version 1.1.0

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: yuima
 Type: Package
 Title: The YUIMA Project Package for SDEs
-Version: 1.0.81
+Version: 1.1.0
 Depends: R(>= 2.10.0), methods, zoo, stats4, utils, expm, cubature,
         mvtnorm
 Author: YUIMA Project Team
@@ -10,6 +10,6 @@ Description: Simulation and Inference for Stochastic Differential Equations.
 License: GPL-2
 URL: http://R-Forge.R-project.org/projects/yuima/
 NeedsCompilation: yes
-Packaged: 2016-02-23 09:40:35 UTC; jago
+Packaged: 2016-06-16 21:20:26 UTC; jago
 Repository: CRAN
-Date/Publication: 2016-02-24 09:11:33
+Date/Publication: 2016-06-17 08:18:18

MD5

@@ -1,25 +1,25 @@
 94d55d512a9ba36caa9b7df079bae19f *COPYING
-72d548f3f68b10613b64fe73fa2deb1e *DESCRIPTION
-758becb196f42e4ca921421a68ec5074 *NAMESPACE
-f19743a7aa2c27b296771c8d2c3d7372 *NEWS
+5ed5cafe1fb199b2ec63c35e207b7802 *DESCRIPTION
+d857ad92fb17c3c045b8faaac8e3bff8 *NAMESPACE
+8974cebbc115d47cbe1cb75bf5a5fe3c *NEWS
 86f3ae6987c8a36c75b0be09a879ed5c *R/AllClasses.R
 ecfdd33974bed758ee54d2ec06ce2232 *R/CPoint.R
 dd610167315ee57f6b5921a5da017867 *R/CarmaNoise.R
 4b3325c594164c1ef9e617555903b7ec *R/CholeskyfGn.R
-eae8f6ef5de6ca4c3a4bb773359e8fc2 *R/ClassCogarch.R
-0cb173b37e7bc1ad44f2a2ae326e3d99 *R/DiagnosticCogarch.R
-67a0748469f880195c9249e192adca7f *R/FunctionAndOperators.R
-eccb209bb9cbbadd94e65ed0f12553ea *R/MM.COGARCH.R
-182130ccb6068965a31268e16e666bff *R/NewClasses.R
-327ad0c15660132826408b2e6dc6aa4c *R/PseudoLogLikCOGARCH.R
+7f217011b04e1d680505978b7119c8ab *R/ClassCogarch.R
+2e0bac6f55b108d4008cea8270d6d68f *R/DiagnosticCogarch.R
+62fcbc884d65fea03707a45004b45a8f *R/FunctionAndOperators.R
+6a2b38cdb0ad114f39e5b8db61d123ff *R/MM.COGARCH.R
+3df835a56754f4978d70878f2fac06dd *R/NewClasses.R
+03e030f1075484e5246b1b4fbba5831e *R/PseudoLogLikCOGARCH.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
 cdaae861c947a38f8189cfc12d18c5db *R/cce.R
-5ceb81a051e2e5f4621f153c21178e24 *R/cogarchNoise.R
+062cfafa31f5862ca35e7866e1c88254 *R/cogarchNoise.R
 81109b8c6aa4eed3435772af607b114e *R/hyavar.R
 febfb56c6f3c5e1d41854337c9ec5e00 *R/lasso.R
 80a34e4a1f572dd2115686cee71be223 *R/limiting.gamma.R
@@ -32,20 +32,20 @@ ee94a80376999cb09d7a5c984b520687 *R/noisy.sampling.R
 e920d4330c561d82faa6695675da2d3b *R/phi.test.R
 197ae3a057ce2b8309af7c30a30f3337 *R/poisson.random.sampling.R
 dccb8679a411fb8e915736c8510f710e *R/qgv.R
-3b2c6d05f70f82903d2698bd4f32edf1 *R/qmle.R
-a289599bcc62d5807bcf620b12ea15af *R/rng.R
+2755e5e9b6e1eb52517eaffd44dec6e3 *R/qmle.R
+dcabbf35c3fb6432ad041bd08967cc9c *R/rng.R
 1f567847cf9c65fa085f2f9a50d3e8e6 *R/sampling2grid.R
 6b59a2f01c61e56fc36165759434a326 *R/setCarma.R
 5426f7206ebb290a6d3fc11729dbb6c7 *R/setCogarch.R
