diff --git a/DESCRIPTION b/DESCRIPTION
index 24db611..ca712fb 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: AutoPop
Title: Autopolyploid population simulations
-Version: 1.0.0.0000
+Version: 1.0.0.1
Authors@R:
c(person(given = "Michelle", family ="Gaynor", middle = "L", email = "shellyleegaynor@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-3912-6079")),
@@ -18,7 +18,7 @@ Description: R-based joint-dynamic population simulation for
License: GPL (>= 3)
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
Suggests:
testthat (>= 3.0.0)
Imports:
diff --git a/NAMESPACE b/NAMESPACE
index edc6254..8d1ad8d 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,8 +1,10 @@
# Generated by roxygen2: do not edit by hand
importFrom(MASS,ginv)
+importFrom(MASS,mvrnorm)
importFrom(stats,dhyper)
importFrom(stats,na.omit)
+importFrom(stats,pnorm)
importFrom(stats,rbeta)
importFrom(stats,rbinom)
importFrom(stats,rmultinom)
diff --git a/R/alphabeta.calc.R b/R/alphabeta.calc.R
index 0dc1941..d333208 100644
--- a/R/alphabeta.calc.R
+++ b/R/alphabeta.calc.R
@@ -1,8 +1,9 @@
#' Mature survival - Calculates alpha and beta from mean and variance.
#'
-#' @description Calculates the alpha and beta parameters based on mu, the mean probability of mature survival, and the calculated variance.
+#' @description Calculates the alpha and beta parameters based on mu, here the mean probability of mature survival,
+#' and s calculated variance.
#'
-#' @param mu Mean probability of mature survival. Must be a single integer between 0 and 1.
+#' @param mu Sample mean. Must be a single integer between 0 and 1.
#' @param var Derived variance based on var.option. Must be a single integer between 0 and 1.
#'
#' @returns List with the parameters alpha and beta.
diff --git a/R/env.copula.R b/R/env.copula.R
new file mode 100644
index 0000000..25228ed
--- /dev/null
+++ b/R/env.copula.R
@@ -0,0 +1,32 @@
+#' Environmental Copula - Calculates correlation probability.
+#'
+#' @description Calculates the probability coefficient which allow cytotype and stage correlations
+#'
+#'
+#' @param rho Correlation coefficient. Must be a single integer between 0 and 1.
+#' @inheritParams gen.iter.f.choosy
+#'
+#' @returns A list of two matrix: X and U. Here, X is the matrix which was simulated from
+#' a multivariate normal distribution. The U matrix is simply the X matrix transformed into a uniform distribution.
+#' Each matrix has 3 columns, one for each cytotype. The length of the matrix matches the number of generations provided.
+#'
+#' @importFrom MASS mvrnorm
+#' @importFrom stats pnorm
+#'
+
+
+env.copula <- function(rho, generations){
+ # Create covariance matrix
+ n <- 3 # Three cytotypes
+ mean <- rep(0,n)
+ # covariance matrix for the cytotypes
+ SIGMA <- ((1-rho)*diag(n) + rho*matrix(1, nrow = n, ncol = n))
+ # Simulate from a multivariate normal distribution
+ X <- MASS::mvrnorm(n = generations, mu = mean, Sigma = SIGMA)
+ # Transform to uniform using pnorm, which is the cdf for a normal distribution
+ U <- pnorm(X)
+ out <- list(X, U)
+ return(out)
+}
+
+
diff --git a/R/form.autopop.R b/R/form.autopop.R
index bf27b51..d907a52 100644
--- a/R/form.autopop.R
+++ b/R/form.autopop.R
@@ -26,10 +26,10 @@
#' * C2: relative abundance of all diploids (ie. sum2x/sum).
#' * C3: relative abundance of all triploids (ie. sum3x/sum).
#' * C4: relative abundance of all tetraploids (ie. sum4x/sum)
-#'
+
form.autopop <- function(popvect, generations){
- plot.pop <- as.data.frame(do.call(rbind, popvect))
+ plot.pop <- as.data.frame(do.call(rbind, popvect))[,1:12]
plot.pop$gen <- 1:(generations + 1)
plot.pop[is.na(plot.pop)] = 0
diff --git a/R/gen.iter.f.choosy.R b/R/gen.iter.f.choosy.R
index 36cd880..7b4aa84 100644
--- a/R/gen.iter.f.choosy.R
+++ b/R/gen.iter.f.choosy.R
@@ -19,6 +19,7 @@
#' @inheritParams one.iter.f.choosy
#' @inheritParams cytotype_repro_mate
#' @inheritParams form.autopop
+#' @inheritParams env.copula
#'
#' @returns A single data frame as defined by `form.autopop()`. Each row is a generation. The columns are as follows,
#' * V1: number of immature diploids.
