A collection of procedures for analysing, visualising, 
  and managing single-case data. Multi-phase and multi-baseline designs are 
  supported. Analysing methods include regression models 
  (multilevel, multivariate, bayesian), between case standardised mean difference, 
  overlap indices ('PND', 'PEM', 'PAND', 'NAP', 'PET', 'tau-u', 'IRD', 'baseline corrected tau', 
  'CDC'), and randomization tests. Data preparation functions support outlier 
  detection, handling missing values, scaling, and custom transformations. 
  An export function helps to generate html, word, and latex tables in a 
  publication friendly style. A shiny app allows to use scan in a graphical 
  user interface.
  More details can be found in the online book 'Analyzing single-case data with 
  R and scan', Juergen Wilbert (2026)
  <https://jazznbass.github.io/scan-Book/>.

Implementation of deep learning–based changepoint detection 
    algorithm designed for time series with smooth local fluctuations. 
    The method fits localized feed‑forward neural networks to approximate the 
    underlying smooth component and constructs a residual‑based detector that 
    isolates abrupt structural changes. A fully data‑adaptive 
    empirical cumulative distribution function (ECDF) based thresholding 
    rule and refinement procedures yield accurate changepoint localization 
    without parametric assumptions on noise or trend structure.

Detection of anomalous space-time clusters using the scan 
  statistics methodology. Focuses on prospective surveillance of data streams, 
  scanning for clusters with ongoing anomalies. Hypothesis testing is made 
  possible by Monte Carlo simulation. Allévius (2018) <doi:10.21105/joss.00515>.

Import, plot, and diagnose results from statistical
  catch-at-age models, used in fisheries stock assessment.

Generates cell-level cytokine activity estimates using relevant information from gene sets constructed with the 'CytoSig' and the 'Reactome' databases and scored using the modified 'Variance-adjusted Mahalanobis (VAM)' framework for single-cell RNA-sequencing (scRNA-seq) data. 'CytoSig' database is described in: Jiang at al., (2021) <doi:10.1038/s41592-021-01274-5>. 'Reactome' database is described in: Gillespie et al., (2021) <doi:10.1093/nar/gkab1028>. The 'VAM' method is outlined in: Frost (2020) <doi:10.1093/nar/gkaa582>.