Parse static-chamber greenhouse gas measurement
    files generated by a variety of instruments; compute flux rates using
    multi-observation metadata; and generate diagnostic metrics and plots.
    Designed to be easy to integrate into reproducible scientific workflows.

Provides a flexible framework for post-processing thermal
  dissipation sap flow data using statistical methods and machine learning.
  This framework includes anomaly correction, outlier removal, gap-filling,
  trend removal, signal damping correction, and sap flux density calculation.
  The functions in this package can also apply to other time series with
  various artifacts.

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.