@@ -47,42 +48,69 @@
#' * C2: relative abundance of all diploids (sum2x/sum).
#' * C3: relative abundance of all triploids (sum3x/sum).
#' * C4: relative abundance of all tetraploids (sum4x/sum)
-#'
-#'
-
+#' * i1e: diploid immature survival probability during t - 1.
+#' * i2e: triploid immature survival probability during t - 1.
+#' * i3e: tetraploid immature survival probability during t - 1.
+#' * m1e: diploid mature survival probability during t - 1.
+#' * m2e: triploid mature survival probability during t - 1.
+#' * m3e: tetraploid mature survival probability during t - 1.
#source("R/one-iter-f-choosy.R")
#source("R/format-iter-choosy.R")
#source("R/mature.surv.calc.R")
-gen.iter.f.choosy <- function(generations, init.pop, env.ci, aii.vec,
+gen.iter.f.choosy <- function(generations, init.pop, env.ci, env.sigma, aii.vec,
as.matur, as.msurv, d, gnum.base,
- b, cc, s, mc, density.type = "all",
- mate.lazy = FALSE){
+ b, cc, s, mc,
+ gam.density.type = "all", is.density.type = "all", rho, mate.lazy = FALSE,
+ rj = c(1,1), yj = c(1,1), env.immature.survival = FALSE){
## Set up first generation
popvect <- list()
- first.vec <- c(0, 0, 0, init.pop, 0, 0, 0, 0, 0, 0, 0, 0)
+ first.vec <- c(0, 0, 0, init.pop, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
popvect[[1]] <- first.vec
- as.msurv.e <- list()
- as.msurv.e[[1]] <- as.msurv
-
+ as.msurv.e <- list()
+ as.msurv.e[[1]] <- c(0,0,0)
+ Xi.Ui.vec <- env.copula(rho = rho, generations = generations)
+ Xi.vec <- Xi.Ui.vec[[1]]
+ Ui.vec <- Xi.Ui.vec[[2]]
+ if(sum(as.msurv) != 0){
+ msurv.alpha.beta <- surv.shape(env.ci = env.ci, raw.means = as.msurv)
+ }
## Loop for set number of generations #########################################
for(i in 1:generations){
- # Environmental. Stochasticity
- m1e <- mature.surv.calc(env.ci = env.ci, as.msurv = as.msurv.e[[1]][1])
- m2e <- mature.surv.calc(env.ci = env.ci, as.msurv = as.msurv.e[[1]][2])
- m3e <- mature.surv.calc(env.ci = env.ci, as.msurv = as.msurv.e[[1]][3])
+ if(sum(as.msurv) != 0){
+ # Mature Survival - Environmental. Stochasticity
+ m1e <- qbeta(Ui.vec[i,1], msurv.alpha.beta[1,1], msurv.alpha.beta[2,1])
+ m2e <- qbeta(Ui.vec[i,2], msurv.alpha.beta[1,2], msurv.alpha.beta[2,2])
+ m3e <- qbeta(Ui.vec[i,3], msurv.alpha.beta[1,3], msurv.alpha.beta[2,3])
as.msurv.e[[i + 1]] <- c(m1e, m2e, m3e)
+ } else{
+ as.msurv.e[[i + 1]] <- c(0, 0, 0)
+ }
ct.vec <- popvect[[i]]
- popvect[[i + 1]] <- one.iter.f.choosy(ct.vec, aii.vec = aii.vec , as.matur = as.matur,
+ popvect[[i + 1]] <- one.iter.f.choosy(ct.vec, aii.vec = aii.vec, as.matur = as.matur,
as.msurv.e.set = as.msurv.e[[i + 1]], d = d, gnum.base = gnum.base,
- b = b, cc = cc, s = s, mc = mc, density.type, mate.lazy = mate.lazy)
+ b = b, cc = cc, s = s, mc = mc, mate.lazy = mate.lazy,
+ env.sigma = env.sigma, Xi.is = Xi.vec[i,], gam.density.type = gam.density.type,
+ is.density.type = is.density.type,
+ rj = rj,
+ yj = yj, env.immature.survival = env.immature.survival)
}
onedf <- form.autopop(popvect, generations)
+ survival_rates <- as.data.frame(do.call(rbind, popvect))[1:(generations+1),13:15]
+ for(i in 1:generations){
+ onedf$i1e[i] <- survival_rates$V13[i]
+ onedf$i2e[i] <- survival_rates$V14[i]
+ onedf$i3e[i] <- survival_rates$V15[i]
+ onedf$m1e[i] <- as.msurv.e[[i]][1]
+ onedf$m2e[i] <- as.msurv.e[[i]][2]
+ onedf$m3e[i] <- as.msurv.e[[i]][3]
+ }
+
return(onedf)
}
diff --git a/R/mu.options.R b/R/mu.options.R
new file mode 100644
index 0000000..ec3d0d4
--- /dev/null
+++ b/R/mu.options.R
@@ -0,0 +1,30 @@
+#' Max mean allowed
+#'
+#' Given an env.ci value, we identified allowed means between 0 and 1.