 174277762d4756c409f5944da9936bbf *R/setHawkes.R
-5c2484358f14a6b27c01b9e6fdafb672 *R/setMultiModel.R
+c34e9db831fd409c663122016092dce1 *R/setMultiModel.R
 fcecca27a76849cd88191dfa8599b2b6 *R/setPoisson.R
-91121e7917e6d96d06974fcf83d0ddc7 *R/sim.euler.R
+dcf30fabb24dbc198690ead4c025feb7 *R/sim.euler.R
 d6d8dbbbeb5b8950787d890e953d38ed *R/sim.euler.space.discretized.R
 357799e4682e2422e3373bb4390ff035 *R/simCP.R
 8085cc8546d7bc91626fbf170354256e *R/simFunctional.R
-b33875431d372cee1cdadc397a10b147 *R/simulate.R
-a591c474fd0f72d5d3c272c763d634ad *R/simulateForMapsIntegralAndOperator.R
+7df0f18bb092c8a6fca41ab7704d303b *R/simulate.R
+b8df7acb6591737adcdd35122eedf681 *R/simulateForMapsIntegralAndOperator.R
 0c79ceae831d52303acef93dab016825 *R/simulateMultiProcess.R
 8ef695059bbac2781e15d7dd3bc69db2 *R/spectralcov.R
 f506cf2d30a5f912b5221112d7ae8965 *R/subsampling.R
@@ -54,7 +54,7 @@ ac8f9649532292ef242a77f4b884e055 *R/toLatex.R
 edfabacecae0ebb9b40324f1d499b4b9 *R/yuima.characteristic.R
 e5347ae9c63c878226d4e14d20638cf5 *R/yuima.data.R
 56a8471869f97e9fb2e53ce8d5ce3252 *R/yuima.functional.R
-29d3edfaabddc9c78abb0ef02d6021c7 *R/yuima.model.R
+8c686ffd9c78d06299e75d1ec5e61ad3 *R/yuima.model.R
 e3c164669a627b3f16fc6da48e714da5 *R/yuima.sampling.R
 c1ee1ff8bddedff2f1dd24eeb10782c3 *R/zzz.R
 b516711f126a8aafd4aae9b758da7824 *data/LogSPX.rda
@@ -65,18 +65,20 @@ cd8687c920284fc3afb12b9638fa70d2 *inst/COPYRIGHTS
 1c61255f47e28af617c8eee89bc652b3 *man/CPoint.Rd
 2ff567959c3646587b4eea7855f3fd40 *man/CarmaNoise.Rd
 f6cedc6c81529d87e7e7bdad57f28308 *man/Diagnostic.Cogarch.Rd
+373a5beea48671dc52cc980da3c9f39f *man/Integral.sde.rd
+8b6a11819c8fd0ba2551e37e69ab7a66 *man/Integrand.rd
 14addf8dd1e42e51dad008b6d0c85532 *man/LogSPX.Rd
 33b5307defeed5658299f5aec563673e *man/MWK151.Rd
-20e9e7e07168880ee42e2d58fa6817f6 *man/adaBayes.Rd
+630772946ee56f1ac924c387871eb15e *man/adaBayes.Rd
 246cfd2ae45d70a7082f4ca558a946a3 *man/asymptotic_term.Rd
 cd4c530aef0469d9b86787b2dd843bcd *man/bns.test.Rd
 d3a8b9c61dcbd7bba509401886329b9c *man/carma.info-class.Rd
 7a733600a5a369bd41c0a0b8cb72243d *man/cce.Rd
-11b35c0612f472dc2e7327a87d558562 *man/cogarch.gmm.incr.rd
-193344676a72a75ee39f20b5f24e6577 *man/cogarch.gmm.rd
+66d9893fcc3f99afadfb0285fff7ecaf *man/cogarch.est.incr.rd
+9a8ac5d2c7a8925aebf561fb96b06f72 *man/cogarch.est.rd
 eae56a5cb6a4dce72101ebd9afe3a572 *man/cogarch.info-class.Rd
-568e476d9c991093b411581340899c39 *man/cogarchNoise.Rd
-cd082b563d4eeb9adee170ae9170551c *man/gmm.rd
+bd8202a738aeedfafa15fd891d7275bf *man/cogarchNoise.Rd
+fcaeb71b9c9285267d393b0622c2dc63 *man/gmm.rd
 4abad16109cdbcba47618b3eafd0b1d8 *man/hyavar.Rd
 80cd357001c0325e5680dc2a50449258 *man/info.Output.Rd
