Identifies the locations of neurons, and estimates their calcium concentrations over time using the SCALPEL method proposed in Petersen, Ashley; Simon, Noah; Witten, Daniela. SCALPEL: Extracting neurons from calcium imaging data. Ann. Appl. Stat. 12 (2018), no. 4, 2430--2456. <doi:10.1214/18-AOAS1159>. <https://projecteuclid.org/euclid.aoas/1542078051>.

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 piecewise linear regression 
  models, multilevel models, overlap indices ('PND', 'PEM', 'PAND', 'PET', 'tau-u', 
  'baseline corrected tau', 'CDC'), and randomization tests. Data preparation functions 
  support outlier detection, handling missing values, scaling, truncation, 
  rank transformation, and smoothing. An export function helps to generate 
  html and latex tables in a publication friendly style. More details can be 
  found in the online book 'Analyzing single-case data with R and scan', Juergen Wilbert (2023)
  <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.