Package 'MetaRVM'

Description

This function formats raw MetaRVM simulation output by:

1. Converting time steps to calendar dates

2. Adding user-defined demographic attributes from the initialization-derived population metadata

3. Handling different disease states appropriately:

   • Regular states (S, E, I, etc.): Keep values at integer time points

   • New count states (n_ prefix): Sum pairs to get daily counts

Usage

format_metarvm_output(sim_output, config)

Arguments

Value

data.table with formatted output including calendar dates and demographics

Note

This function is used for formatting the meta_sim output when MetaRVM function is called.

Description

The core simulation engine that implements a stochastic compartmental SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) model for respiratory virus epidemics across multiple demographic subpopulations. The function compiles and executes an ODIN-based differential equation model with time-varying contact patterns, vaccination dynamics, and complex disease progression pathways.

Usage

meta_sim(
  N_pop,
  ts,
  S0,
  I0,
  P0,
  R0,
  H0 = rep(0, N_pop),
  D0 = rep(0, N_pop),
  Ia0 = rep(0, N_pop),
  Ip0 = rep(0, N_pop),
  E0 = rep(0, N_pop),
  V0 = rep(0, N_pop),
  m_weekday_day,
  m_weekday_night,
  m_weekend_day,
  m_weekend_night,
  start_day = 0,
  delta_t,
  vac_mat,
  dv,
  de,
  pea,
  dp,
  da,
  ds,
  psr,
  dh,
  phr,
  dr,
  ve,
  nsteps,
  is.stoch = FALSE,
  seed = NULL,
  do_chk = FALSE,
  chk_time_steps = NULL,
  chk_file_names = NULL
)

Arguments

Details

The model implements a metapopulation epidemiological framework with the following features:

Compartmental Structure:

• S: Susceptible individuals

• E: Exposed (incubating) individuals

• I_presymp: Presymptomatic infectious individuals

• I_asymp: Asymptomatic infectious individuals

• I_symp: Symptomatic infectious individuals

• H: Hospitalized individuals

• R: Recovered individuals

• D: Deceased individuals

• V: Vaccinated individuals

• P: Total living population (excludes deaths)

Disease Progression Pathways:

1. S → E: Exposure through contact with infectious individuals

2. E → I_asymp/I_presymp: Progression to infectious states (proportion pea)

3. I_presymp → I_symp: Development of symptoms

4. I_asymp → R: Direct recovery from asymptomatic state

5. I_symp → R/H: Recovery or hospitalization (proportion psr)

6. H → R/D: Hospital discharge or death (proportion phr)

7. R → S: Loss of immunity

8. S → V: Vaccination

9. V → S/E: Vaccine waning or breakthrough infection

Mixing Patterns: Contact patterns vary by:

• Day of week: Weekday vs. weekend patterns

• Time of day: Day (6 AM - 6 PM) vs. night (6 PM - 6 AM) patterns

• Each pattern specified by N_pop × N_pop contact matrix

Force of Infection: Transmission occurs through contact between susceptible/vaccinated individuals and all infectious compartments (I_presymp + I_asymp + I_symp), modified by:

• Population-specific transmission rate, ts

• Time-varying contact patterns

• Vaccine effectiveness for breakthrough infections

Stochastic vs. Deterministic Mode:

• Deterministic: Uses exact differential equations

• Stochastic: Adds demographic stochasticity via binomial draws

Vaccination Implementation: Vaccination is implemented as time-varying input with:

• Scheduled vaccination counts per time step and subpopulation

• Vaccine effectiveness reducing infection probability

• Waning immunity returning individuals to susceptible state

Value

Returns a data.table with the following structure:

step

Integer time step index (0 to nsteps)

time

Continuous simulation time (step × delta_t)

disease_state

Character vector of compartment names

population_id

Character vector of subpopulation identifiers

value

Numeric values representing population counts in each compartment

Available disease states in output:

• Core compartments: S, E, I_presymp, I_asymp, I_symp, H, R, D, V, P

• Derived outputs: I_all (total infectious), cum_V (cumulative vaccinations)

• Transition flows: n_SE, n_EI, n_HR, n_HD, etc. (new infections, hospitalizations, deaths)

• Debug outputs: p_SE, p_VE, I_eff (probabilities and effective populations)

Parameter Scaling

All duration parameters are automatically converted to rates (1/duration). Scalar parameters are automatically expanded to vectors of length N_pop. This allows flexible specification of homogeneous or heterogeneous parameters.

