Compute the per-capita growth rate for a given model. Can handle stochastic and deterministic models, and has the option to discard burn in for stochastic models.

```
lambda(ipm, ...)
# S3 method for simple_di_det_ipm
lambda(ipm, type_lambda = "last", log = FALSE, ...)
# S3 method for simple_di_stoch_kern_ipm
lambda(ipm, type_lambda = "stochastic", burn_in = 0.1, log = NULL, ...)
# S3 method for simple_di_stoch_param_ipm
lambda(ipm, type_lambda = "stochastic", burn_in = 0.1, log = NULL, ...)
# S3 method for general_di_det_ipm
lambda(ipm, type_lambda = "last", log = FALSE, ...)
# S3 method for general_di_stoch_kern_ipm
lambda(ipm, ..., type_lambda = "stochastic", burn_in = 0.1, log = NULL)
# S3 method for general_di_stoch_param_ipm
lambda(ipm, ..., type_lambda = "stochastic", burn_in = 0.1, log = NULL)
# S3 method for simple_dd_det_ipm
lambda(ipm, type_lambda = "all", ..., log = FALSE)
# S3 method for simple_dd_stoch_kern_ipm
lambda(ipm, ..., type_lambda = "stochastic", burn_in = 0.1, log = NULL)
# S3 method for simple_dd_stoch_param_ipm
lambda(ipm, ..., type_lambda = "stochastic", burn_in = 0.1, log = NULL)
# S3 method for general_dd_det_ipm
lambda(ipm, type_lambda = "last", ..., log = FALSE)
# S3 method for general_dd_stoch_kern_ipm
lambda(ipm, ..., type_lambda = "stochastic", burn_in = 0.1, log = NULL)
# S3 method for general_dd_stoch_param_ipm
lambda(ipm, ..., type_lambda = "stochastic", burn_in = 0.1, log = NULL)
```

- ipm
An object returned by

`make_ipm()`

.- ...
other arguments passed to methods.

- type_lambda
Either

`'all'`

,`'last'`

, or`'stochastic'`

.`'all'`

returns a vector of lambda values for each time step of the simulation (equal in length to the`iterations`

argument of`make_ipm()`

).`'last'`

returns the lambda value for the final timestep.`'stochastic'`

returns a single value, which by default is`mean(log(lambda(ipm, type_lambda = "all")))`

, with the proportion of`burn_in`

iterations removed from the beginning of the simulation. Set`log`

to`FALSE`

to get`lambda`

on the linear scale for stochastic models (i.e.`exp(mean(log(lambdas)))`

).- log
Return lambda on the log scale? This is

`TRUE`

by default for stochastic models, and`FALSE`

for deterministic models.- burn_in
The proportion of iterations to discard. Default is 0.1 (i.e. first 10% of iterations in the simulation).

When `type_lambda = "all"`

, an array. Rows correspond to time
steps, and columns correspond to parameter sets (if any). For other types,
a numeric vector.