plot.type = "animate"` (default value) it animates each generation individually. If `plot.type = "static"` it plots all lines rapidly. If `plot.type = "none"` nothing is plotted.
`print.data`	Logical indicating whether all simulation results should be returned as a `data.frame`. Default value is `FALSE`.
`knitr`	Logical indicating if plot is intended to show up in RMarkdown files made by the `Knitr` R package.

Details

The effective population size (Ne) is strongly connected with the rate of genetic drift (for details, see Waples, 2022).

Value

If plot.type = "static" or "animate", plots the timeseries of all simulations, with each line+color referring to a different simulation. Note that if many simulations (generally more than 20) are simulated, colors might be cycled and different simulation will have the same color. If print.data = TRUE, returns a data.frame with the simulation results.

Author(s)

Matheus Januario, Dan Rabosky, Jennifer Auler

References

Fisher RA (1922) On the dominance ratio. Proc. R. Soc. Edinb 42:321–341

Kimura M (1955) Solution of a process of random genetic drift with a continuous model. PNAS–USA 41(3):144–150

Tran, T. D., Hofrichter, J., & Jost, J. (2013). An introduction to the mathematical structure of the Wright–Fisher model of population genetics. Theory in Biosciences, 132(2), 73-82. [good for the historical review, math can be challenging]

Waples, R. S. (2022). What is Ne, anyway?. Journal of Heredity.

Wright S (1931) Evolution in Mendelian populations. Genetics 16:97–159

Examples


#Default values:
WFDriftSim(Ne = 5, n.gen = 10, knitr = TRUE)

#A population which has already fixed one of the alleles:
WFDriftSim(Ne = 5, n.gen = 10, p0=1, knitr = TRUE)

#Many populations::
WFDriftSim(Ne = 5, n.gen = 10, p0=0.2, n.sim=10, knitr = TRUE)

######## continuing a previous simulation:
n.gen_1stsim <- 10 # number of gens in the 1st sim:
sim1 <- WFDriftSim(Ne = 5, n.gen = n.gen_1stsim, p0=.2, n.sim=10, 
plot.type = "none", print.data = TRUE, knitr = TRUE)
n.gen_2ndsim <-7 # number of gens in the 2nd sim:
# now, note how we assigned p0:
sim2 <- WFDriftSim(Ne = 5, n.gen = n.gen_2ndsim, p0=sim1[,ncol(sim1)], 
plot.type = "static", n.sim=10, print.data = TRUE, knitr = TRUE)

# if we want to merge both simulations, then we have to:
# remove first column of 2nd sim (because it repeats
# the last column of the 1st sim)
sim2 <- sim2[,-1]

# re-name 2nd sim columns:
colnames(sim2) <- paste0("gen", (n.gen_1stsim+1):(n.gen_1stsim+n.gen_2ndsim))

#finally, merging both rounds of simulations:
all_sims <- cbind(sim1, sim2)
head(all_sims)


#Default values:
WFDriftSim(Ne = 5, n.gen = 10, knitr = TRUE)

#A population which has already fixed one of the alleles:
WFDriftSim(Ne = 5, n.gen = 10, p0=1, knitr = TRUE)

#Many populations::
WFDriftSim(Ne = 5, n.gen = 10, p0=0.2, n.sim=10, knitr = TRUE)

######## continuing a previous simulation:
n.gen_1stsim <- 10 # number of gens in the 1st sim:
sim1 <- WFDriftSim(Ne = 5, n.gen = n.gen_1stsim, p0=.2, n.sim=10, 
plot.type = "none", print.data = TRUE, knitr = TRUE)
n.gen_2ndsim <-7 # number of gens in the 2nd sim:
# now, note how we assigned p0:
sim2 <- WFDriftSim(Ne = 5, n.gen = n.gen_2ndsim, p0=sim1[,ncol(sim1)], 
plot.type = "static", n.sim=10, print.data = TRUE, knitr = TRUE)

# if we want to merge both simulations, then we have to:
# remove first column of 2nd sim (because it repeats
# the last column of the 1st sim)
sim2 <- sim2[,-1]

# re-name 2nd sim columns:
colnames(sim2) <- paste0("gen", (n.gen_1stsim+1):(n.gen_1stsim+n.gen_2ndsim))

#finally, merging both rounds of simulations:
all_sims <- cbind(sim1, sim2)
head(all_sims)

Whale Phylogeny

Description

An ultrametric phylogenetic tree of the living cetaceans. Phylogeny generated by Steeman et al (2009). This dataset is part of the package and is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Usage

whale_phylo
whale_phylo

Format

An ultrametric phylo object with 87 tips

Source

Original phylogeny generation by Steeman et al (2001). File obtained from Rabosky et al, 2014.

Package 'evolved'

Help Index

Occurrences of ammonoid fossils

Description

Usage

Format

Source

Birds species list

Description

Usage

Format

Source

References

Calculate paleo diversity curves through different methods

Description

Usage

Arguments

Value

Author(s)

References

Examples

Find and fix small rounding errors in ultrametric trees

Description

Usage

Arguments

Value

Author(s)

References

Examples

Counting protein sequence differences

Description

Usage

Arguments

Value

Author(s)

Examples

Cytochrome Oxidase sequences

Description

Usage

Format

Details

Source

Whale body size and speciation rates

Description

Usage

Format

Details

Source

References

Occurrence of dinosaur fossils

Description

Usage

Format

Source

Estimate speciation assuming a pure-birth process

Description

Usage

Arguments

Value

Author(s)

References

Examples

Fit a constant-rate birth-death process to a phylogeny

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Make a lineage through time (LTT) plot

Description

Usage

Arguments

Value

Author(s)

References

Examples

Occurrences of mammal fossils

Details, generics, and methods for the `ProteinSeq` class