Performing impulse-response function (IRF) analysis of relevant variables of agent-based simulation models, in particular for models described in 'LSD' format. Based on the data produced by the simulation model, it performs both linear and state-dependent IRF analysis, providing the tools required by the Counterfactual Monte Carlo (CMC) methodology (Amendola and Pereira (2024) <doi:10.1016/j.jebo.2024.106811>), including state identification and sensitivity. CMC proposes retrieving the causal effect of shocks by exploiting the opportunity to directly observe the counterfactual in a fully controlled experimental setup. 'LSD' (Laboratory for Simulation Development) is free software available at <https://www.labsimdev.org/>).

Tools for sensitivity analysis of 'LSD' simulation models. Reads object-oriented data produced by 'LSD' simulation models and performs screening and global sensitivity analysis (Sobol decomposition method, Saltelli et al. (2008) ISBN:9780470725177). A Kriging or polynomial meta-model (Kleijnen (2009) <doi:10.1016/j.ejor.2007.10.013>) is estimated using the simulation data to provide the data required by the Sobol decomposition. 'LSD' (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at <https://www.labsimdev.org/>).

The solution of equality constrained least squares problem (LSE) is
    given through four analytics methods (Generalized QR Factorization, Lagrange 
    Multipliers, Direct Elimination and Null Space method). We expose the 
    orthogonal decomposition called Generalized QR Factorization (GQR) and also RQ 
    factorization. Finally some codes for the solution of LSE applied in 
    quaternions.

Implements bootstrap methods for linear regression models with errors following a time-varying process, focusing on approximating the distribution of the least-squares estimator for regression models with locally stationary errors. It enables the construction of bootstrap and classical confidence intervals for regression coefficients, leveraging intensive simulation studies and real data analysis.