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Fixes a test that was failing for r-devel on CRAN. There
are no other changes.
This is a maintenance to release to ensure compatibility with the
upcoming release of purrr. There aren’t any major changes.
A couple features have been added, and some bugs have been fixed.
as.matrix() method for ipmr_ipm
and ipmr_matrix. This strips the ipmr_matrix
class away, and returns a matrix/array object. When called on an
ipmr_ipm, it returns a list of sub-kernels that are just
standard matrix/array objects. Thanks for @Aariq for requesting this (#63).The title in conv_plot is now correctly updated when
there are multiple parameter sets used to construct an IPM. Previously,
the title was always something like lamdba_1, regardless of
which level of parameter set was being plotted. Thanks to @SanneE1 for pointing this
out (#64).
Clarifies some error messages.
Removes automatic faceting in plot.*, as that
usually failed when too many kernels were present. Instead, just use the
nrow and ncol arguments to the function
now.
Contains a number of bug fixes and some new functionality. The latter are mostly related to PADRINO models, and shouldn’t have too much of an effect on existing user-specified IPMs.
Adds return_sub_kernels argument to
make_ipm() for *_stoch_param and all density
dependent methods. This is due to the large RAM footprint that these
objects can occupy, particularly for long running models. The default is
FALSE, which will save considerable memory space. Can be
set to TRUE for downstream analyses that require
sub-kernels/iteration kernels.
Prettier printing method for PADRINO objects/lists of user-specified models.
Prettier warnings for left/right_ev()
Depending on your view, this may be a bug fix: updates the
log argument in lambda() so that it only
changes the scale, NOT the calculation method. The prior behavior was
documented, but unlikely to be intuitive, and so caused some confusion.
Thanks to @aariq for
pointing this out.
For stochastic IPMs, is_conv_to_asymptotic() and
conv_plot() now check for convergence in stochastic lambda.
That is, they use a cumulative mean log(lambda) over
iterations after discarding a burn-in. Thanks to @aariq for implementing this in #45.
Adds experimental function make_ipm_report(). This
function converts proto_ipm objects into Rmarkdown
documents that, when rendered, contain the equations and parameters used
to implement the model in Latex. See the function documentation for more
details, and report bugs/notation quirks/preferences in the issue
tracker.
Fixes bug in normalization of left/right eigenvectors in simple density independent stochastic models. (thanks to @aariq)
Fixes bug where "drop_levels" was not recognized in
some parameter set indexed models.
Fixes bug where parameter set levels were recycled in some cases.
Fixes bug in lambda() where log = TRUE
had no effect when the IPM was stochastic and type_lambda
was 'last' or 'all'.
Switched from all.equal to absolute tolerance. @aariq in #52.
Fixes bug where the value of lambda() was named for
determinstic models and unnamed for stochastic models. @aariq in #57.
Fixes bug in make_ipm() where a user-specified
sequence wasn’t getting used correctly for certain model classes. @aariq in #58.
Warnings about NAs in data_list are no
longer raised for model objects.
Contains small tweaks, bug fixes, and new feature additions. There shouldn’t be any breaking changes to the API.
Adds log argument to lambda so users
can choose which scale to return. The default is TRUE for
stochastic models and FALSE for deterministic
models.
Allows named expressions in define_env_state() such
that the names of the expressions may be used in
define_kernel(). Before, the name of the expression didn’t
matter, only the names of the outputted list. Now, this will also work
as well.
define_env_state(proto_ipm,
temp = rnorm(1, 20, 3),
precip = rgamma(1, 400, 2),
data_list = list())
More unit tests for parameter resampled models.
Removed innocuous warning messages. Added warnings for
NA values in a few define_* functions and
errors when they’re produced in sub-kernels.
Added the ipmr_ipm class so that most functions can
tell the difference between a PADRINO object and an
ipmr object.
Changes argument tol to tolerance in
is_conv_to_asymptotic() for consistency with other
function/argument names.
corrects a bug where functions in the
define_env_state data_list argument weren’t
recognized.
corrects bug in left_ev() and
right_ev() where parameter set indices were ignored for
deterministic models .
Contains a some tweaks and bug fixes, and a few new 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.
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.
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.
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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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