A developing software suite for multiple change-point and change-point-type feature detection/estimation (data segmentation) in data sequences.

Compute Buishand Range Test, Pettit Test, SNHT, Student t-test, and Mann-Whitney Rank Test, to identify breakpoints in series. For all functions NA is allowed. Since all of the mention methods identify only one breakpoint in a series, a general function to look for N breakpoint is given. Also, the Yamamoto test for climate jump is available. Alexandersson, H. (1986) <doi:10.1002/joc.3370060607>, Buishand, T. (1982) <doi:10.1016/0022-1694(82)90066-X>, Hurtado, S. I., Zaninelli, P. G., & Agosta, E. A. (2020) <doi:10.1016/j.atmosres.2020.104955>, Mann, H. B., Whitney, D. R. (1947) <doi:10.1214/aoms/1177730491>, Pettitt, A. N. (1979) <doi:10.2307/2346729>, Ruxton, G. D., jul (2006) <doi:10.1093/beheco/ark016>, Yamamoto, R., Iwashima, T., Kazadi, S. N., & Hoshiai, M. (1985) <doi:10.2151/jmsj1965.63.6_1157>.

Reads several formats of 13C data (IRIS/Wagner,
    BreathID) and CSV.  Creates artificial sample data for testing.  Fits
    Maes/Ghoos, Bluck-Coward self-correcting formula using 'nls', 'nlme'.
    Methods to fit breath test curves with Bayesian Stan methods are
    refactored to package 'breathteststan'. For a Shiny GUI, see package
    'dmenne/breathtestshiny' on github.

Stan-based curve-fitting function
  for use with package 'breathtestcore' by the same author.
  Stan functions are refactored here for easier testing.

A collection of functions to analyse, visualize and interpret wind data
         and to calculate the potential energy production of wind turbines.

Easily processes batches of univariate or multivariate
    regression models. Returns results in a tidy format and generates
    visualization plots for straightforward interpretation (Wang,
    Shixiang, et al. (2025) <DOI:10.1002/mdr2.70028>).

Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) <doi:10.1038/s41598-018-27293-5>. For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the 'CellCODE' R package described in Chikina (2015) <doi:10.1093/bioinformatics/btv015>. The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses.