FME: A Flexible Modelling Environment for Inverse Modelling, Sensitivity, Identifiability, Monte Carlo Analysis. Provides functions to help in fitting models to data, to perform Monte Carlo, sensitivity and identifiability analysis. It is intended to work with models be written as a set of differential equations that are solved either by an integration routine from package deSolve, or a steady-state solver from package rootSolve. However, the methods can also be used with other types of functions.

References in zbMATH (referenced in 18 articles , 1 standard article )

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  1. Moore, Sharon; Radunskaya, Ami; Zollinger, Elizabeth; Grant, Kathleen A.; Gonzales, Steven; Walter, Nicole A. R.; Baker, Erich J.: Pairing food and drink: a physiological model of blood ethanol levels for a variety of drinking behaviors (2022)
  2. Swanson, Ellen R.; Köse, Emek; Zollinger, Elizabeth A.; Elliott, Samantha L.: Mathematical modeling of tumor and cancer stem cells treated with CAR-T therapy and inhibition of TGF-(\beta) (2022)
  3. Shi, Lei; Feng, Xiaoliang; Qi, Longxing; Xu, Yanlong; Zhai, Sulan: Modeling and predicting the influence of PM(_2.5) on children’s respiratory diseases (2020)
  4. Daniel Kaschek; Wolfgang Mader; Mirjam Fehling-Kaschek; Marcus Rosenblatt; Jens Timmer: Dynamic Modeling, Parameter Estimation, and Uncertainty Analysis in R (2019) not zbMATH
  5. Giorgio Speranza, Roberto Canteri: RxpsG a new open project for Photoelectron and Electron Spectroscopy data processing (2019) not zbMATH
  6. van Lingen, Henk J.; Fadel, James G.; Moraes, Luis E.; Bannink, André; Dijkstra, Jan: Bayesian mechanistic modeling of thermodynamically controlled volatile fatty acid, hydrogen and methane production in the bovine rumen (2019)
  7. Ito, Yusuke; Tauzin, Alexandra; Remion, Azaria; Ejima, Keisuke; Mammano, Fabrizio; Iwami, Shingo: Dynamics of HIV-1 coinfection in different susceptible target cell populations during cell-free infection (2018)
  8. Lee, Kyoungjae; Lee, Jaeyong; Dass, Sarat C.: Inference for differential equation models using relaxation via dynamical systems (2018)
  9. Tripathi, Jai Prakash; Meghwani, Suraj S.; Thakur, Manoj; Abbas, Syed: A modified Leslie-Gower predator-prey interaction model and parameter identifiability (2018)
  10. Dass, Sarat C.; Lee, Jaeyong; Lee, Kyoungjae; Park, Jonghun: Laplace based approximate posterior inference for differential equation models (2017)
  11. Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
  12. Ray, J.; Hou, Z.; Huang, M.; Sargsyan, K.; Swiler, L.: Bayesian calibration of the community land model using surrogates (2015)
  13. Wentz, J. M.; Vainstein, V.; Oldson, D.; Gluzman-Poltorak, Z.; Basile, L. A.; Stricklin, D.: Mathematical model of radiation effects on thrombopoiesis in rhesus macaques and humans (2015)
  14. Kamangira, Boboh; Nyamugure, Philimon; Magombedze, Gesham: A theoretical mathematical assessment of the effectiveness of coartemether in the treatment of \textitPlasmodiumfalciparum malaria infection (2014)
  15. Bartocci, Ezio; Liò, Pietro; Merelli, Emanuela; Paoletti, Nicola: Multiple verification in complex biological systems: the bone remodelling case study (2012)
  16. Soetaert, Karline; Petzoldt, Thomas: Solving ODEs, DAEs, DDEs and PDEs in R (2011)
  17. Karline Soetaert; Thomas Petzoldt: Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME (2010) not zbMATH
  18. Karline Soetaert; Thomas Petzoldt; R. Setzer: Solving Differential Equations in R: Package deSolve (2010) not zbMATH