Instance feature calculation and evolutionary instance generation
    for the traveling salesman problem. Also contains code to "morph" two TSP
    instances into each other. And the possibility to conveniently run a couple
    of solvers on TSP instances.

Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.

Time series prediction is a critical task in data analysis, requiring not only the selection of appropriate models, but also suitable data preprocessing and tuning strategies. 
  TSPredIT (Time Series Prediction with Integrated Tuning) is a framework that provides a seamless integration of data preprocessing, decomposition, model training, hyperparameter optimization, and evaluation. 
  Unlike other frameworks, TSPredIT emphasizes the co-optimization of both preprocessing and modeling steps, improving predictive performance. 
  It supports a variety of statistical and machine learning models, filtering techniques, outlier detection, data augmentation, and ensemble strategies. 
  More information is available in Salles et al. <doi:10.1007/978-3-662-68014-8_2>.

DEPRECATED: This package has been superseded by 'ThSQCA'.
    Please use install.packages('ThSQCA') instead. Provides threshold sweep methods for Qualitative Comparative 
    Analysis (QCA). Implements Condition Threshold Sweep-Single (CTS-S), 
    Condition Threshold Sweep-Multiple (CTS-M), Outcome Threshold Sweep (OTS), 
    and Dual Threshold Sweep (DTS) for systematic exploration of threshold 
    calibration effects on crisp-set QCA results. These methods extend 
    traditional robustness approaches by treating threshold variation as an 
    exploratory tool for discovering causal structures. Also provides Fiss 
    (2011) <doi:10.5465/amj.2011.60263120> core/peripheral condition 
    classification via compute_fiss_core() and generate_fiss_chart(), enabling 
    four-symbol configuration charts that distinguish core conditions (present 
    in both parsimonious and intermediate solutions) from peripheral conditions 
    (intermediate only). Built on top of the 'QCA' package by Dusa (2019) 
    <doi:10.1007/978-3-319-75668-4>, with function arguments following 'QCA' 
    conventions. Based on set-theoretic methods by Ragin (2008) 
    <doi:10.7208/chicago/9780226702797.001.0001> and established robustness 
    protocols by Rubinson et al. (2019) <doi:10.1177/00491241211036158>.