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siacus authored and cran-robot committed May 11, 2015
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11 changes: 5 additions & 6 deletions DESCRIPTION
Original file line number Diff line number Diff line change
@@ -1,15 +1,14 @@
Package: yuima
Type: Package
Title: The YUIMA Project package for SDEs
Version: 1.0.36
Date: 2014-09-29
Title: The YUIMA Project Package for SDEs
Version: 1.0.69
Depends: methods, zoo, stats4, utils, expm, cubature, mvtnorm
Author: YUIMA Project Team
Maintainer: Stefano M. Iacus <stefano.iacus@unimi.it>
Description: Simulation and Inference for Stochastic Differential Equations
Description: Simulation and Inference for Stochastic Differential Equations.
License: GPL-2
URL: http://R-Forge.R-project.org/projects/yuima/
Packaged: 2014-09-29 12:23:30 UTC; jago
NeedsCompilation: yes
Packaged: 2015-05-11 11:42:37 UTC; jago
Repository: CRAN
Date/Publication: 2014-09-29 15:19:08
Date/Publication: 2015-05-11 16:37:02
70 changes: 43 additions & 27 deletions MD5
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@@ -1,45 +1,51 @@
94d55d512a9ba36caa9b7df079bae19f *COPYING
d8b6de662d399ed78e4742ea26550cfd *DESCRIPTION
1e52fbf7bddf050319c4823b890e023b *NAMESPACE
e3d52f5aa84d58e4d3dcddfabda8107a *NEWS
2d1f6089fe0eed51e65b8036f521908c *DESCRIPTION
8c9b9ea96bfe598d06e02ab13eedf38d *NAMESPACE
238d5aef6accb7ce58e3377d53997f58 *NEWS
86f3ae6987c8a36c75b0be09a879ed5c *R/AllClasses.R
ecfdd33974bed758ee54d2ec06ce2232 *R/CPoint.R
49eb6b85e1313ab3a26781c3c70bc192 *R/CarmaNoise.R
dd610167315ee57f6b5921a5da017867 *R/CarmaNoise.R
4b3325c594164c1ef9e617555903b7ec *R/CholeskyfGn.R
0c01e6158fccb23d3c93463835a7a9da *R/WoodChanfGn.R
b06c4bd529085416edd1e696748ea6ad *R/adaBayes.R
7d012d423446202228dfae9756ec0341 *R/asymptotic_term_second.R
086eafac945b90bdd0e1d07a46be37aa *R/asymptotic_term_third.R
a2e9dba4d2ab571053806a3463dd0157 *R/asymptotic_term_third_function.R
ed73101980b3d40dc1a9aaf1d7984c1c *R/ClassCogarch.R
0cb173b37e7bc1ad44f2a2ae326e3d99 *R/DiagnosticCogarch.R
d2d341409883cce55e287c6adeb0391a *R/MM.COGARCH.R
732dcc9e15b9c0a4ce729a7825c4f3cf *R/WoodChanfGn.R
5651a39ecf326e112b498cd1764d352a *R/adaBayes.R
3fc05fc93ac6830f6064162d07ca1051 *R/asymptotic_term_second.R
ce7188b6122aed3cc988f68aa4425098 *R/asymptotic_term_third.R
c84cc727acac73a45493ff22b55a78a4 *R/asymptotic_term_third_function.R
3878b53ad6b45165671e6992e19bcced *R/bns.test.R
be74573aa14795bf124f04706a3c612c *R/cce.R
cdaae861c947a38f8189cfc12d18c5db *R/cce.R
5ceb81a051e2e5f4621f153c21178e24 *R/cogarchNoise.R
81109b8c6aa4eed3435772af607b114e *R/hyavar.R
febfb56c6f3c5e1d41854337c9ec5e00 *R/lasso.R