 bc7cde41c45c90f70d5e39e4363ebbb3 *man/lasso.Rd
@@ -87,21 +89,22 @@ bc7cde41c45c90f70d5e39e4363ebbb3 *man/lasso.Rd
 854eef2bdc9e5c4fbe61e78c5f671843 *man/model.parameter-class.Rd
 db5127cc4b6ee0cd2ab9f40e52341477 *man/mpv.Rd
 5c3df9c78cc7e083e70187c05d3deb4f *man/noisy.sampling.Rd
+752e82c9d403d3dde64b101211cab048 *man/param.Integral.rd
 76d6974ff8cc91d91268d66a9cc85021 *man/param.Output.Rd
 1e62c8b5975366714ae0a052acb8b41c *man/phi.test.Rd
 7566b9ac61bee8350513827f37448cb7 *man/poisson.random.sampling.Rd
 b3165cb5569c8200e1951475a788de29 *man/qgv.Rd
-eca9516651005563f1adc542769c02e0 *man/qmle.Rd
+66b7ef9483c236d4881b4a5642cdb99d *man/qmle.Rd
 361abb4eb2b5e1fac3c9513b27e7623e *man/rconst.Rd
-a1d60f3bf192ee41b668a291b85b9a08 *man/rng.Rd
+8c6138f74ca66dc270d591396e7dac79 *man/rng.Rd
 dcf545cfcafb3d4db450cb64d0ee46ad *man/setCarma.Rd
 ad4ddfb720691c328760854eda4d505b *man/setCharacteristic.Rd
 4767b968b3807fbe8b7ea54ecaa54aa4 *man/setCogarch.Rd
 3553554d6bd21880a43ca82082b7c212 *man/setData.Rd
 85e350be0518275991a13c21e6f2723f *man/setFunctional.Rd
-94ee6e17934ea3c8a306dab331d71b8f *man/setMaps.Rd
+7f0427debb2f32140c82adb8f6b4addf *man/setIntegral.Rd
+99e08ae81a5a8491ca4fc1b71489f349 *man/setMap.Rd
 b8f4403135dff3222c09eb340fc8fe10 *man/setModel.Rd
-10a03bdcc253902b91a3472584cf0513 *man/setMultiModel.Rd
 c1679f065c110af4e9520971454fb65b *man/setPoisson.Rd
 0512596ff48ff056083590f1a6b0fe3f *man/setSampling.Rd
 9472c984217193e30caa73d6520b719f *man/setYuima.Rd
@@ -110,19 +113,23 @@ a155b6054ee3712125c1b4f1ec92fedd *man/simulate.Rd
 c79ad460bb5329d3c28e3f00e4430b46 *man/spectralcov.Rd
 114989e6fbd3d202b0f3a6650a019abb *man/subsampling.Rd
 288e38afe2b8d2975833ef2cfe62e70a *man/toLatex.Rd
+c8383c3a57f9c6f87e8d321e4db0e92e *man/variable.Integral.rd
 4d6967e5c90892209f7bb705f20206dc *man/yuima-class.Rd
 ab1e99f44258125e416cee06598f2ca3 *man/yuima.CP.qmle-class.Rd
-d983c29a1c5e32b80592fa0629ebe302 *man/yuima.Output-class.Rd
+44625cafd565fd6f69402243c7fbf670 *man/yuima.Integral-class.rd
+4ed8f281d7b3f283fc89c0582e46b808 *man/yuima.Output-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
 d8c0fc095d6a7d24a6a5cfd9c2e38586 *man/yuima.model-class.Rd
-63b8473a19d6a9f5c231f843e6e5f626 *man/yuima.multimodel.Rd
+6a72501f7453e18ee6987dede63ffe53 *man/yuima.multimodel.Rd
 068fee21cdb050ca6f46be8739cc25b7 *man/yuima.poisson-class.Rd
 ffbff0dc3f384712fe20d939c8c9bd84 *man/yuima.sampling-class.Rd
+2b1a38d3f511eca643f8bb068606f767 *src/Mt.h
 e33f564d55ac991fbd073d0a9da4a5b2 *src/PseudoLoglikCOGARCH.c
 ee7fcd7f5abf1295e327b6d2f606ba1f *src/carmafilter.c
 a6af7b7e5e386b6aaa2451cd9c02d4f8 *src/cce_functions.c
+5ec50393829fb8f790e61c48d5e780f0 *src/rpts.c

