Subtests Using Algorithmic Rummaging Techniques

Construct subtests from a pool of items by using ant-colony-optimization, genetic algorithms, brute force, or random sampling. Schultze (2017) .

A Tool for Sensitivity Analysis in Structural Equation Modeling

Perform sensitivity analysis in structural equation modeling using meta-heuristic optimization methods (e.g., ant colony optimization and others). The references for the proposed methods are: (1) Leite, W., & Shen, Z., Marcoulides, K., Fish, C., & Harring, J. (2022). (2) Harring, J. R., McNeish, D. M., & Hancock, G. R. (2017) ; (3) Fisk, C., Harring, J., Shen, Z., Leite, W., Suen, K., & Marcoulides, K. (2022). ; (4) Socha, K., & Dorigo, M. (2008) . We also thank Dr. Krzysztof Socha for sharing his research on ant colony optimization algorithm with continuous domains and associated R code, which provided the base for the development of this package.

Automatic Short Form Creation

Performs automatic creation of short forms of scales with an ant colony optimization algorithm and a Tabu search. As implemented in the package, the ant colony algorithm randomly selects items to build a model of a specified length, then updates the probability of item selection according to the fit of the best model within each set of searches. The algorithm continues until the same items are selected by multiple ants a given number of times in a row. On the other hand, the Tabu search changes one parameter at a time to be either free, constrained, or fixed while keeping track of the changes made and putting changes that result in worse fit in a "tabu" list so that the algorithm does not revisit them for some number of searches. See Leite, Huang, & Marcoulides (2008) for an applied example of the ant colony algorithm, and Marcoulides & Falk (2018) for an applied example of the Tabu search.

Evolutionary Parameter Estimation for 'Repast Simphony' Models

The EvoPER, Evolutionary Parameter Estimation for Individual-based Models is an extensible package providing optimization driven parameter estimation methods using metaheuristics and evolutionary computation techniques (Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization for continuous domains, Tabu Search, Evolutionary Strategies, ...) which could be more efficient and require, in some cases, fewer model evaluations than alternatives relying on experimental design. Currently there are built in support for models developed with 'Repast Simphony' Agent-Based framework (< https://repast.github.io/>) and with NetLogo (< https://ccl.northwestern.edu/netlogo/>) which are the most used frameworks for Agent-based modeling.

Adaptive Nature-Inspired Algorithms for Hybrid Genetic Optimization

The Genetic Algorithm (GA) is a type of optimization method of Evolutionary Algorithms. It uses the biologically inspired operators such as mutation, crossover, selection and replacement.Because of their global search and robustness abilities, GAs have been widely utilized in machine learning, expert systems, data science, engineering, life sciences and many other areas of research and business. However, the regular GAs need the techniques to improve their efficiency in computing time and performance in finding global optimum using some adaptation and hybridization strategies. The adaptive GAs (AGA) increase the convergence speed and success of regular GAs by setting the parameters crossover and mutation probabilities dynamically. The hybrid GAs combine the exploration strength of a stochastic GAs with the exact convergence ability of any type of deterministic local search algorithms such as simulated-annealing, in addition to other nature-inspired algorithms such as ant colony optimization, particle swarm optimization etc. The package 'adana' includes a rich working environment with its many functions that make possible to build and work regular GA, adaptive GA, hybrid GA and hybrid adaptive GA for any kind of optimization problems. Cebeci, Z. (2021, ISBN: 9786254397448).

Metaheuristic for Optimization

An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 ), grey wolf optimizer (Mirjalili et al., 2014 ), dragonfly algorithm (Mirjalili, 2015 ), firefly algorithm (Yang, 2009 ), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 ), harmony search algorithm (Mahdavi et al., 2007 ), moth flame optimizer (Mirjalili, 2015 , sine cosine algorithm (Mirjalili, 2016 ), whale optimization algorithm (Mirjalili and Lewis, 2016 ), clonal selection algorithm (Castro, 2002 ), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013).

Optimization Based Ensemble Forecasting Using MCS Algorithm

The real-life data is complex in nature. No single model can capture all aspect of complex time series data. In this package, 14 models, namely Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM, Deep LSTM, Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest (RF), k-Nearest Neighbour (KNN), XGBoost (XGB), Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonality (ETS) and TBATS models, have been implemented and their accuracy have been checked. An PCA based error index has been proposed to select a group of best models using MCS algorithms. After selecting the models, the forecasts from these models have been ensembled using optimization techniques. This package allows to implement 20 optimization techniques, namely, Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), Bat Algorithm (BA), Black Hole Optimization Algorithm (BHO), Clonal Selection Algorithm (CLONALG), Cuckoo Search (CS), Cat Swarm Optimization (CSO), Dragonfly Algorithm (DA), Differential Evolution (DE), Firefly Algorithm (FFA), Genetic Algorithm (GA), Gravitational Based Search Algorithm (GBS), Grasshopper Optimisation Algorithm (GOA), Grey Wolf Optimizer (GWO), Harmony Search Algorithm (HS), Krill-Herd Algorithm (KH), Moth Flame Optimizer (MFO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Shuffled Frog Leaping (SFL) and Whale Optimization Algorithm (WOA). This package has been developed using concept of Wang et al. (2022) , Qu et al. (2022) and Kriz (2019) .

A Simple and Robust JSON Parser and Generator for R

A reasonably fast JSON parser and generator, optimized for statistical data and the web. Offers simple, flexible tools for working with JSON in R, and is particularly powerful for building pipelines and interacting with a web API. The implementation is based on the mapping described in the vignette (Ooms, 2014). In addition to converting JSON data from/to R objects, 'jsonlite' contains functions to stream, validate, and prettify JSON data. The unit tests included with the package verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications.

General Non-Linear Optimization

General Non-linear Optimization Using Augmented Lagrange Multiplier Method.

Flexible Optimization of Complex Loss Functions with State and Parameter Space Constraints

Flexible optimizer with numerous input specifications for detailed parameterisation. Designed for complex loss functions with state and parameter space constraints. Visualization tools for validation and analysis of the convergence are included.