R: Systemic Risk and Network Reconstruction

# Systemic Risk and Network Reconstruction

## Documentation for package ‘systemicrisk’ version 0.4.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

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 average degree
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
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