Package: FME 1.3.6.3

Karline Soetaert

FME: A Flexible Modelling Environment for Inverse Modelling, Sensitivity, Identifiability and 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.

Authors:Karline Soetaert [aut, cre], Thomas Petzoldt [aut]

FME_1.3.6.3.tar.gz
FME_1.3.6.3.zip(r-4.7)FME_1.3.6.3.zip(r-4.6)FME_1.3.6.3.zip(r-4.5)
FME_1.3.6.3.tgz(r-4.6-x86_64)FME_1.3.6.3.tgz(r-4.6-arm64)FME_1.3.6.3.tgz(r-4.5-x86_64)FME_1.3.6.3.tgz(r-4.5-arm64)
FME_1.3.6.3.tar.gz(r-4.7-arm64)FME_1.3.6.3.tar.gz(r-4.7-x86_64)FME_1.3.6.3.tar.gz(r-4.6-arm64)FME_1.3.6.3.tar.gz(r-4.6-x86_64)
FME_1.3.6.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
FME/json (API)

# Install 'FME' in R:
install.packages('FME', repos = c('https://r-forge.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://r-forge.r-project.org/projects/fme

On CRAN:

Conda:

8.95 score 7 packages 451 scripts 6.2k downloads 43 mentions 15 exports 8 dependencies

Last updated from:1e07880fc1. Checks:11 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE130
linux-devel-x86_64NOTE107
source / vignettesOK205
linux-release-arm64NOTE125
linux-release-x86_64NOTE167
macos-release-arm64NOTE104
macos-release-x86_64NOTE243
macos-oldrel-arm64NOTE121
macos-oldrel-x86_64NOTE180
windows-develNOTE103
windows-releaseNOTE136
windows-oldrelNOTE115
wasm-releaseOK96

Exports:collincross2longgaussianWeightsGridLatinhypermodCostmodCRLmodFitmodMCMCNormobsplotpseudoOptimsensFunsensRangeUnif

Dependencies:codadeSolvelatticeMASSminpack.lmminqaRcpprootSolve

Sensitivity, Calibration, Identifiability, Monte Carlo Analysis of a Steady-State Model
A steady-state model of oxygen in a marine sediment | Global sensitivity analysis : Sensitivity ranges | Local sensitivity analysis : Sensitivity functions | Fitting the model to the data | Running a Markov chain Monte Carlo | Finally
Rendered fromFMEsteady.Rnwusingutils::Sweave

Last update: 2014-10-29
Started: 2014-01-07

Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME
Introduction | The test model | Local sensitivity analysis | Multivariate parameter identifiability | Fitting the model to data | MCMC | Model prediction | Monte Carlo applications | Discussion | The Fortran version of the model
Rendered fromFME.Rnwusingutils::Sweave

Last update: 2014-01-07
Started: 2014-01-07

Sensitivity, Calibration, Identifiability, Monte Carlo Analysis of a Dynamic Simulation Model
Introduction | The example model | Global sensitivity | Local sensitivity | Multivariate sensitivity analysis | Fitting the model to data | Markov chain Monte Carlo | Distributions | Examples | Finally
Rendered fromFMEdyna.Rnwusingutils::Sweave

Last update: 2014-01-07
Started: 2014-01-07

Tests of the Markov Chain Monte Carlo Implementation
Introduction | Function modMCMC | Sampling from a normal distribution | Sampling from a lognormal distribution | The banana | A simple chemical model | Fitting a nonlinear model | Finally
Rendered fromFMEmcmc.Rnwusingutils::Sweave

Last update: 2014-01-07
Started: 2014-01-07

Sensitivity, Calibration, Identifiability, Monte Carlo Analysis of a Nonlinear Model
Fitting a Monod function | Finally
Rendered fromFMEother.Rnwusingutils::Sweave

Last update: 2014-01-07
Started: 2014-01-07