NAMESPACE

@@ -74,7 +74,12 @@ exportClasses("yuima",
 "yuima.Output",
 "info.Output",
 "param.Output",
-"yuima.multimodel"
+"yuima.multimodel",
+"param.Integral",
+"variable.Integral",
+"Integrand",
+"Integral.sde",
+"yuima.Integral"
 )
 
 exportMethods(
@@ -104,6 +109,7 @@ exportMethods(
 "Fnorm",
 "asymptotic_term",
 "cbind.yuima"
+#"rbind.yuima"
 )
 
 ## function which we want to expose to the user
@@ -120,8 +126,9 @@ export(dconst)
 export(rconst)
 
 export(setCogarch)
-export(setMaps)
-export(setMultiModel)
+export(setMap)
+#export(setMultiModel)
+export(setIntegral)
 
 export(dim)
 export(length)
@@ -147,8 +154,8 @@ export(get.zoo.data)
 ##export(ql,rql,ml.ql)
 ##export(rql)
 export(adaBayes)
-export(rIG, rNIG, rbgamma, rngamma, rstable) ##:: random number generator for Inverse Gaussian
-export(dIG, dNIG, dbgamma, dngamma) ##:: random number generator for I\
+export(rIG, rNIG, rbgamma, rvgamma, rstable,rpts,rnts) ## random number generators in rng.R
+export(dIG, dNIG, dbgamma, dvgamma) ## pdfs in rng.R
 export(limiting.gamma)
 
 
@@ -185,7 +192,7 @@ export(qgv)
 export(mmfrac)
 
 export(cbind.yuima)
-
+#export(rbind.yuima)
 S3method(print, phitest)
 S3method(print, qgv)
 S3method(print, mmfrac)
@@ -199,6 +206,7 @@ S3method(toLatex, yuima)
 S3method(toLatex, yuima.model)
 S3method(toLatex, yuima.carma)
 S3method(toLatex, yuima.cogarch)
+S3method(rbind, yuima.model)
 
 S3method(plot, yuima.llag) # Oct. 10, 2015
 S3method(plot, yuima.mllag) # Oct. 10, 2015

NEWS

@@ -39,3 +39,5 @@
                   added mllag.R, mllag.Rd (multiple lead-lag detector);
                           spectralcov.R, spectralcov.Rd (spectral covariance estimator)
 2016/01/14: modified rng.R, rng.Rd, adaBayes.Rd, qmle.Rd, setModel.Rd, spectralcov.Rd
+2016/05/26: added rpts and rnts in rng.R and the corresponding c language file
+

R/ClassCogarch.R

@@ -1,8 +1,8 @@
 # In order to deal the cogarch model in the \texttt{yuima} package
-# We define a new class called \texttt{yuima.cogarch} and its structure 
+# 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 
+# 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}.
@@ -30,34 +30,37 @@ setClass("yuima.cogarch",
 
 # Class 'gmm.cogarch'
 
-setClass("cogarch.gmm",representation(
-  model = "yuima.cogarch",
+setClass("cogarch.est",representation(
+  yuima = "yuima",
   objFun="character"),
   contains="mle"
 )
 
 
-setClass("summary.cogarch.gmm",representation(    objFun = "ANY",
-                                                  objFunVal = "ANY" ),
-         contains="summary.mle"
+setClass("summary.cogarch.est",
+  representation(objFun = "ANY",
+  objFunVal = "ANY"),
+  contains="summary.mle"
 )
 
 
 
-setClass("cogarch.gmm.incr",representation(Incr.Lev = "ANY",
-                                           logL.Incr = "ANY"
+setClass("cogarch.est.incr",
+         representation(Incr.Lev = "ANY",
+         logL.Incr = "ANY"
 ),
-contains="cogarch.gmm"
+contains="cogarch.est"
 )
 
-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("summary.cogarch.est.incr",
+         representation(logL.Incr = "ANY",
+                        MeanI = "ANY",
+                        SdI = "ANY",
+                        logLI = "ANY",
+                        TypeI = "ANY",
+                        NumbI = "ANY",
+                        StatI ="ANY"),
+         contains="summary.cogarch.est"
 )
 
 
@@ -71,12 +74,12 @@ setClass("summary.cogarch.gmm.incr",representation(logL.Incr = "ANY",
 # ),
 # contains="mle"
 # )
-# 
+#
 # setClass("cogarch.gmm",representation(
 #   model = "yuima.cogarch",
 #   objFun="character"),
 #   contains="mle"
 # )
 # setClass("gmm.cogarch",
 #   contains="mle"
-#   )  
+#   )