ncsu-landscape-dynamics/rpops: PoPS 2.0.1

Added

• calibrate can now use the Mathews Correlation Coefficient as the summary statistic of to keep or reject parameter sets (@ChrisJones687, #145).

• validate, calibrate, pops_multirun, and pops now take use_survival_rates, survival_rate_month, survival_rate_day, and survival_rates_file (@ChrisJones687, #147).

  • validate, calibrate, pops_multirun, and pops now take network_movement as a parameter. This parameter controls how dispersal occurs along the network (@ChrisJones687, #147).

• validate, calibrate, pops_multirun, auto_manage and pops no longer take network_min_distance and network_max_distance as these parameters are now passed in through the parameter_means and parameter_cov_matrix parameters and are calibrated as part of the calibration if network kernel is selected (@ChrisJones687, #140).

• calibrate now calibrates the network_min_distance and network_max_distance parameters during calibration and they are now part of the parameter_means and parameter_cov_matrix that are exported from the calibration (@ChrisJones687, #140).

• calibrate has more flexible success metric options removes use_distance, use_rmse, and use_mcc parameters and replaces it with the more flexible success_metrics parameter. Users can now select multiple combinations of different success metrics for the calibration (@ChrisJones687, #150).

• pops_multirun removed the ability to write all simulations. (@ChrisJones687, #144).

• the deterministic parameter has been renamed to dispersal_stochasticity. This was done to be more consistent with generate_stochasticity, movement_stochasticity, and establishment_stochasticity parameters (@ChrisJones687, #147).

• validate, calibrate, pops_multirun, and pops now propogate uncertainty from host and initial conditions. This adds the parameters use_initial_condition_uncertainty and use_host_uncertainty. If use_initial_condition_uncertainty is TRUE the infected_file and/or exposed_file need to have 2 layers a mean and standard deviation. If use_host_uncertainty is is TRUE the host_file needs to have 2 layers a mean and standard deviation. For each model run a host and/or initial conditions are drawn from the mean and sd layers so that each run has a unique host and/or initial conditions this will allow for both propogation of uncertainty from these sources but also partitioning (@ChrisJones687, #151).

• fixed error on parameter draws in select situations (@ChrisJones687, #146).