Package: SIRE 1.1.0

SIRE: Finding Feedback Effects in SEM and Testing for Their Significance

Provides two main functionalities. 1 - Given a system of simultaneous equation, it decomposes the matrix of coefficients weighting the endogenous variables into three submatrices: one includes the subset of coefficients that have a causal nature in the model, two include the subset of coefficients that have a interdependent nature in the model, either at systematic level or induced by the correlation between error terms. 2 - Given a decomposed model, it tests for the significance of the interdependent relationships acting in the system, via Maximum likelihood and Wald test, which can be built starting from the function output. For theoretical reference see Faliva (1992) <doi:10.1007/BF02589085> and Faliva and Zoia (1994) <doi:10.1007/BF02589041>.

Authors:Gianmarco Vacca [aut, cre]

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4 exports 2.57 score 70 dependencies 13 mentions 5 scripts 217 downloads

Last updated 5 years agofrom:23066e5039. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:causal_decomposedec_calcfeedback_mlrho_calc

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecowplotcpp11DerivdoBydplyrfansifarvergenericsggplot2glueGPArotationgtableigraphisobandlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixmatrixcalcMatrixModelsmgcvmicrobenchmarkminqamnormtmodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpsychpurrrquantregR6RColorBrewerRcppRcppEigenrlangRsolnpsandwichscalesSparseMstringistringrsurvivalsystemfittibbletidyrtidyselecttruncnormutf8vctrsviridisLitewithrzoo