Package: sde 2.0.21

sde: Simulation and Inference for Stochastic Differential Equations

Description: Provides functions for simulation and inference for stochastic differential equations (SDEs). It accompanies the book "Simulation and Inference for Stochastic Differential Equations: With R Examples" (Iacus, 2008, Springer; ISBN: 978-0-387-75838-1).

Authors:Stefano Maria Iacus [aut, cre]

sde_2.0.21.tar.gz
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sde_2.0.21.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
sde/json (API)
NEWS

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

Bug tracker:https://github.com/siacus/sde/issues

Datasets:
  • DWJ - Weekly closings of the Dow-Jones industrial average
  • quotes - Daily closings of 20 financial time series from 2006-01-03 to 2007-12-31

On CRAN:

Conda:

7.49 score 17 packages 196 scripts 6.3k downloads 1 mentions 45 exports 44 dependencies

Last updated from:75a8ef5a26. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK180
linux-devel-x86_64OK139
source / vignettesOK192
linux-release-arm64OK142
linux-release-x86_64OK143
macos-release-arm64OK99
macos-release-x86_64OK255
macos-oldrel-arm64OK93
macos-oldrel-x86_64OK253
windows-develOK116
windows-releaseOK126
windows-oldrelOK115
wasm-releaseOK114

Exports:BBridgeBMcpointDBridgedcBSdcCIRdcEleriandcEulerdcKesslerdcOUdcOzakidcShojidcSimdsCIRdsOUEULERloglikGBMgmmHPloglikksdensksdiffksdriftlinear.mart.efMOdistpcBSpcCIRpcOUpsCIRpsOUqcBSqcCIRqcOUqsCIRqsOUrcBSrcCIRrcOUrsCIRrsOUsde.simsdeAICsdeDivSIMlogliksimple.efsimple.ef2

Dependencies:ashbitopscliclustercolorspacecpp11deSolvefarverfdafdsFNNggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitMASSMatrixmclustmgcvmulticoolmvtnormnlmepcaPPpracmaR6rainbowRColorBrewerRcppRCurlrlangS7scalesvctrsviridisLitewithrzoo

Errata corrige to 1st edition of the companion book

Rendered fromsde.errata.Rnwusingutils::Sweaveon May 24 2026.

Last update: 2022-08-08
Started: 2022-08-08

Readme and manuals

Help Manual

Help pageTopics
Brownian motion, Brownian bridge, and geometric Brownian motion simulatorsBBridge BM GBM
Volatility change-point estimator for diffusion processescpoint
Simulation of diffusion bridgeDBridge
Approximated conditional law of a diffusion process by Elerian's methoddcElerian
Approximated conditional law of a diffusion processdcEuler
Approximated conditional law of a diffusion process by Kessler's methoddcKessler
Approximated conditional law of a diffusion process by Ozaki's methoddcOzaki
Approximated conditional law of a diffusion process by the Shoji-Ozaki methoddcShoji
Pedersen's simulated transition densitydcSim
Weekly closings of the Dow-Jones industrial averageDWJ
Euler approximation of the likelihoodEULERloglik
Generalized method of moments estimatorgmm
Ait-Sahalia Hermite polynomial expansion approximation of the likelihoodHPloglik
Nonparametric invariant density, drift, and diffusion coefficient estimationksdens ksdiff ksdrift
Linear martingale estimating functionlinear.mart.ef
Markov Operator distance for clustering diffusion processes.MOdist
Daily closings of 20 financial time series from 2006-01-03 to 2007-12-31quotes
Black-Scholes-Merton or geometric Brownian motion process conditional lawdcBS pcBS qcBS rcBS
Conditional law of the Cox-Ingersoll-Ross processdcCIR pcCIR qcCIR rcCIR
Ornstein-Uhlenbeck or Vasicek process conditional lawdcOU pcOU qcOU rcOU
Cox-Ingersoll-Ross process stationary lawdsCIR psCIR qsCIR rsCIR
Ornstein-Uhlenbeck or Vasicek process stationary lawdsOU psOU qsOU rsOU
Simulation of stochastic differential equationsde.sim
Akaike's information criterion for diffusion processessdeAIC
Phi-Divergences test for diffusion processessdeDiv
Pedersen's approximation of the likelihoodSIMloglik
Simple estimating functions of types I and IIsimple.ef
Simple estimating function based on the infinitesimal generator a the diffusion processsimple.ef2