80a34e4a1f572dd2115686cee71be223 *R/limiting.gamma.R
45c0e3867458fe420679267060a09502 *R/llag.R
f6ab699bac600a3e5955f46962c224c4 *R/lse.R
448a942a88fc20b5ab6f605dc1355262 *R/llag.R
461a01b4c243bf315b46950ecb3dcad7 *R/lse.R
8dff29b07d75ba4703870061db483f75 *R/mmfrac.R
70759bc971fc6d2b6b15fc8ec668a894 *R/mpv.R
ee94a80376999cb09d7a5c984b520687 *R/noisy.sampling.R
e920d4330c561d82faa6695675da2d3b *R/phi.test.R
197ae3a057ce2b8309af7c30a30f3337 *R/poisson.random.sampling.R
6590dec2df8596e6bb2309d9221d29bc *R/qgv.R
a796ff4aa1d56f3dbc97f575f31837e4 *R/qmle.R
a48b2c53eebbd57280b7af0599c7b224 *R/rng.R
dccb8679a411fb8e915736c8510f710e *R/qgv.R
f5f0b2b4241c47cfbaf94242f3431c2b *R/qmle.R
ed6b747c418179641c5a60187aa38ec3 *R/rng.R
1f567847cf9c65fa085f2f9a50d3e8e6 *R/sampling2grid.R
b51ccb32c0eb42f1f41a2cb0f9146e32 *R/setCarma.R
274647d24585461cd5c9312c02803d79 *R/setCarma.R
5426f7206ebb290a6d3fc11729dbb6c7 *R/setCogarch.R
fcecca27a76849cd88191dfa8599b2b6 *R/setPoisson.R
e3c6ef46b773b904b3a09a2ce90fb7ad *R/sim.euler.R
91121e7917e6d96d06974fcf83d0ddc7 *R/sim.euler.R
d6d8dbbbeb5b8950787d890e953d38ed *R/sim.euler.space.discretized.R
357799e4682e2422e3373bb4390ff035 *R/simCP.R
4e853abd5a8650f417708751b1616e44 *R/simFunctional.R
9b90685f81331cabcc349fc6f690c151 *R/simulate.R
8085cc8546d7bc91626fbf170354256e *R/simFunctional.R
fdc63130ee8389a91cd27e241b2aa952 *R/simulate.R
f506cf2d30a5f912b5221112d7ae8965 *R/subsampling.R
074dc2d814dee60f80b59ba5014cff4d *R/toLatex.R
0510298428e63f28c2737cfb7e520c5b *R/yuima.R
ac8f9649532292ef242a77f4b884e055 *R/toLatex.R
510269f286f311811ea3ecd0cef42a6a *R/yuima.R
edfabacecae0ebb9b40324f1d499b4b9 *R/yuima.characteristic.R
e5347ae9c63c878226d4e14d20638cf5 *R/yuima.data.R
56a8471869f97e9fb2e53ce8d5ce3252 *R/yuima.functional.R
f8eda262a707953d62bdd414e5939a52 *R/yuima.model.R
29d3edfaabddc9c78abb0ef02d6021c7 *R/yuima.model.R
e3c164669a627b3f16fc6da48e714da5 *R/yuima.sampling.R
c1ee1ff8bddedff2f1dd24eeb10782c3 *R/zzz.R
bce7107dc324a66a94865b56f9e3bbf3 *data/MWK151.rda
Expand All @@ -48,12 +54,19 @@ c39fee5ae7e8ac95f0d0e32290b8da46 *inst/CITATION
cd8687c920284fc3afb12b9638fa70d2 *inst/COPYRIGHTS
1c61255f47e28af617c8eee89bc652b3 *man/CPoint.Rd
2ff567959c3646587b4eea7855f3fd40 *man/CarmaNoise.Rd
f6cedc6c81529d87e7e7bdad57f28308 *man/Diagnostic.Cogarch.Rd
055e9a102fcf02b4f9419311839f5675 *man/MWK151.Rd
8d58fd161183296785e5b46c700957cb *man/adaBayes.Rd
246cfd2ae45d70a7082f4ca558a946a3 *man/asymptotic_term.Rd
cd4c530aef0469d9b86787b2dd843bcd *man/bns.test.Rd
d3a8b9c61dcbd7bba509401886329b9c *man/carma.info-class.Rd
97843ab2beecfee0d0d7c2936d8526d0 *man/cce.Rd