+#'
+#' @param env.ci Proportion of environmental variance used to define mature survival rate per generation
+#' with a beta distribution. This number must be in between 0 and 1, but cannot be equal to 0 or 1.
+#'
+#' @return All values allowed and not allowed.
+#'
+
+mu.options <- function(env.ci){
+ p.mu <- seq(0.001,1, by = 0.0001)
+ notallowed <- c()
+ allowed <- c()
+ for(i in 1:length(p.mu)){
+
+ mu <- p.mu[i]
+ var <- (env.ci*(mu*(1-mu)))
+ less <- (mu*(mu-1))
+
+ if((var > less) == FALSE){
+ notallowed <- c(notallowed, mu)
+ }else{
+ allowed <- c(allowed, mu)
+ }
+ }
+ out <- list(not_allowed = notallowed, allowed = allowed)
+ return(out)
+}
+
diff --git a/R/one.iter.f.choosy.R b/R/one.iter.f.choosy.R
index 8bafa60..134d6b0 100644
--- a/R/one.iter.f.choosy.R
+++ b/R/one.iter.f.choosy.R
@@ -9,21 +9,36 @@
#'
#' @param ct.vec Population composition at time t.
#' Indicates the sum of each type at time t, `ct.vec = c(2x_immature, 3x_immature, 4x_immature, 2x_mature, 3x_mature, 4x_mature)`
-#' @param aii.vec The survival probability of an immature individual for each cytotype. Must be a list of three integers between 0 and 1. For example, `aii.vec = c(0.5, 0.3, 0.5)`.
-#' @param as.matur The probability of maturation from an immature stage to the mature stage for each cytotype. Must be a list of three integers between 0 and 1. For example, `as.matur = c(0.5, 0.3, 0.5)`.
-#' @param as.msurv.e.set The survival probability of a mature individual for each cytotype. Must be a list of three integers between 0 and 1. For example, `as.msurv.e.set = c(0.5, 0.3, 0.5)`.
+#' @param aii.vec The survival probability of an immature individual for each cytotype. Must be a list of three integers between 0 and 1. For example, `aii.vec = c(0.0005, 0.005, 0.0005)`.
+#' @param as.matur The probability of maturation from an immature stage to the mature stage for each cytotype. Must be a list of three integers between 0 and 1. For example, `as.matur = c(0.60, 0.06, 0.60)`.
+#' @param as.msurv.e.set The survival probability of a mature individual for each cytotype. Must be a list of three integers between 0 and 1. These are defined based on user supplied
+#' mean probability of mature survival (`as.msurv`), proportion of environmental variance (`env.ci`),
#' This list is defined within the `gen.iter.f.choosy()` function.
#' @param d Strength of density dependency on gamete production for each cytotype. Must be a list of three integers between 0 and 1. For example, `d = c(0.001, 0.009, 0.001)`.
#' @param gnum.base Mean number of gametes per individual per cytotype. Must be a list of three numeric values. For example, `gnum.base = c(100, 100, 100)`.
#' @param mate.lazy Default = FALSE, this prevents selfing from occurring during outcrossing. However, this increases the computational time by 31x!
-#' @inheritParams cytotype_repro_mate
-#' @param density.type Default = "all", this sets the density at time t as all individuals at time t.
+#' @param gam.density.type Default = "all", this sets the density dependence type for number of gametes produced at time t as all individuals at time t.
+#' Alternatively, "like-cytotype" sets the density at time t for each cytotype based on only the total immature and mature individuals
+#' of that cytotype at time t.
+#' @param rj List of two indicates the stage dependent density dependent impact of immature and mature individuals on number of gametes produced. Default is c(1,1).
+#' @param is.density.type Default = "all", this sets the density dependence type for immature surivival at time t as all individuals at time t.
#' Alternatively, "like-cytotype" sets the density at time t for each cytotype based on only the total immature and mature individuals
#' of that cytotype at time t.
-#' @returns List of 9, with 1:6 representing the number of individuals of each cytotype
-#' and at both stages at time t + 1. Items 7:9 are the number of gametes sampled for
-#' 2x, 3x, and 4x individuals at time t.
-#
+#' @param yj List of two indicates the stage dependent density dependent impact of immature and mature individuals on the probability of immature survival. Default is c(1,1).
+#' @param env.sigma Sigma value to define environmental variance corresponding to immature survival rate per generation.
+#' @param Xi.is Sample from a multivariate normal distribution. Allows immature and mature survival probabilities be correlated,
+#' as well as allowing for a correlation among cytotypes based on a supplied `rho` value.
+#' @param env.immature.survival Default = FALSE. When FALSE, the mean immature survival rate is equal to the exponential of the inverse
+#' immature survival rate (`aii`) times the sum of immature individuals and mature individuals of the cytotype indicated by `is.density.type`,
+#' which may be modified by `yj`. When this variable equals TRUE, the previous mean, or the immature determinate survival rate, is transformed
+#' by log(mu/(1-mu)) and the Johnson SB distribution is sampled given the `env.sigma` and `Xi.is` (defined in `gen.iter.f.choosy()`).
+#' @inheritParams cytotype_repro_mate
+#' @returns List of 15, with 1:6 representing the number of individuals of each cytotype
+#' and at both stages at time t + 1. Items 7:9 are the number of offspring generated for
+#' 2x, 3x, and 4x individuals at time t. Items 10:12 are the number of gametes generated from
+#' 2x, 3x, and 4x individuals at time t. Items 12:15 are the immature survival probabilities
+#' at time t.
+#'
#'
#' @importFrom stats na.omit rbinom
#'
@@ -31,12 +46,11 @@
#'
# One iteration function
-one.iter.f.choosy <- function(ct.vec, aii.vec,
- as.matur, as.msurv.e.set,
- d, gnum.base,
- s, b,
- cc, mc,
- density.type = "all", mate.lazy = FALSE){
+one.iter.f.choosy <- function(ct.vec, aii.vec, as.matur, as.msurv.e.set,
+ d, gnum.base, s, b,
+ cc, mc, gam.density.type, is.density.type,
+ mate.lazy = FALSE, rj = c(1,1), yj = c(1,1), env.sigma,
+ Xi.is, env.immature.survival){
# Empty vector for next generation
ctp1.vec <- rep(0, 6)
@@ -44,15 +58,16 @@ one.iter.f.choosy <- function(ct.vec, aii.vec,
# Calculate current generations gnum.vec
## gnum.vec = gnum.base times the exponential of the product of strength of density dependency
## times the sum of all individuals at time t
- if(density.type == "all"){
- gnum.vec <- c((gnum.base[1]*exp(-d[1]*(sum(ct.vec[1:6])))),
- (gnum.base[2]*exp(-d[2]*(sum(ct.vec[1:6])))),
- (gnum.base[3]*exp(-d[3]*(sum(ct.vec[1:6])))))
+ if(gam.density.type == "all"){
+ gam.density <- ((rj[1]*sum(ct.vec[1:3]))+(rj[2]*sum(ct.vec[4:6])))
+ gnum.vec <- c((gnum.base[1]*exp(-d[1]*gam.density)),
+ (gnum.base[2]*exp(-d[2]*gam.density)),
+ (gnum.base[3]*exp(-d[3]*gam.density)))
gnum.vec[is.na(gnum.vec)] <- 0
- } else if(density.type == "like-cytotype"){
- gnum.vec <- c((gnum.base[1]*exp(-d[1]*(sum(c(ct.vec[1], ct.vec[4]))))),
- (gnum.base[2]*exp(-d[2]*(sum(c(ct.vec[2], ct.vec[5] ))))),
- (gnum.base[3]*exp(-d[3]*(sum(c(ct.vec[3], ct.vec[6]))))))
+ } else if(gam.density.type == "like-cytotype"){
+ gnum.vec <- c((gnum.base[1]*exp(-d[1]*((rj[1]*ct.vec[1]) + (rj[2]*ct.vec[4])))),
+ (gnum.base[2]*exp(-d[2]*((rj[1]*ct.vec[2]) + (rj[2]*ct.vec[5])))),
+ (gnum.base[3]*exp(-d[3]*((rj[1]*ct.vec[3]) + (rj[2]*ct.vec[6])))))
gnum.vec[is.na(gnum.vec)] <- 0
} else print("Density type unavailable. This option only allows density.type to be set as all or like-cytotype.")
