Reference

Building and running a network model These functions allow to define the characteristics of a network (topology, initial conditions, …) and the statistical properties of the corresponding model (priors, covariates, …).
Definition of network properties
new_networkModel()	Create an empty network model
set_topo()	Set the topology in a network model.
set_init()	Set initial conditions in a network model
set_obs()	Set observations in a network model
set_steady()	Flag some network compartments as being in a steady state
set_split()	Flag some network compartments as being split compartments
set_half_life()	Set the half-life for radioactive tracers
add_pulse_event()	Register a pulse event on one of the compartment of a topology
Definition of statistical properties
add_covariates()	Add fixed effects of one or several covariates to some parameters.
set_size_family()	Set the distribution family for observed sizes
set_prop_family()	Set the distribution family for observed proportions
set_prior() set_priors()	Set prior(s) for a network model
missing_priors()	Get a table with parameters which are missing priors
Available priors
available_priors()	List the available priors for model parameters
constant_p()	Define a fixed-value prior
uniform_p()	Define a uniform prior
normal_p()	Define a truncated normal prior (on [0;+Inf])
hcauchy_p()	Define a half-Cauchy prior (on [0;+Inf])
scaled_beta_p()	Define a beta prior (on [0;scale])
exponential_p()	Define an exponential prior
gamma_p()	Define a gamma prior
Examining a network model
comps()	Return the compartments of a network model
groups(<networkModel>)	Get the grouping for a networkModel object
params()	Return the parameters of a network model
priors()	Return the tibble containing the priors of a networkModel
prop_family()	Return the distribution family for observed proportions
size_family()	Return the distribution family for observed sizes
topo()	Return the list of topologies, or a unique topology if all identical
ggtopo()	Plot a topology
Running a network model
run_mcmc()	Run a MCMC sampler on a network model using Stan
Post-run functions These functions allow basic manipulation of the mcmc.list object returned when running a model. Posterior predictive checks can be performed, and network properties such as steady states can be calculated.
Visualization and manipulation of MCMC samples
plot(<networkModel>)	Plot observations/trajectories/predictions from a network model
tidy_mcmc()	Extract a tidy output from an mcmc.list
Math(<mcmc.list>)	Math generics for mcmc.list objects
Ops(<mcmc.list>)	Ops generics for mcmc.list objects
c(<mcmc.list>)	Combine mcmc.list objects
`[`(<networkModelStanfit>)	Subset method for networkModelStanfit objects
mcmc_heatmap()	Draw a heatmap based on the correlations between parameters
Posterior predictive checks
predict(<networkModel>)	Add a column with predictions from a fit
tidy_dpp()	Prepare tidy data and posterior predictions
Network properties
tidy_trajectories()	Build a tidy table with the trajectories for each iteration
tidy_steady_states()	Build a tidy table with the calculated steady states for each iteration
tidy_flows()	Build a tidy table with the flows for each iteration
Visualization of network fluxes
sankey()	Draw a Sankey plot for a network and estimated flows
quick_sankey()	Draw a Sankey plot with basic defaults
ggflows()	A quick-and-dirty way of visualizing relative flows in a network
Simulation toolkit The package provides functions that can be used to simulate data for a given network topology and some parameter values.
sample_from_prior()	Sample from a prior object
sample_params()	Sample parameter values from priors
set_params()	Set the parameters in a network model
sample_from()	Generate samples from a network model
Datasets Datasets shipped with the package (example models, example run, and datasets used in the case studies).
Example models and run
aquarium_mod	A simple aquarium network model, ready to run
aquarium_run	An MCMC run from a simple aquarium network model
trini_mod	Network model for nitrogen fluxes in Trinidadian streams (Collins et al. 2016)
Datasets used in the case studies
eelgrass	Eelgrass phosphate incorporation data (McRoy & Barsdate 1970)
lalaja	Dataset for nitrogren fluxes in a Trinidadian mountain stream (Collins 2016)
li2017	Protein degradation in Arabidopsis plants (Li et al. 2017)
Miscellaneous Those are mostly methods implemented in the package. Typically the package user will not need to call those functions themselves.
Prior-related
Ops(<prior>)	Implementation of the '==' operator for priors
format(<prior>)	Pretty formatting of a prior object
format(<prior_tibble>)	Pretty formatting of a prior_tibble object
obj_sum(<prior>)	Function used for displaying prior object in tibbles
pillar_shaft(<prior>)	Function used for displaying prior object in tibbles
print(<prior>)	Pretty printing of a prior object
print(<prior_tibble>)	Pretty printing of a prior_tibble object
type_sum(<prior>)	Function used for displaying prior object in tibbles
Topology-related
Ops(<topology>)	Ops generics for topology objects
as_tbl_graph(<topology>)	Convert a network topology to a tbl_graph
ggtopo(<networkModel>)	Plot a network topology
ggtopo(<topology>)	Plot a topology
print(<topology>)	Pretty printing of a topology object
Others
as.mcmc.list(<tidy_flows>)	Convert a tidy_flows object to an mcmc.list
as.mcmc.list(<tidy_steady_states>)	Convert a tidy_steady_states object to an mcmc.list
filter(<ppcNetworkModel>)	Filter method for output of tidy_data_and_posterior_predict()
isotracer-package isotracer	The 'isotracer' package
plot(<ready_for_unit_plot>)	Plot output from split_to_unit_plot
posterior_predict(<networkModelStanfit>)	Draw from the posterior predictive distribution of the model outcome
print(<networkModel>)	Print method for networkModel objects
select(<mcmc.list>)	Select parameters based on their names
Miscellaneous Functions not sorted in the previous categories.
reexports groups select as.mcmc.list varnames %>%	Objects exported from other packages
as_tbl_graph()	Generic for as_tbl_graph()
calculate_steady_state()	Calculate steady-state compartment sizes for a network
delta2prop()	Convert delta notation to proportion of heavy isotope
dic()	Calculate DIC from a model output
filter	Filter (alias for filter function from dplyr)
filter_by_group()	Filter a tibble based on the "group" column
posterior_predict()	Draw from the posterior predictive distribution of the model outcome
project()	Calculate the trajectories of a network model
prop2delta()	Convert isotopic proportions to delta values
stanfit_to_named_mcmclist()	Convert a Stanfit object to a nicely named mcmc.list object
tidy_data()	Extract data from a networkModel object into a tidy tibble.
tidy_posterior_predict()	Draw from the posterior predictive distribution of the model outcome
traceplot()	Plot mcmc.list objects