Toolbox to process raw data from closed loop flux chamber (or tent)
    setups into ecosystem gas fluxes usable for analysis. It goes from a
    data frame of gas concentration over time (which can contain several
    measurements) and a meta data file indicating which measurement was done
    when, to a data frame of ecosystem gas fluxes including quality diagnostics.
    Organized with one function per step, maximizing user flexibility and backwards
    compatibility. Different models to estimate the fluxes from the raw data are
    available: exponential as described in
    Zhao et al (2018) <doi:10.1016/j.agrformet.2018.08.022>, exponential as described in
    Hutchinson and Mosier (1981) <doi:10.2136/sssaj1981.03615995004500020017x>,
    quadratic, and linear. Other functions include quality assessment,
    plotting for visual check, calculation of fluxes based on the setup
    specific parameters (chamber size, plot area, ...), gross primary production
    and transpiration rate calculation, and light response curves.

Implements methods for multiple change point detection in multivariate
    time series with non-stationary dynamics and cross-correlations. The methodology
    is based on a model in which each component has a fluctuating mean represented by
    a random walk with occasional abrupt shifts, combined with a stationary vector
    autoregressive structure to capture temporal and cross-sectional dependence. The
    framework is broadly applicable to correlated multivariate sequences in which
    large, sudden shifts occur in all or subsets of components and are the primary
    targets of interest, whereas small, smooth fluctuations are not. Although random
    walks are used as a modeling device, they provide a flexible approximation for a
    wide class of slowly varying or locally smooth dynamics, enabling robust
    performance beyond the strict random walk setting.

An interactive 'shiny'-based tool for exploration and quality
    assurance and quality control (QA/QC) of eddy covariance flux tower
    data processing. It generates data-point removal code via
    user-directed selection from a scatterplot, and can export a cleaned
    .csv with removed points set to NA plus an R script for
    reproducibility. Reference: Key (2025) <DOI:10.5281/zenodo.15597159>.