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roundup <- function(x, n){ceiling(ceiling(x) / n) * n}

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dropall <- function(x){
    isFac = NULL
    for (i in 1:dim(x)[2]){isFac[i] = is.factor(x[ , i])}

    for (i in 1:length(isFac)){
        x[, i] = x[, i][ , drop = TRUE]
        }
    return(x)
    }

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#Circular means are useful if you are dealing with data that are inherently "circular" such as the day or month of the year, or direction.
#For example, imagine your data consists of the month in which an event occurs, and you want to report the average month. If you had 3 observations in December, and 3 in February, the average should be in January (1) whereas the more conventional arithmetic mean would tell you the answer was 7. The trick to dealing with this issue is to convert the data into radians, and do a bunch of trigonometry.

#This is how you might approach it in R:

#You have 3 observations in December (12), and 3 in February.

m = c(12,12,12,2,2,2)

#First you convert these values to an angle, then to radians. There are 12 (approximately) equally spaced points in the year for month, which we specify with np.

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#A likelihood function for GOMPERTZ, GOMPERTZ-MAKEHANM and SILER models.

likeLT <- function(lifetable,pars,type="GO"){

    # Extract data from life table
    Dx = lifetable$Dx
    Nx = lifetable$Nx
    StartInt = lifetable$StartAge
    EndInt = lifetable$EndAge
    LT.Type = as.character(lifetable$Type[1])

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 m = (t(m[nrow(m):1,]))

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library(RColorBrewer)

#Assume your data are in a data frame.
df1 <- data.frame(x = 1:100)

#Define the basic palette (this case goes from red - yellow - green, but you could make it any 3 colours.)
colCode <- colorRampPalette(c("red", "yellow", "green"))(n = 999)

#Make a vector (of length 999), but break apart the parts of the sequence that should be red yellow and green.
#This allows you to alter where the yellow "pivot point" is.

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Squares <- 1:64
RiceOnASquare = 1
for (i in 2:64){
  RiceOnASquare = append(RiceOnASquare,RiceOnASquare[i-1]*2)
}
sum(RiceOnASquare)

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#Stochastic geometric population growth rate

#Simulation settings
pgr = 1.05
var.pgr = 0.1
startPop = 10
nGen = 100
ntrials = 1000
pseudoExtinction = 1

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palettebuildr <- function(pathToJPEG = "logo.jpg", ncols = 3, dist.method = "euclidian", clust.method = "complete"){
  require(jpeg)
  
  #Read in the jpeg file
  img <- readJPEG(pathToJPEG)
  
  #Using the whole image is overkill, especially for large files.
  #Therefore, create a grid from which extract the colors
  xgrid <- ceiling(seq(1, dim(img)[1], length.out = 50))
  ygrid <- ceiling(seq(1, dim(img)[2], length.out = 50))

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#How to subset the COMADRE Animal Matrix Database (works with COMPADRE too)

#Create a vector of indices to retain
subsetID <- which(comadre$metadata$Order %in% c("Monotremata","Didelphimorphia","Paucituberculata","Microbiotheria","Dasyurormorphia","Peramelemorphia","Diprotodontia"))

#Subset entire COMADRE object to JUST the above subset
#First make a copy

subset.comadre <- comadre