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config()
, breaking the components of a
config
object out into a hierarchy of (new) S3 objects:
predictors
, life_cycle
,
parameters
, predictor_spec
,
transition_function
, and transition
. This has
numerous advantages, including the ability for users to build up
individual components of a config
with validation from the
new classes, rather than constructing the entire config
object in one step. This update means users need to modify any existing
config
objects to fit the new schema. The most significant
changes are:
life_cycle
class,
which contains a list of transition
objects. Previously,
this data was represented in the transitions
and
parameters
attributes of a config
.transition_function
. Previously,
config
s contained strings with the name of functions to
get()
from the environment; now we pass the
transition_function
s themselves. This removes the model’s
dependence on the global R environment, improving reproducibility.predictor_spec
and parameters
,
passing parameter and predictor values to
transition_function
s is now done more explicitly.config
s to conform to the new
schema.read_config()
and write_config()
,
which provided the ability to serialize and deserialize
config
s. These functions were not sufficient to
comprehensively serialize all config
s for reproducible
model results. Now, the package only handles in-memory objects.run_all_configs()
and the related
parallel
package dependency. This function was just a thin
wrapper over a call to lapply
or a parallel apply
operation.graph_lifecycle()
for visualizing the flow
between life stages, and the dependency on the package
igraph
. Instead, life_cycle
includes a print
method with useful information on the flow between life stages.vary_param()
and
vary_many_params()
.graph_population_each_group()
and
graph_population_overall_trend()
. These functions were
intended as an easy way to visualize model results, but they did not
handle all cases, since model output can be highly variable. We now
recommend users write their own code, e.g. with ggplot2
, to
visualize results.run()
: age_group
, process
and
infected
. These columns were populated based on the
assumption that life stage names were structured as a three-character
string like
<process><infected><age_group>
, and used
downstream in the graphing functions. Now, users are completely free to
name life stages as they wish.annual_growth_rate()
, for determining the annual
factor by which population changes. Results from the
growth_rate()
function can be sensitive to the specific
time period being modeled – this aims to be a more universally
applicable alternative.growth_rate()
to use base R.config
s to the package.
ogden2005
, which replicates an existing deer tick
population model by Ogden et al. (2005).winter_tick
, based on literature on the winter tick and
used as an example of the package’s flexibility for studying different
tick species.host_example_config
,
infect_example_config
, and
temp_example_config
, which are used as examples of package
use. Added examples with these config
s to the readme.config()
to take a single
predictors
argument, instead of separate
weather
and host_comm
arguments. This
simplifies the config()
structure, and allows using other
types of data as a predictor for transitions.
write_config()
arguments are updated to reflect this
change.snow_cover_fun()
transition function which is
used in the winter_tick
model configuration.write_config()
to write steps and initial
population as integers rather than decimal numbers.IxPopDyMod
packageThese 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.