Checkpointing

When do_chk = TRUE, the function saves a checkpoint file containing:

• Final compartment states for simulation continuation

• All model parameters for reproducibility

• Vaccination schedule data

• Population structure information

Author(s)

Arindam Fadikar, Charles Macal, Ignacio Martinez-Moyano, Jonathan Ozik

References

• ODIN package: https://mrc-ide.github.io/odin/

• Fadikar, A., et al. "Developing and deploying a use-inspired metapopulation modeling framework for detailed tracking of stratified health outcomes"

See Also

metaRVM for high-level simulation interface with configuration files parse_config for configuration file processing format_metarvm_output for output formatting with demographics

Examples

options(odin.verbose = FALSE)
# Basic deterministic simulation
N_pop <- 2
nsteps <- 400

# Initialize populations
S0 <- rep(1000, N_pop)
I0 <- rep(10, N_pop)
P0 <- S0 + I0
R0 <- rep(0, N_pop)

# Contact matrices (simplified - identity matrices)
contact_matrix <- diag(N_pop)

# Basic vaccination schedule (10% vaccination)
vac_mat <- matrix(0, nrow = nsteps + 1, ncol = N_pop + 1)
vac_mat[, 1] <- 0:nsteps
vac_mat[1, 1 + (1:N_pop)] <- P0 * 0.1

# Run simulation
results <- meta_sim(
  N_pop = N_pop,
  ts = 0.5,
  S0 = S0,
  I0 = I0,
  P0 = P0,
  R0 = R0,
  m_weekday_day = contact_matrix,
  m_weekday_night = contact_matrix,
  m_weekend_day = contact_matrix,
  m_weekend_night = contact_matrix,
  delta_t = 0.5,
  vac_mat = vac_mat,
  dv = 365,
  de = 3,
  pea = 0.3,
  dp = 2,
  da = 7,
  ds = 7,
  psr = 0.95,
  dh = 10,
  phr = 0.9,
  dr = 180,
  ve = 0.8,
  nsteps = nsteps,
  is.stoch = FALSE
)

Description

metaRVM() is the high-level entry point for running a MetaRVM metapopulation respiratory virus simulation. It parses the configuration, runs one or more simulation instances (deterministic or stochastic), formats the ODIN/MetaRVM output into a tidy long table with calendar dates and demographic attributes, and returns a MetaRVMResults object for downstream analysis and plotting.

Usage

metaRVM(config_input)

Arguments

Details

The configuration input controls:

• Population structure (user-defined categories and the initial compartment counts from the initialization file)

• Disease parameters (ts, ve, de, dp, da, ds, dh, dr, pea, psr, phr, dv, etc.)

• Mixing matrices (weekday/weekend, day/night contact patterns)

• Vaccination schedule and immunity waning

• Simulation settings (start date, length, number of instances, stochastic vs. deterministic mode, checkpointing)

Internally, metaRVM():

1. Parses the YAML configuration via parse_config().

2. Calls the ODIN-based simulation engine meta_sim() for each instance.

3. Uses format_metarvm_output() to convert time steps to dates and attach demographic attributes.

4. Wraps the formatted output and metadata in a MetaRVMResults object that supports method chaining for subsetting, summarizing, and plotting.

Value

A MetaRVMResults R6 object with three key components:

$results

A tidy data.table with one row per date–subpopulation–disease state–instance combination. Typical columns include:

• date: calendar date (Date)

• user-defined demographic category columns (if present in the initialization file)

• disease_state: compartment or flow label (e.g., S, E, I_symp, H, R, D, n_SE, n_IsympH, etc.)

• value: population count or daily flow

• instance: simulation instance index (1, 2, …)

$config

The MetaRVMConfig object used for the run.

$run_info

A list with metadata such as n_instances, date_range, delta_t, and checkpoint information.

Author(s)

Arindam Fadikar, Charles Macal, Ignacio Martinez-Moyano, Jonathan Ozik

References

Fadikar, A., et al. "Developing and deploying a use-inspired metapopulation modeling framework for detailed tracking of stratified health outcomes"

See Also

parse_config() for reading YAML configurations, MetaRVMConfig for configuration management, MetaRVMResults for analysis and plotting, meta_sim() for the low-level simulation engine.

