Compute detailed and aggregated performance spectrum for event data. The detailed performance spectrum describes the event data in terms of segments, where the performance of each segment is measured and plotted for any occurrences of this segment over time and can be classified, e.g., regarding the overall population. The aggregated performance spectrum visualises the amount of cases of particular performance over time. Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2018) <doi:10.1007/978-3-319-98648-7_9>.

Provides an implementation of particle swarm optimisation consistent with the standard PSO 2007/2011 by Maurice Clerc. Additionally a number of ancillary routines are provided for easy testing and graphics.

Particle swarm optimization - a basic variant.

Implements an n-dimensional parameter space partitioning algorithm for evaluating the global behaviour of formal computational models as described by Pitt, Kim, Navarro and Myung (2006) <doi:10.1037/0033-295X.113.1.57>.

Estimation and inference of spatial and spatio-temporal 
    semiparametric models including spatial or spatio-temporal non-parametric 
    trends, parametric and non-parametric covariates and, possibly, a spatial 
    lag for the dependent variable and temporal correlation in the noise.
    The spatio-temporal trend can be decomposed in ANOVA way including main and 
    interaction functional terms. Use of SAP algorithm to estimate the spatial 
    or spatio-temporal trend and non-parametric covariates. The methodology of 
    these models can be found in next references
    Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>;
    Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and,
    particularly referred to the focus of the package, Minguez, R., 
    Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.

Spearman's rank correlation test with precomputed exact
        null distribution for n <= 22.

Smoothing splines with penalties on order m derivatives.