phylocurve 1.0.0 [source] [manual] [

phylocurve 0.1.1 [source] [manual] [

**Verson 1.3.0**.

**Verson 1.0.0**. Initial upload to CRAN. This version update implements covariance-based REML implementations of distance-based methods for assessing multivairate evolutionary rates, phylogenetic signal, and performing PGLS (Adams 2014a-c). NOTE: Not backward-compatible with versions 0.1.0 - 0.1.1.

New functions include `sim.mult()`

,
`K.mult()`

, `rate.mult()`

, `compare.rate.mult()`

, `compare.multivar.rate.mult()`

,
and `pgls.mult()`

. These functions can incorporate alternative evolutionary models, incorporate within-species variation, and allow missing data. These methods are appropriate for both function-valued and other high-dimensional multivariate traits (provided traits are appropriately aligned). Here's an example implementing the new functions incorporating within-species variation, and missing data.
`data <- sim.mult(nspecies = 50,R = diag(5),error = rep(1,5),nreps = 5,nmissing = 100,seed = 1)`

rate.mult.fitted <- rate.mult(tree = data$tree,Y = data$Y_raw,type = "mult",error = "estimate")

groups <- setNames(factor(c(rep("a",25),rep("b",25)),levels=c("a","b")),nm = data$tree$tip.label)

# Compare evolutionary rates on the tree

compare.rate.mult(rate.mult.fitted = rate.mult.fitted,groups = groups)

# Multivariate phylogenetic signal

K.mult(rate.mult.fitted)

# Multivariate PGLS

require(phytools)

X <- as.matrix(fastBM(data$tree))

pgls.mult(rate.mult.fitted = rate.mult.fitted,X = X)

# To compare rate among trait dimensions, the null hypothesis was stored above in rate.mult.fitted

# Suppose we want to test an alternative hypothesis that dimensions 1:3 evolve under a different rate than 4:5

alt1 <- rate.mult(data$tree,Y = data$Y_raw[,c(1,2:4)],type = "mult",error = "estimate")

alt2 <- rate.mult(data$tree,Y = data$Y_raw[,c(1,5:6)],type = "mult",error = "estimate")

alt_list <- list(alt1,alt2)

compare.multivar.rate.mult(null_model = rate.mult.fitted,alt_model_list = alt_list)

Added a phylocurve Version 0.1.1 PDF manual. Also adjusted internal functions and package dependencies.

Initial launch of phylocurve R package. Included functions are `simcurves`

, `get_tip_coefficients`

, `phylocurve`

, `phylocurve.pgls`

, and `phylocurve.signal`

. Here's some example code to demonstrate basic functionality:

`require(phylocurve)`

require(phytools)

# simulate evolution of a function-valued trait (glm with logit link)

sim_data <- simcurves()

# ancestral curve reconstruction

anc_recon <- phylocurve(formula = y~x,tree = sim_data$tree,data = sim_data$data)

# estimate phylogenetic signal

phylocurve.signal(tip_coefficients = anc_recon$tip_coefficients,tree = sim_data$tree)

# assess correlated trait evolution with a univariate trait

X <- fastBM(sim_data$tree)

phylocurve.pgls(tip_coefficients = anc_recon$tip_coefficients,univariate_trait = X,tree = sim_data$tree)