Examples

options(odin.verbose = FALSE)
example_config <- system.file("extdata", "example_config.yaml",
                              package = "MetaRVM")

# Run a single-instance simulation from a YAML file
results <- metaRVM(example_config)

# Print a high-level summary
results

# Access the tidy results table
head(results$results)

# Summarize and plot hospitalizations and deaths by user-defined categories
results$summarize(
  group_by       = c("age", "zone"),
  disease_states = c("H", "D"),
  stats          = c("median", "quantile"),
  quantiles      = c(0.25, 0.75)
)$plot()

# Using a pre-parsed configuration object
cfg <- parse_config(example_config, return_object = TRUE)
results2 <- metaRVM(cfg)

Description

R6 class to handle MetaRVM checkpoint data. This class is a simplified version of MetaRVMConfig tailored for storing and accessing simulation checkpoints.

Details

The MetaRVMCheck class is designed to hold the state of a simulation at a specific time point, allowing for continuation or analysis. It stores all necessary parameters and population states.

Super class

MetaRVM::MetaRVMConfig -> MetaRVMCheck

Public fields

Methods

Public methods

• MetaRVMCheck$new()

• MetaRVMCheck$clone()

Method new()

Initialize a new MetaRVMCheck object

Usage

MetaRVMCheck$new(input)

Arguments

Returns

A new MetaRVMCheck object.

Method clone()

The objects of this class are cloneable with this method.

Usage

MetaRVMCheck$clone(deep = FALSE)

Arguments

Author(s)

Arindam Fadikar

Description

R6 class to handle MetaRVM configuration data with validation and methods. This class encapsulates all configuration parameters needed for MetaRVM simulations, providing methods for parameter access, validation, and introspection.

Details

The MetaRVMConfig class stores parsed configuration data from YAML files and provides structured access to simulation parameters. It automatically validates configuration completeness and provides convenient methods for accessing demographic categories, initialization-derived population metadata, and other simulation settings.

Public fields

Methods

Public methods

• MetaRVMConfig$new()

• MetaRVMConfig$get()

• MetaRVMConfig$get_all()

• MetaRVMConfig$list_parameters()

• MetaRVMConfig$parameter_summary()

• MetaRVMConfig$set()

• MetaRVMConfig$print()

• MetaRVMConfig$get_pop_map()

• MetaRVMConfig$get_category_names()

• MetaRVMConfig$get_category_values()

• MetaRVMConfig$get_all_categories()

• MetaRVMConfig$clone()

Method new()

Initialize a new MetaRVMConfig object

Usage

MetaRVMConfig$new(input)

Arguments

Returns

New MetaRVMConfig object (invisible)

Method get()

Get a configuration parameter

Usage

MetaRVMConfig$get(param)

Arguments

Returns

The requested parameter value

Method get_all()

Get all configuration parameters as a list

Usage

MetaRVMConfig$get_all()

Returns

Named list of all configuration parameters

Method list_parameters()

List all available parameter names

Usage

MetaRVMConfig$list_parameters()

Returns

Character vector of parameter names

Method parameter_summary()

Show summary of parameter types and sizes

Usage

MetaRVMConfig$parameter_summary()

Returns

Data frame with parameter information

Method set()

Set a configuration parameter

Usage

MetaRVMConfig$set(param, value)

Arguments

Returns

Self (invisible) for method chaining

Method print()

Print summary of configuration

Usage

MetaRVMConfig$print()

Returns

Self (invisible)

Method get_pop_map()

Get population mapping data

Usage

MetaRVMConfig$get_pop_map()

Returns

data.table containing population_id and user-defined demographic category columns

Method get_category_names()

Get names of all category columns

Usage

MetaRVMConfig$get_category_names()

Details

Category columns are automatically detected from the initialization CSV file. Any column that is not a reserved column (population_id, N, S0, I0, R0, V0, etc.) is treated as a demographic category (e.g., age, zone, income_level, occupation).

