The correct_lambda function accomplishes Step 4 of the algorithm with regard to replications of the estimated pattern coefficient matrix. More specifically, the correct_lambda function re-orders and/or re-signs as needed within the estimated pattern coefficient matrix for each replication of a simulation study with exploratory factor analysis.
REREFACT-package
(Package: REREFACT) :
A post-rotation algorithm that REorders and/or REflects FACTors for each
The REREFACT package is an open source package for R (R Development Core Team, 2015), which provides user-defined functions for accessing a post-rotation algorithm that REorders and/or REflects FACTors for each replication of a simulation study with exploratory factor analysis.
The correct_gamma function accomplishes Step 4 of the algorithm with regard to replications of the estimated eta on x regression coefficient matrix. More specifically, the correct_gamma function re-orders and/or re-signs as needed within the estimated eta on x regression coefficient matrix for each replication of a simulation study with exploratory factor analysis.
The rerefact function accomplishes Step 1 thru Step 3 of the algorithm and creates the P that is used in the correct_alpha, correct_beta, correct_gamma, correct_lambda and correct_psi functions to accomplish Step 4 of the algorithm.
A list containing 200 replications of the estimated covariance matrix for the vector of residuals for eta provided by replication numbers 1 through 100 and 4701 through 4800 in Example 1 from Myers, Ahn, Lu, Celimli, and Zopluoglu (2016).
A list containing 200 replications of the estimated intercept or mean vector provided by replication numbers 1 through 100 and 4701 through 4800 in Example 2 from Myers, Ahn, Lu, Celimli, and Zopluoglu (2016).
A list containing 200 replications of the estimated covariance matrix for the vector of residuals for eta provided by replication numbers 1 through 100 and 4701 through 4800 in Example 2 from Myers, Ahn, Lu, Celimli, and Zopluoglu (2016).
A list containing 200 replications of the estimated pattern coefficient matrix provided by replication numbers 1 through 100 and 4701 through 4800 in Example 1 from Myers, Ahn, Lu, Celimli, and Zopluoglu (2016).
A list containing 200 correct P_i to re-order and/or re-sign as needed within the relevant parameter estimates provided by replication numbers 1 through 100 and 4701 through 4800 in Example 2 from Myers, Ahn, Lu, Celimli, and Zopluoglu (2016).
The correct_beta function accomplishes Step 4 of the algorithm with regard to replications of the estimated eta on eta regression coefficient matrix. More specifically, the correct_beta function re-orders and/or re-signs as needed within the estimated eta on eta regression coefficient matrix for each replication of a simulation study with exploratory factor analysis.
providing 
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