Dimension reduction for multivariate data of extreme events with a PCA like procedure as described in Reinbott, Janßen, (2024), <doi:10.48550/arXiv.2408.10650>. Tools for necessary transformations of the data are provided.

Maximally selected rank statistics with
 several p-value approximations.

Incorporates Approximate Bayesian Computation to get a posterior distribution and to select a model optimal parameter for an observation point. Additionally, the meta-sampling heuristic algorithm is realized for parameter estimation, which requires no model runs and is dimension-independent. A sampling scheme is also presented that allows model runs and uses the meta-sampling for point generation. A predictor is realized as the meta-sampling for the model output. All the algorithms leverage a machine learning method utilizing the maxima weighted Isolation Kernel approach, or 'MaxWiK'. The method involves transforming raw data to a Hilbert space (mapping) and measuring the similarity between simulated points and the maxima weighted Isolation Kernel mapping corresponding to the observation point. Comprehensive details of the methodology can be found in the papers Iurii Nagornov (2024) <doi:10.1007/978-3-031-66431-1_16> and Iurii Nagornov (2023) <doi:10.1007/978-3-031-29168-5_18>.

The maybe type represents the possibility of some value or nothing. 
    It is often used instead of throwing an error or returning `NULL`.
    The advantage of using a maybe type over `NULL` is that it is both composable 
    and requires the developer to explicitly acknowledge the potential absence 
    of a value, helping to avoid the existence of unexpected behaviour.

A suite of utility functions providing functionality commonly
    needed for production level projects such as logging, error handling,
    cache management and date-time parsing. Functions for date-time parsing and 
    formatting require that time zones be specified explicitly, avoiding a common 
    source of error when working with environmental time series.