Returns

Character vector of category column names, or empty vector if no categories

Examples

\dontrun{
config <- MetaRVMConfig$new("config.yaml")
category_names <- config$get_category_names()  # e.g., c("age", "zone", "risk_group")
}

Method get_category_values()

Get unique values for a specific category

Usage

MetaRVMConfig$get_category_values(category_name)

Arguments

Returns

Character/numeric vector of unique values for the specified category

Examples

\dontrun{
config <- MetaRVMConfig$new("config.yaml")
ages <- config$get_category_values("age")  # if age is defined
income_levels <- config$get_category_values("income_level")  # if defined
}

Method get_all_categories()

Get all categories as a named list

Usage

MetaRVMConfig$get_all_categories()

Returns

Named list where names are category column names and values are vectors of unique values for each category. Returns empty list if no categories.

Examples

\dontrun{
config <- MetaRVMConfig$new("config.yaml")
all_cats <- config$get_all_categories()
# Returns: list(age = c("0-17", "18-64", "65+"), risk_group = c("low", "high"), ...)
}

Method clone()

The objects of this class are cloneable with this method.

Usage

MetaRVMConfig$clone(deep = FALSE)

Arguments

Author(s)

Arindam Fadikar

Examples

# Initialize from YAML file
example_config <- system.file("extdata", "example_config.yaml", package = "MetaRVM")
config <- MetaRVMConfig$new(example_config)

# Access parameters
config$get("N_pop")
config$get("start_date")

# Get demographic category names (user-defined)
category_names <- config$get_category_names()  # e.g., c("age", "zone", "risk_group")

# Get values for specific categories
ages <- config$get_category_values("age")

# Get all categories as a named list
all_categories <- config$get_all_categories()


## ------------------------------------------------
## Method `MetaRVMConfig$get_category_names`
## ------------------------------------------------

## Not run: 
config <- MetaRVMConfig$new("config.yaml")
category_names <- config$get_category_names()  # e.g., c("age", "zone", "risk_group")

## End(Not run)

## ------------------------------------------------
## Method `MetaRVMConfig$get_category_values`
## ------------------------------------------------

## Not run: 
config <- MetaRVMConfig$new("config.yaml")
ages <- config$get_category_values("age")  # if age is defined
income_levels <- config$get_category_values("income_level")  # if defined

## End(Not run)

## ------------------------------------------------
## Method `MetaRVMConfig$get_all_categories`
## ------------------------------------------------

## Not run: 
config <- MetaRVMConfig$new("config.yaml")
all_cats <- config$get_all_categories()
# Returns: list(age = c("0-17", "18-64", "65+"), risk_group = c("low", "high"), ...)

## End(Not run)

Description

R6 class to handle MetaRVM simulation results with comprehensive analysis and visualization methods. This class stores formatted simulation results and provides methods for data summarization, subsetting, and visualization with flexible demographic groupings.

Details

The MetaRVMResults class automatically formats raw simulation output upon initialization, converting time steps to calendar dates and adding demographic attributes. It provides methods for flexible data summarization across any user-defined demographic categories, plus method chaining for streamlined analysis workflows.

Public fields

Methods

Public methods

• MetaRVMResults$new()

• MetaRVMResults$print()

• MetaRVMResults$plot()

• MetaRVMResults$subset_data()

• MetaRVMResults$summarize()

• MetaRVMResults$clone()

Method new()

Initialize a new MetaRVMResults object

Usage

MetaRVMResults$new(
  raw_results,
  config,
  run_info = NULL,
  formatted_results = NULL
)

Arguments

Returns

New MetaRVMResults object (invisible)

Method print()

Print summary of results

Usage

MetaRVMResults$print()

Returns

Self (invisible)

Method plot()

Plot simulation trajectories directly from results

Usage

MetaRVMResults$plot(
  group_by = character(0),
  disease_states = NULL,
  date_range = NULL,
  instances = NULL,
  stats = NULL,
  quantiles = c(0.25, 0.75),
  exclude_p_columns = TRUE,
  ci_level = 0.95,
  theme = theme_minimal(),
  title = NULL
)

Arguments

Returns

ggplot object

Method subset_data()

Subset the data based on any combination of parameters

Usage

MetaRVMResults$subset_data(
  ...,
  disease_states = NULL,
  date_range = NULL,
  instances = NULL,
  exclude_p_columns = TRUE
)

Arguments

Returns

MetaRVMResults object with subset of results

Method summarize()

Summarize results across specified demographic characteristics

Usage

MetaRVMResults$summarize(
  group_by,
  disease_states = NULL,
  date_range = NULL,
  stats = c("mean", "median", "sd"),
  quantiles = c(0.25, 0.75),
  exclude_p_columns = TRUE
)

Arguments

Returns

data.table with summarized time series data or all instances if stats = NULL

Method clone()

The objects of this class are cloneable with this method.

