Reaction rate dynamics can be retrieved from metabolite
  concentration time courses. User has to provide corresponding stoichiometric
  matrix but not a regulation model (Michaelis-Menten or similar).
  Instead of solving an ordinary differential equation (ODE) system describing the evolution of concentrations,
  we use B-splines to catch the concentration and rate dynamics then
  solve a least square problem on their coefficients with non-negativity
  (and optionally monotonicity) constraints.
  Constraints can be also set on initial values of concentration.
  The package 'dynafluxr' can be used as a library but also as
  an application with command line interface dynafluxr::cli("-h") or graphical user interface dynafluxr::gui().

While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).

Generalised model for population dynamics of invasive Aedes mosquitoes. Rationale and model structure are described here: Da Re et al. (2021) <doi:10.1016/j.ecoinf.2020.101180> and Da Re et al. (2022) <doi:10.1101/2021.12.21.473628>.

Returns dynamic fit index (DFI) cutoffs for latent variable models 
    that are tailored to the user's model statement, model type, and sample size.  
    This is the counterpart of the Shiny Application, <https://dynamicfit.app>.

A collection of novel tools for generating species distribution and abundance models (SDM) that are dynamic through both space and time. These highly flexible functions incorporate spatial and temporal aspects across key SDM stages; including when cleaning and filtering species occurrence data, generating pseudo-absence records, assessing and correcting sampling biases and autocorrelation, extracting explanatory variables and projecting distribution patterns. Throughout, functions utilise Google Earth Engine and Google Drive to minimise the computing power and storage demands associated with species distribution modelling at high spatio-temporal resolution.