Introduction to Scientific Programming and Simulation Using R

1. Examples
   
2. LV <- function(t=NULL, y, a, b, g, e, K=Inf)
   
3. c(a*y[1]*(1 - y[1]/K) - b*y[1]*y[2], g*b*y[1]*y[2] - e*y[2])
   
4. xy <- RK4adapt(LV, 0, c(100, 50), 200, 1, tol=1e-3,
   
5. a=0.05, K=Inf, b=0.0002, g=0.8, e=0.03)
   
6. par(mfrow = c(2,1))
   
7. plot(xy$y[,1], xy$y[,2], type='p',
   
8. xlab='prey', ylab='pred', main='RK4, adaptive h')
   
9. plot(xy$t, xy$y[,1], type='p', xlab='time',
   
10. ylab='prey circles pred triangles', main='RK4, adaptive h')
    
11. points(xy$t, xy$y[,2], pch=2)
    
12. par(mfrow=c(1,1))
    
13. https://cran.r-project.org/web/packages/spuRs/spuRs.pdf

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1. Examples
   
2. transect.1 <- trapTransect(distances = 1:4,
   
3. seed.counts = c(4, 3, 2, 0))
   
4. transect.2 <- trapTransect(distances = 1:3,
   
5. seed.counts = c(3, 2, 1))
   
6. transect.3 <- trapTransect(distances=(1:5)/2,
   
7. seed.counts = c(3, 4, 2, 3, 1))
   
8. allTraps <- transectHolder(transect.1, transect.2, transect.3,
   
9. family="Weibull")
   
10. mean(allTraps)
    
11. sd.transectHolder(allTraps)
    
12. https://cran.r-project.org/web/packages/spuRs/spuRs.pdf

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thanks for your sharing.