@@ -68,10 +83,37 @@ one.iter.f.choosy <- function(ct.vec, aii.vec,
trip.gamps <- off[2]; if(is.na(trip.gamps)){trip.gamps <- 0}
tetr.gamps <- off[3]; if(is.na(tetr.gamps)){tetr.gamps <- 0}
+ # Setup Immature Survival Probability
+ if(is.density.type == "all"){
+ is.density <- ((yj[1]*sum(ct.vec[1:3]))+(yj[2]*sum(ct.vec[4:6])))
+ immature.det.surv <- c((exp(-aii.vec[1]*is.density)),
+ (exp(-aii.vec[2]*is.density)),
+ (exp(-aii.vec[3]*is.density)))
+ } else if(is.density.type == "like-cytotype"){
+ immature.det.surv <- c((exp(-aii.vec[1]*((yj[1]*ct.vec[1]) + (yj[2]*ct.vec[4])))),
+ (exp(-aii.vec[2]*((yj[1]*ct.vec[2]) + (yj[2]*ct.vec[5])))),
+ (exp(-aii.vec[3]*((yj[1]*ct.vec[3]) + (yj[2]*ct.vec[6])))))
+ } else print("Density type unavailable. This option only allows density.type to be set as all or like-cytotype.")
+
+ immature.det.surv[is.na(immature.det.surv)] <- 0
+ if(env.immature.survival == TRUE){
+ immature.mu<- c()
+ as.isurv <- c()
+ for(z in 1:3){
+ immature.mu[z] <- log(immature.det.surv[z]/(1-immature.det.surv[z]))
+ as.isurv[z] <- (exp(immature.mu[z] + (env.sigma*Xi.is[z]))/(1 + exp(immature.mu[z] + (env.sigma*Xi.is[z]))))
+ }
+ as.isurv[is.na(as.isurv)] <- 0
+ } else{
+ as.isurv <- immature.det.surv
+ }
+
+
+
# Diploids - two steps: 1. Maturation+Survival and 2. Gamete production (defined above)
## Step 1:
### Apply survival and maturation
- Ndip.imm <- rbinom(n = 1, size = ct.vec[1], prob = exp(-aii.vec[1]*sum(ct.vec[1], ct.vec[4]))) #density
+ Ndip.imm <- rbinom(n = 1, size = ct.vec[1], prob = as.isurv[1]) #density
Ndip.mat <- rbinom(n = 1, size = ct.vec[1], prob = as.matur[1])
Ndip.surv <- rbinom(n = 1, size = ct.vec[4], prob = as.msurv.e.set[1])
@@ -84,7 +126,7 @@ one.iter.f.choosy <- function(ct.vec, aii.vec,
# Triploids
## Step 1:
### Apply survival and maturation
- Ntrip.imm <- rbinom(n = 1, size = ct.vec[2], prob = exp(-aii.vec[2]*sum(ct.vec[2], ct.vec[5])))
+ Ntrip.imm <- rbinom(n = 1, size = ct.vec[2], prob = as.isurv[2])
Ntrip.mat <- rbinom(n = 1, size = ct.vec[2], prob = as.matur[2])
Ntrip.surv <- rbinom(n = 1, size = ct.vec[5], prob = as.msurv.e.set[2])
@@ -97,7 +139,7 @@ one.iter.f.choosy <- function(ct.vec, aii.vec,
# Tetraploids
## Step 1:
### Apply survival and maturation
- Ntetr.imm <- rbinom(n = 1, size = ct.vec[3], prob = exp(-aii.vec[3]*sum(ct.vec[3], ct.vec[6])))
+ Ntetr.imm <- rbinom(n = 1, size = ct.vec[3], prob = as.isurv[3])
Ntetr.mat <- rbinom(n = 1, size = ct.vec[3], prob = as.matur[3])
Ntetr.surv <- rbinom(n = 1, size = ct.vec[6], prob = as.msurv.e.set[3])
@@ -106,8 +148,9 @@ one.iter.f.choosy <- function(ct.vec, aii.vec,
ctp1.vec[6] <- Ntetr.mat + Ntetr.surv
ctp1.vec[3] <- tetr.gamps + Ntetr.imm
+ ctp1.vec[is.na(ctp1.vec)] <- 0
# Combined and Return
- ctp1.vec <- c(ctp1.vec, off[1:6])
+ ctp1.vec <- c(ctp1.vec, off[1:6], as.isurv)
return(ctp1.vec)
}
diff --git a/R/surv.shape.R b/R/surv.shape.R
new file mode 100644
index 0000000..6f822ce
--- /dev/null
+++ b/R/surv.shape.R
@@ -0,0 +1,24 @@
+#' Survival shape - Calculates the alpha and beta value for survival beta distributions
