# ipmr 0.0.3

Contains a some tweaks and bug fixes, and a few new features:

## Features

Implements `right_ev()`

and `left_ev()`

methods for stochastic models.

Adds a new function, `conv_plot()`

, to graphically check for convergence to asymptotic dynamics in deterministic models.

Adds a new function, `discretize_pop_vector()`

, to compute the empirical density function for a population trait distribution given a set of observed trait values.

Adds print methods for density dependent models.

Adds `log`

argument for `lambda`

.

## Bug fixes

Corrects bug where `tol`

argument was ignored in `is_conv_to_asymptotic()`

.

Gives output from `lambda()`

names. Before, outputs from deterministic models with many parameter sets became hard to follow.

# ipmr 0.0.2

Contains a some tweaks and bug fixes. There is one major API change that renames parameters in `define_kernel`

.

Renames function arguments `hier_effs`

-> `par_sets`

, `levels_ages`

/`levels_hier_effs`

-> `age_indices`

/`par_set_indices`

. The idea was to shift from thinking about these IPMs as resulting from multilevel/hierarchical regresssion models to IPMs constructed from parameter sets (which can be derived from any number of other methods).

Corrects some bugs that caused `vital_rate_funs()`

to break for `stoch_param`

and density dependent models.

Updates the age X size model interface so that `max_age`

kernels can be specified separately if they have different functional forms from their non-`max_age`

versions.

`make_iter_kernel`

can handle computations passed into `mega_mat`

(e.g. `mega_mat = c(P + F, 0, I, C))`

.

Makes `plot.ipmr_matrix`

more flexible, which is now the recommended default `plot`

method for `ipm`

objects.

Changes to internal code that won’t affect user experience.

# ipmr 0.0.1

This is the first version of `ipmr`

. It contains methods for constructing a variety of IPMs as well as methods for basic analysis. Complete documentation is in the vignettes and on the package website.