Algorithms for fitting scaled sparse linear regression and estimating precision matrices.

Generalized additive models under shape
        constraints on the component functions of the linear predictor.       
        Models can include multiple shape-constrained (univariate
        and bivariate) and unconstrained terms. Routines of the 
        package 'mgcv' are used to set up the model matrix, print, 
        and plot the results. Multiple smoothing parameter 
        estimation by the Generalized Cross Validation or similar.
        See Pya and Wood (2015) <doi:10.1007/s11222-013-9448-7> 
        for an overview. A broad selection of shape-constrained 
        smoothers, linear functionals of smooths with shape constraints,
        and Gaussian models with AR1 residuals.

A collection of procedures for analysing, visualising, 
  and managing single-case data. These include regression models 
  (multilevel, multivariate, bayesian), between case standardised mean difference, 
  overlap indices ('PND', 'PEM', 'PAND', '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 (2025)
  <https://jazznbass.github.io/scan-Book/>.

An entirely data-driven cell type annotation tools, which requires training data to learn the classifier, but not biological knowledge to make subjective decisions. It consists of three steps: preprocessing training and test data, model fitting on training data, and cell classification on test data. See Xiangling Ji,Danielle Tsao, Kailun Bai, Min Tsao, Li Xing, Xuekui Zhang.(2022)<doi:10.1101/2022.02.19.481159> for more details.

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>.