+#'
+#' @description Calculates the shape parameters needed to sample a beta distribution
+#' for both immature survival and mature survival
+#'
+#'
+#' @param raw.means Mean probability of mature survival or immature survival. Must be a list of three integer between 0 and 1.
+#' @param env.ci Proportion of environmental variance used to define mature survival rate per generation.
+#' Must be a single integer greater than or equal to 0 and less than 1.
+#'
+#' @returns Matrix of alpha and beta parameters where matrix[1,i] is the alpha value for the ith mean, and matrix[2,i]
+#' is the beta value for the ith mean.
+#'
+#' @importFrom stats na.omit rbeta
+#'
+
+
+surv.shape <- function(env.ci, raw.means){
+ var <- var.option(env.ci = env.ci, mu = raw.means)
+ alpha.beta <- sapply(1:length(raw.means), function(item) alphabeta.calc(raw.means[item], var[item]))
+ return(alpha.beta)
+}
+
+
diff --git a/R/var.option.R b/R/var.option.R
index 4a52eca..28e87e5 100644
--- a/R/var.option.R
+++ b/R/var.option.R
@@ -14,5 +14,6 @@
var.option <- function(env.ci, mu){
var <- (env.ci*mu*(1-mu))
+ #var <- max(var, .Machine$double.xmin)
return(var)
}
diff --git a/README.md b/README.md
index a539483..2439c5c 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@
## Introduction
-R-based joint-dynamic population simulation for diploids, triploids, and autotetraploids. For details on these methods, see [Gaynor et al. 2023](https://doi.org/10.1101/2023.03.29.534764). To summarize, this package contains a stochastic stage-structured matrix population dynamics model for diploid, triploid, and autotetraploid perennial plants with overlapping generations and two life-stages (reproductively immature and reproductively mature). Stay-tune for v2.0.0, which will include population genetic simulations.
+R-based joint-dynamic population simulation for diploids, triploids, and autotetraploids. For details on these methods, see [Gaynor et al. Preprint](https://doi.org/10.1101/2023.03.29.534764). To summarize, this package contains a stochastic stage-structured matrix population dynamics model for diploid, triploid, and autotetraploid perennial plants with overlapping generations and two life-stages (reproductively immature and reproductively mature). Stay-tune for v2.0.0, which will include population genetic simulations.
## Installation
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 01ed869..e39784a 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -28,6 +28,9 @@ reference:
- muvar.calc
- var.option
- form.autopop
+ - env.copula
+ - mu.options
+ - surv.shape
articles:
- title: AutoPop
navbar: ~
diff --git a/docs/.DS_Store b/docs/.DS_Store
deleted file mode 100644
index 2e040af..0000000
Binary files a/docs/.DS_Store and /dev/null differ
diff --git a/docs/404.html b/docs/404.html
index 21af1d2..12ec079 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -7,8 +7,8 @@
Page not found (404) • AutoPop
-
-
+
+
License • AutoPopLicense • AutoPopGNU General Public License • AutoPop
-
-
+
+
@@ -32,7 +32,7 @@
AutoPop
- 1.0.0.0000
+ 1.0.0.1