Estimates monotone regression and variance functions in a nonparametric model, based on Dette, Holger, Neumeyer, and Pilz (2006) <doi:10.3150/bj/1151525131>.

Computes Monte Carlo standard errors for summaries of Monte Carlo output. Summaries and their standard errors are based on columns of Monte Carlo simulation output. Dennis D. Boos and Jason A. Osborne (2015) <doi:10.1111/insr.12087>.

Simplifies Monte Carlo simulation studies by automatically 
             setting up loops to run over parameter grids and parallelising
             the Monte Carlo repetitions. It also generates LaTeX tables.

Monte Carlo simulation allows testing different conditions given to the correct structural equation models. This package runs Monte Carlo simulations under different conditions (such as sample size or normality of data). Within the package data sets can be simulated and run based on the given model.
  First, continuous and normal data sets are generated based on the given model. Later Fleishman's power method (1978) <DOI:10.1007/BF02293811> is used to add non-normality if exists. 
  When data generation is completed (or when generated data sets are given) model test can also be run.
  Please cite as "Orçan, F. (2021). MonteCarloSEM: An R Package to Simulate Data for SEM. International Journal of Assessment Tools in Education, 8 (3), 704-713."

Fast implementations of mathematical operations and performance metrics for multi-objective optimization, including filtering and ranking of dominated vectors according to Pareto optimality, hypervolume metric, C.M. Fonseca, L. Paquete, M. López-Ibáñez (2006) <doi:10.1109/CEC.2006.1688440>, epsilon indicator, inverted generational distance, computation of the empirical attainment function, V.G. da Fonseca, C.M. Fonseca, A.O. Hall (2001) <doi:10.1007/3-540-44719-9_15>, and  Vorob'ev threshold, expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) <doi:10.1016/j.ejor.2014.07.032>, among others.

Provides functionality to generate compound optimal designs for targeting the multiple experimental objectives directly, ensuring that the full set of research questions is answered as economically as possible. Designs can be found using point or coordinate exchange algorithms combining estimation, inference and lack-of-fit criteria that account for model inadequacy. Details and examples are given by Koutra et al. (2024) <doi:10.48550/arXiv.2412.17158>.