Man pages for systemicrisk
Systemic Risk and Network Reconstruction
| calibrate_ER | Calibrate ER model to a given density |
| calibrate_ER.nonsquare | Calibrate ER model to a given density with a nonsquare matrix |
| calibrate_FitnessEmp | Calibrate empirical fitness model to a given density |
| choosethin | Calibrate Thinning |
| cloneMatrix | Creates a deep copy of a matrix |
| default | Default of Banks |
| default_cascade | Default Cascade |
| default_clearing | Clearing Vector with Bankruptcy Costs |
| diagnose | Outputs Effective Sample Size Diagonistics for MCMC run |
| ERE_step_cycle | Does one Gibbs Step on a cycle |
| findFeasibleMatrix | Finds a Nonnegative Matrix Satisfying Row and Column Sums |
| findFeasibleMatrix_targetmean | Creates a feasible starting matrix with a desired mean... |
| genL | Generate Liabilities Matrix from Prior |
| getfeasibleMatr | Creates a feasible starting matrix |
| GibbsSteps_kcycle | Gibbs sampling step of a matrix in the ERE model |
| Model.additivelink.exponential.fitness | Fitness model for liabilities matrix |
| Model.fitness.conditionalmeandegree | Mean out-degree of a node with given fitness in the fitness... |
| Model.fitness.genlambdaparprior | Prior distribution for eta and zeta in the fitness model |
| Model.fitness.meandegree | Mean out-degree of a random node the fitness model |
| Model.Indep.p.lambda | Combination of Independent Models for p and lambda |
| Model.lambda.constant | Model for a Constant lambda |
| Model.lambda.constant.nonsquare | Model for a Constant lambda and Non-Square Matrices |
| Model.lambda.GammaPrior | Model with Gamma Prior on Lambda |
| Model.lambda.Gammaprior_mult | Model Using Multiple Independent Components |
| Model.p.BetaPrior | Model for a Random One-dimensional p |
| Model.p.Betaprior_mult | Model Using Multiple Independent Components |
| Model.p.constant | Model for a Constant p |
| Model.p.constant.nonsquare | Model for a constant p and Non-Square Matrices |
| Model.p.Fitness.Servedio | Multiplicative Fitness Model for Power Law |
| sample_ERE | Sample from the ERE model with given row and column sums |
| sample_HierarchicalModel | Sample from Hierarchical Model with given Row and Column Sums |
| steps_ERE | Perform Steps of the Gibbs Sampler of the ERE model |
