Systemic Risk and Network Reconstruction

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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