Usage

MetaRVMResults$clone(deep = FALSE)

Arguments

Author(s)

Arindam Fadikar

Examples

options(odin.verbose = FALSE)
example_config <- system.file("extdata", "example_config.yaml", package = "MetaRVM")
# Run simulation
results_obj <- metaRVM(example_config)
# Access formatted results
head(results_obj$results)

# Subset data with multiple filters
subset_data <- results_obj$subset_data(
  age = c("18-64", "65+"), 
  disease_states = c("H", "D"),
  date_range = c(as.Date("2024-01-01"), as.Date("2024-02-01"))
)

# Method chaining for analysis and visualization
results_obj$subset_data(disease_states = "H")$summarize(
  group_by = c("age", "zone"), 
  stats = c("median", "quantile"),
  quantiles = c(0.25, 0.75)
)$plot()

Description

R6 class for summarized MetaRVM results with plotting capabilities and method chaining support. This class stores summarized simulation data and provides visualization methods that automatically adapt based on the data structure and grouping variables.

Details

The MetaRVMSummary class is designed to work seamlessly with method chaining from MetaRVMResults. It stores either summary statistics (mean, median, quantiles, etc.) or individual instance data, and provides intelligent plotting methods that automatically determine appropriate visualizations based on the data structure and demographic groupings.

The class supports two data types:

• Summary data: Contains aggregated statistics across simulation instances

• Instance data: Contains individual trajectory data for each simulation instance

Plotting behavior adapts automatically:

• Single grouping variable: Facets by demographic category, colors by disease state

• Two grouping variables: Grid layout with both demographics as facet dimensions

• Three grouping variables: Grid layout with first two as facets, third as color

Public Fields

data

data.table containing summarized results

config

MetaRVMConfig object from original simulation

type

Character string indicating data type ("summary" or "instances")

Public fields

Methods

Public methods

• MetaRVMSummary$new()

• MetaRVMSummary$print()

• MetaRVMSummary$plot()

• MetaRVMSummary$clone()

Method new()

Initialize MetaRVMSummary object

Usage

MetaRVMSummary$new(data, config, type)

Arguments

Returns

New MetaRVMSummary object (invisible)

Method print()

Print summary of the data object

Usage

MetaRVMSummary$print()

Returns

Self (invisible)

Method plot()

Plot method that shows median with quantile bands

Usage

MetaRVMSummary$plot(ci_level = 0.95, theme = theme_minimal(), title = NULL)

Arguments

Details

This method creates time series plots with automatic layout adaptation based on grouping variables:

• For summary data: Shows median lines with quantile confidence bands

• Automatically determines faceting strategy based on number of grouping variables

• Uses disease states for color differentiation when appropriate

The method requires specific data structure:

• Summary data must contain 'median_value' and quantile columns (e.g., 'q25', 'q75')

• Instance data must contain 'instance' column for individual trajectory grouping

Returns

ggplot object

Method clone()

The objects of this class are cloneable with this method.

Usage

MetaRVMSummary$clone(deep = FALSE)

Arguments

Author(s)

Arindam Fadikar

Examples

options(odin.verbose = FALSE)
example_config <- system.file("extdata", "example_config_dist.yaml", package = "MetaRVM")
# Run simulation
results <- metaRVM(example_config)
# Typically created through method chaining
summary_obj <- results$subset_data(disease_states = "H")$summarize(
  group_by = c("age", "zone"), 
  stats = c("median", "quantile"),
  quantiles = c(0.25, 0.75)
)

# Direct plotting
summary_obj$plot()

# Plot with custom ggplot theme and confidence level
summary_obj$plot(theme = ggplot2::theme_bw())

Description

Reads and parses a YAML configuration file for MetaRVM simulations, extracting all necessary parameters for epidemic modeling including population data, disease parameters, mixing matrices, vaccination schedules, and simulation settings.