0372fd2928614dd2eb256038b5ec7a24 *man/cce.Rd
11b35c0612f472dc2e7327a87d558562 *man/cogarch.gmm.incr.rd
193344676a72a75ee39f20b5f24e6577 *man/cogarch.gmm.rd
eae56a5cb6a4dce72101ebd9afe3a572 *man/cogarch.info-class.Rd
568e476d9c991093b411581340899c39 *man/cogarchNoise.Rd
e3e05c86fdbfef19097fb819ca56d4d5 *man/gmm.rd
4abad16109cdbcba47618b3eafd0b1d8 *man/hyavar.Rd
bc7cde41c45c90f70d5e39e4363ebbb3 *man/lasso.Rd
83b71e10610d8835a34015fafd55363c *man/limiting.gamma.Rd
0f5f1fbda6b30a88a1053f3e1d5660c5 *man/llag.Rd
Expand All @@ -67,26 +80,29 @@ b3165cb5569c8200e1951475a788de29 *man/qgv.Rd
edd884cb0899ffddeaaea9eb766505b3 *man/qmle.Rd
361abb4eb2b5e1fac3c9513b27e7623e *man/rconst.Rd
3a21c90c366ccae181e12b2d671541f5 *man/rng.Rd
462fdc39ca445291aa57448f8fa5b201 *man/setCarma.Rd
dcf545cfcafb3d4db450cb64d0ee46ad *man/setCarma.Rd
ad4ddfb720691c328760854eda4d505b *man/setCharacteristic.Rd
4767b968b3807fbe8b7ea54ecaa54aa4 *man/setCogarch.Rd
3553554d6bd21880a43ca82082b7c212 *man/setData.Rd
85e350be0518275991a13c21e6f2723f *man/setFunctional.Rd
a0478e917fe2724c571982606bfe2fce *man/setModel.Rd
c1679f065c110af4e9520971454fb65b *man/setPoisson.Rd
0512596ff48ff056083590f1a6b0fe3f *man/setSampling.Rd
9472c984217193e30caa73d6520b719f *man/setYuima.Rd
ab5b290fd33ce465c9c035d49995ce77 *man/simFunctional.Rd
2c6af6886af990bce19c59db3c69581f *man/simulate.Rd
a155b6054ee3712125c1b4f1ec92fedd *man/simulate.Rd
114989e6fbd3d202b0f3a6650a019abb *man/subsampling.Rd
b1a949448d14837ea6b6a659b6aaa87f *man/toLatex.Rd
288e38afe2b8d2975833ef2cfe62e70a *man/toLatex.Rd
4d6967e5c90892209f7bb705f20206dc *man/yuima-class.Rd
ab1e99f44258125e416cee06598f2ca3 *man/yuima.CP.qmle-class.Rd
28d1963ad07cdc1f7814fecda97d5a87 *man/yuima.carma-class.Rd
b7d632c4c18a8ea2704a636546c8dddc *man/yuima.carma.qmle-class.Rd
eef1fed04f5cfb5e46597d6189fbab1b *man/yuima.characteristic-class.Rd
1e3b52d8dbb839ced5130649542b87b8 *man/yuima.cogarch-class.Rd
567d0e7cd9693628511083b185d6f480 *man/yuima.data-class.Rd
14d129c89f98d6fc2237dd6a92705b73 *man/yuima.functional-class.Rd
75b82c6075bfd6f69e2ebf7b02cd243a *man/yuima.model-class.Rd
068fee21cdb050ca6f46be8739cc25b7 *man/yuima.poisson-class.Rd
ffbff0dc3f384712fe20d939c8c9bd84 *man/yuima.sampling-class.Rd
0bf98fc47e9537461ba9fc9805275f4d *src/cce_functions.c
ee7fcd7f5abf1295e327b6d2f606ba1f *src/carmafilter.c
f1826a4887ff9ce03bfd191ad9b9c8b3 *src/cce_functions.c
32 changes: 20 additions & 12 deletions NAMESPACE
Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,8 @@ importFrom("graphics", "plot")
import("zoo")
importFrom(stats, confint)
import("stats4")
import("expm")
import("mvtnorm")
import("expm")
import("mvtnorm")
import("cubature")

