Package: sde 2.0.18

sde: Simulation and Inference for Stochastic Differential Equations

Companion package to the book Simulation and Inference for Stochastic Differential Equations With R Examples, ISBN 978-0-387-75838-1, Springer, NY. *

Authors:Stefano Maria Iacus

sde_2.0.18.tar.gz
sde_2.0.18.zip(r-4.5)sde_2.0.18.zip(r-4.4)sde_2.0.18.zip(r-4.3)
sde_2.0.18.tgz(r-4.4-x86_64)sde_2.0.18.tgz(r-4.4-arm64)sde_2.0.18.tgz(r-4.3-x86_64)sde_2.0.18.tgz(r-4.3-arm64)
sde_2.0.18.tar.gz(r-4.5-noble)sde_2.0.18.tar.gz(r-4.4-noble)
sde_2.0.18.tgz(r-4.4-emscripten)sde_2.0.18.tgz(r-4.3-emscripten)
sde.pdf |sde.html
sde/json (API)
NEWS

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

Peer review:

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:

45 exports 2.98 score 49 dependencies 14 dependents 1 mentions 163 scripts 2.1k downloads

Last updated 2 years agofrom:f6d0c39ca5. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 25 2024
R-4.5-win-x86_64NOTEAug 25 2024
R-4.5-linux-x86_64NOTEAug 25 2024
R-4.4-win-x86_64NOTEAug 25 2024
R-4.4-mac-x86_64NOTEAug 25 2024
R-4.4-mac-aarch64NOTEAug 25 2024
R-4.3-win-x86_64NOTEAug 25 2024
R-4.3-mac-x86_64NOTEAug 25 2024
R-4.3-mac-aarch64NOTEAug 25 2024

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

Dependencies:ashbitopscliclustercolorspacedeSolvefansifarverfdafdsFNNggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitmagrittrMASSMatrixmclustmgcvmulticoolmunsellmvtnormnlmepcaPPpillarpkgconfigpracmaR6rainbowRColorBrewerRcppRCurlrlangscalestibbleutf8vctrsviridisLitewithrzoo

Errata corrige to 1st edition of the companion book

Rendered fromsde.errata.Rnwusingutils::Sweaveon Aug 25 2024.

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