Chapter 2. Age-classified matrix models
pop.projection section 2.2. Projection of population growth rates.
Chapter 4. Stage-classified matrix models
lambda section 4.4. Returns the dominant eigenvalue
stable.stage section 4.5. Returns the stable stage distribution (right eigenvector)
reproductive.value section 4.6. Returns the reproductive value (left eigenvector)
damping.ratio section 4.7. Returns the damping ratio
eigen.analysis section 4.8. Computes eigenvalues and vectors, including the dominant eigenvalue , stable stage distribution, reproductive value, damping ratio, sensitivities, and elasticities. Since version 2.0, these are now included as separate functions as well
Chapter 5. Events in the Life Cycle
fundamental.matrix section 5.3.1. Calculate age-specific survival from a stage classified matrix using the fundamental matrix N
net.reproductive.rate section 5.3.4. Calculate the net reproductive rate of a stage classified matrix using the dominant eigenvalue of the matrix R.
generation.time section 5.3.5. Calculate the generation time of a stage-classified matrix
Age-specific survivorship and fertility curves in Fig 5.1 and 5.2 are
now included in demo(Caswell).
Chapter 6. Parameter estimation
projection.matrix section 6.1.1. Estimate vital rates and construct a projection matrix using transtion frequency tables
QPmat section 6.2.2. Construct a projection
matrix from a time series of individuals per stage using Wood's
quadratic programming method. Requires quadprog library.
Chapter 9. Sensitivity analysis
sensitivity section 9.1. Calculate sensitivities
elasticity section 9.2. Calculate elasticities
secder section 9.7. Second derivatives of eigenvalues
Chapter 10. Life Table Response Experiments
LTRE section 10.1 and 10.2. Fixed designs in
LTREs. See demo(Caswell) for variance
decomposition in random design (Fig 10.10).
Chapter 12. Statistical inference
boot.transitions section 12.1.4. Resample observed census transitions in a stage-fate data frame
resample section 12.1.5.2. Resample transitions in a projction matrix from a multinomial distribution (and fertilites from a log normal)
Chapter 14. Environmental stochasticity
stoch.growth.rate section 14.3. Calculate the log stochastic growth rate by simulation and Tuljapukar's approximation
stoch.sens section 14.4.1. Senstivity and elasticity of stochastic growth rate from numerical simultations
stoch.projection section 14.5.3. Project stochastic growth from a sequence of matrices in a uniform and nonuniform environment
Chapter 15. Demographic stochasticity
multiresultm section 15.1.3. Incorporate demographic stochasticity into population projections. The example uses the whale dataset to create a plot like figure 15.3.
Chris Stubben