importFrom(utils, toLatex)
Expand All @@ -24,10 +24,12 @@ exportClasses("yuima",
"model.parameter",
"yuima.carma",
"carma.info",
"yuima.carma.qmle",
"yuima.poisson",
"yuima.qmle",
"yuima.CP.qmle"
"yuima.carma.qmle",
"yuima.poisson",
"yuima.qmle",
"yuima.CP.qmle",
"cogarch.info",
"yuima.cogarch"
)

exportMethods(
Expand Down Expand Up @@ -68,9 +70,11 @@ export(setData)
export(setSampling)
export(setCharacteristic)
export(setCarma)
export(setPoisson)
export(dconst)
export(rconst)
export(setPoisson)
export(dconst)
export(rconst)

export(setCogarch)

export(dim)
export(length)
Expand All @@ -87,6 +91,7 @@ export(poisson.random.sampling)
export(noisy.sampling)
export(mpv)
export(bns.test)
export(hyavar) # asymptotic variance estimator for the Hayashi-Yoshida estimator

export(get.zoo.data)

Expand Down Expand Up @@ -118,13 +123,16 @@ export(CPoint)
export(qmleR)
export(qmleL)


export(CarmaNoise) # Estimates the Levy in carma model
export(gmm) # Estimation COGARCH(P,Q) using Method Of Moments
export(cogarchNoise)
export(Diagnostic.Cogarch)


export(qgv)
export(mmfrac)



export(cbind.yuima)

S3method(print, phitest)
Expand All @@ -135,7 +143,7 @@ S3method(print, yuima.lasso)
S3method(toLatex, yuima)
S3method(toLatex, yuima.model)
S3method(toLatex, yuima.carma)

S3method(toLatex, yuima.cogarch)

useDynLib(yuima)

9 changes: 9 additions & 0 deletions NEWS
Original file line number Diff line number Diff line change
Expand Up @@ -23,3 +23,12 @@
2014/07/31: fixed setSampling and print methods
2014/09/08: fixed a bug in cce_functions.c
2014/09/23: added Compound Poisson simulator
2014/11/10: fixed a bug in cce_functions.c
changed the optimization method of the method "QMLE" of cce
modified cce.Rd
2014/11/11: fixed a bug in cce.R (method "GME")
modified the example of cce.Rd
2015/04/02: fixed a bug in rng.R (function "rstable")
2015/04/21: added hyavar.R, hyavar.Rd (asymptotic variance estimator for HY)
fixed a bug in llag.R
modified cce.Rd, cce_functions.c
10 changes: 8 additions & 2 deletions R/CarmaNoise.R
Original file line number Diff line number Diff line change
Expand Up @@ -324,7 +324,13 @@ yuima.CarmaNoise<-function(y,tt,ar.par,ma.par,

idx.r<-match(0,Im(diagA$values))
lambda.r<-Re(diagA$values[idx.r])
int<-0

if(is.na(idx.r)){
yuima.warn("all eigenvalues are immaginary numbers")
idx.r<-1
lambda.r<-diagA$values[idx.r]
}
int<-0

derA<-aEvalPoly(ar.par[c(p:1)],lambda.r)
# if(q==1){
Expand Down Expand Up @@ -360,7 +366,7 @@ yuima.CarmaNoise<-function(y,tt,ar.par,ma.par,
#
# }
# }
return(lev.und)
return(Re(lev.und))
}


82 changes: 82 additions & 0 deletions R/ClassCogarch.R
Original file line number Diff line number Diff line change
@@ -0,0 +1,82 @@
# In order to deal the cogarch model in the \texttt{yuima} package
# We define a new class called \texttt{yuima.cogarch} and its structure
# is similar to those used for the carma model.
# The class \texttt{yuima.cogarch} extends the \texttt{yuima.model} and has
# an additional slot that contains informations about the model stored in an object of
# class \texttt{cogarch.info}.
# The class \texttt{cogarch.info} is build internally by the function \texttt{setCogarch} and it is a
# the first slot of an object of class \texttt{yuima.cogarch}.

# Class 'cogarch.info'
setClass("cogarch.info",
representation(p="numeric",
q="numeric",
ar.par="character",
ma.par="character",
loc.par="character",
Cogarch.var="character",
V.var="character",
Latent.var="character",
XinExpr="logical",
measure="list",
measure.type="character")
)

# Class 'yuima.cogarch'

setClass("yuima.cogarch",
representation(info="cogarch.info"),
contains="yuima.model")

# Class 'gmm.cogarch'

setClass("cogarch.gmm",representation(
model = "yuima.cogarch",
objFun="character"),
contains="mle"
)


setClass("summary.cogarch.gmm",representation( objFun = "ANY",
objFunVal = "ANY" ),
contains="summary.mle"
)



setClass("cogarch.gmm.incr",representation(Incr.Lev = "ANY",
logL.Incr = "ANY"
),
contains="cogarch.gmm"
)

setClass("summary.cogarch.gmm.incr",representation(logL.Incr = "ANY",
MeanI = "ANY",
SdI = "ANY",
logLI = "ANY",
TypeI = "ANY",
NumbI = "ANY",
StatI ="ANY"),
contains="summary.cogarch.gmm"
)





# setClass("cogarch.gmm.incr",representation(Incr.Lev = "ANY",
# model = "yuima.cogarch",
# logL.Incr = "ANY",
# objFun="character"
# ),
# contains="mle"
# )
#
# setClass("cogarch.gmm",representation(
# model = "yuima.cogarch",
# objFun="character"),
# contains="mle"
# )
# setClass("gmm.cogarch",
# contains="mle"
# )

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