Usage

parse_config(config_file, return_object = FALSE)

Arguments

Details

The function processes a YAML configuration file with the following main sections:

Simulation Configuration:

• random_seed: Optional random seed for reproducibility in case of stochastic simulations or stochastic parameters

• nsim: Number of simulation instances (default: 1)

• nrep: Number of stochastic replicates per parameter set (default: 1)

• simulation_mode: Optional simulation mode. Must be one of "deterministic" or "stochastic" (default: "deterministic").

• start_date: Simulation start date in MM/DD/YYYY format

• length: Simulation length in days

• checkpoint_dir: Optional checkpoint directory for saving intermediate results

• checkpoint_dates: Optional list of dates to save checkpoints.

• restore_from: Optional path to restore simulation from checkpoint

Population Data:

• initialization: CSV file with initial population states and optional user-defined category columns. The file must contain columns population_id, N, S0, I0, V0, R0. Any additional columns are treated as demographic categories.

• vaccination: CSV file with vaccination schedule over time. The first column must be dates in MM/DD/YYYY format. The rest of the columns must corresponds to respective subpopulations in the numeric order of population_id.

Mixing Matrices: Contact matrices for different time periods. Each CSV file must have a matrix of order (N_pop x N_pop), where, N_pop is the number of subpopulations. It is assumed that the i-th row and j-th column correspond to i-th and j-th subpopulations.

• weekday_day, weekday_night: Weekday contact patterns

• weekend_day, weekend_night: Weekend contact patterns

Disease Parameters: Epidemiological parameters (can be scalars or distributions):

• ts: Transmission rate for symptomatic individuals

• ve: Vaccine effectiveness

• de, dp, da, ds, dh, dr: Duration parameters for different disease states

• pea, psr, phr: Probability parameters for disease transitions

Sub-population Parameters: sub_disease_params allows specification of different parameter values for specific demographic categories (e.g., age groups, races).

The function supports stochastic parameters through distribution specifications with dist, mu, sd, shape, rate, etc.

Value

If return_object = FALSE (default), returns a named list containing:

N_pop

Number of population groups

pop_map

Data.table with population_id and user-defined demographic categories

S_ini, E_ini, I_asymp_ini, I_presymp_ini, I_symp_ini, H_ini, D_ini, P_ini, V_ini, R_ini

Initial compartment populations

vac_time_id, vac_counts, vac_mat

Vaccination schedule data

m_wd_d, m_wd_n, m_we_d, m_we_n

Contact mixing matrices

ts, ve, dv, de, dp, da, ds, dh, dr, pea, psr, phr

Disease parameter matrices (nsim × N_pop)

start_date

Simulation start date as Date object

sim_length

Simulation length in days

nsim

Number of simulation instances

nrep

Number of stochastic replicates per parameter set

simulation_mode

Simulation mode: "deterministic" or "stochastic"

random_seed

Random seed used (if any)

delta_t

Time step size (fixed at 0.5)

chk_file_names, chk_time_steps, do_chk

Checkpointing configuration

If return_object = TRUE, returns a MetaRVMConfig object with methods for parameter access and validation.

Parameter Distributions

Disease parameters can be specified as distributions for stochastic modeling:

• lognormal: dist: "lognormal", mu: value, sd: value

• gamma: dist: "gamma", shape: value, rate: value

• uniform: dist: "uniform", min: value, max: value

• beta: dist: "beta", shape1: value, shape2: value

• gaussian: dist: "gaussian", mean: value, sd: value

File Requirements

Population initialization file must contain columns:

• population_id: Unique identifier for each population group, natural numbers

• N: Total population size of the subpopulation

• S0, I0, V0, R0: Initial compartment counts

• Optional user-defined category columns (e.g., age, race, zone, income_level, occupation)

Vaccination file must contain: date (MM/DD/YYYY format) and vaccination counts for each population group

Author(s)

Arindam Fadikar

See Also

metaRVM for running simulations with parsed configuration MetaRVMConfig for the configuration object class process_vac_data for vaccination data processing

Examples

options(odin.verbose = FALSE)
example_config <- system.file("extdata", "example_config.yaml", package = "MetaRVM")
# Parse configuration file and return list (backward compatible)
config <- parse_config(example_config)

# Parse and return MetaRVMConfig object for method chaining
config_obj <- parse_config(example_config, return_object = TRUE)

# Access parameters from config object
config_obj$get("N_pop")
config_obj$list_parameters()

# Use with MetaRVM simulation
results <- metaRVM(config_obj)