Nothing
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
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