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
sde_2.0.21.zip(r-4.7-x86_64)sde_2.0.21.zip(r-4.7-arm64)sde_2.0.21.zip(r-4.6-x86_64)sde_2.0.21.zip(r-4.6-arm64)sde_2.0.21.zip(r-4.5-x86_64)
sde_2.0.21.tgz(r-4.6-x86_64)sde_2.0.21.tgz(r-4.6-arm64)sde_2.0.21.tgz(r-4.5-x86_64)sde_2.0.21.tgz(r-4.5-arm64)
sde_2.0.21.tar.gz(r-4.7-arm64)sde_2.0.21.tar.gz(r-4.7-x86_64)sde_2.0.21.tar.gz(r-4.6-arm64)sde_2.0.21.tar.gz(r-4.6-x86_64)
sde_2.0.21.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
sde/json (API)

# 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:

7.28 score 15 packages 212 scripts 4.0k downloads 1 mentions 45 exports 44 dependencies

Last updated from:75a8ef5a26. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	128
linux-devel-x86_64	OK	161
source / vignettes	OK	224
linux-release-arm64	OK	133
linux-release-x86_64	OK	151
macos-release-arm64	OK	107
macos-release-x86_64	OK	265
macos-oldrel-arm64	OK	88
macos-oldrel-x86_64	OK	229
windows-devel-arm64	OK	337
windows-devel-x86_64	OK	135
windows-release-arm64	OK	342
windows-release-x86_64	OK	108
windows-oldrel-x86_64	OK	91
wasm-release	OK	112

Exports:BBridge BM cpoint DBridge dcBS dcCIR dcElerian dcEuler dcKessler dcOU dcOzaki dcShoji dcSim dsCIR dsOU EULERloglik GBM gmm HPloglik ksdens ksdiff ksdrift linear.mart.ef MOdist pcBS pcCIR pcOU psCIR psOU qcBS qcCIR qcOU qsCIR qsOU rcBS rcCIR rcOU rsCIR rsOU sde.sim sdeAIC sdeDiv SIMloglik simple.ef simple.ef2

Dependencies:ash bitops cli cluster colorspace cpp11 deSolve farver fda fds FNN ggplot2 glue gtable hdrcde isoband kernlab KernSmooth ks labeling lattice lifecycle locfit MASS Matrix mclust mgcv multicool mvtnorm nlme pcaPP pracma R6 rainbow RColorBrewer Rcpp RCurl rlang S7 scales vctrs viridisLite withr zoo

Errata corrige to 1st edition of the companion book

Rendered fromsde.errata.Rnwusingutils::Sweave

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Brownian motion, Brownian bridge, and geometric Brownian motion simulators	BBridge BM GBM
Volatility change-point estimator for diffusion processes	cpoint
Simulation of diffusion bridge	DBridge
Approximated conditional law of a diffusion process by Elerian's method	dcElerian
Approximated conditional law of a diffusion process	dcEuler
Approximated conditional law of a diffusion process by Kessler's method	dcKessler
Approximated conditional law of a diffusion process by Ozaki's method	dcOzaki
Approximated conditional law of a diffusion process by the Shoji-Ozaki method	dcShoji
Pedersen's simulated transition density	dcSim
Weekly closings of the Dow-Jones industrial average	DWJ
Euler approximation of the likelihood	EULERloglik
Generalized method of moments estimator	gmm
Ait-Sahalia Hermite polynomial expansion approximation of the likelihood	HPloglik
Nonparametric invariant density, drift, and diffusion coefficient estimation	ksdens ksdiff ksdrift
Linear martingale estimating function	linear.mart.ef
Markov Operator distance for clustering diffusion processes.	MOdist
Daily closings of 20 financial time series from 2006-01-03 to 2007-12-31	quotes
Black-Scholes-Merton or geometric Brownian motion process conditional law	dcBS pcBS qcBS rcBS
Conditional law of the Cox-Ingersoll-Ross process	dcCIR pcCIR qcCIR rcCIR
Ornstein-Uhlenbeck or Vasicek process conditional law	dcOU pcOU qcOU rcOU
Cox-Ingersoll-Ross process stationary law	dsCIR psCIR qsCIR rsCIR
Ornstein-Uhlenbeck or Vasicek process stationary law	dsOU psOU qsOU rsOU
Simulation of stochastic differential equation	sde.sim
Akaike's information criterion for diffusion processes	sdeAIC
Phi-Divergences test for diffusion processes	sdeDiv
Pedersen's approximation of the likelihood	SIMloglik
Simple estimating functions of types I and II	simple.ef
Simple estimating function based on the infinitesimal generator a the diffusion process	simple.ef2