Estimates, predict and forecast dynamic models as well as Machine Learning metrics which assists in model selection for further analysis. The package also have capabilities to provide tools and metrics that are useful in machine learning and modeling. For example, there is quick summary, percent sign, Mallow's Cp tools and others. The ecosystem of this package is analysis of economic data for national development. The package is so far stable and has high reliability and efficiency as well as time-saving. The package is a variety but the following references are important guide to the major themes in the package (Hyndman & Athanasopoulos (2014 ISBN 978-0-9875071-0-5), Alkire & Santos (2014, doi.org/10.1016/j.worlddev.2014.01.026)).

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>).

The goal of 'dynamicpv' is to provide a simple way to calculate (net) present values and outputs from health economic models (especially cost-effectiveness and budget impact) in discrete time that reflect dynamic pricing and dynamic uptake. Dynamic pricing is also known as life cycle pricing; dynamic uptake is also known as multiple or stacked cohorts, or dynamic disease prevalence. Shafrin (2024) <doi:10.1515/fhep-2024-0014> provides an explanation of dynamic value elements, in the context of Generalized Cost Effectiveness Analysis, and Puls (2024) <doi:10.1016/j.jval.2024.03.006> reviews challenges of incorporating such dynamic value elements. This package aims to reduce those challenges.