version 2.4.7

DESCRIPTION

@@ -1,30 +1,31 @@
 Package: smooth
 Type: Package
 Title: Forecasting Using State Space Models
-Version: 2.4.6
-Date: 2018-08-25
+Version: 2.4.7
+Date: 2018-11-30
 Authors@R: person("Ivan", "Svetunkov", email = "ivan@svetunkov.ru", role = c("aut", "cre"),
                   comment="Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK")
 URL: https://github.com/config-i1/smooth
 BugReports: https://github.com/config-i1/smooth/issues
+Language: en-GB
 Description: Functions implementing Single Source of Error state space models for purposes
              of time series analysis and forecasting. The package includes Exponential Smoothing,
              SARIMA, Complex Exponential Smoothing, Simple Moving Average, Vector Exponential
              Smoothing in state space forms, several simulation functions and intermittent demand
              state space models.
 License: GPL (>= 2)
-Depends: R (>= 3.0.2), greybox (>= 0.2.3)
+Depends: R (>= 3.0.2), greybox (>= 0.3.3)
 Imports: Rcpp (>= 0.12.3), stats, graphics, forecast, nloptr, utils,
         zoo
 LinkingTo: Rcpp, RcppArmadillo (>= 0.8.100.0.0)
 Suggests: Mcomp, numDeriv, testthat, knitr, rmarkdown
 VignetteBuilder: knitr
-RoxygenNote: 6.1.0
+RoxygenNote: 6.1.1
 Encoding: UTF-8
 NeedsCompilation: yes
-Packaged: 2018-08-25 14:46:09 UTC; config
+Packaged: 2018-11-30 15:46:23 UTC; isvetunkov
 Author: Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics
     and Forecasting, Lancaster University, UK)
 Maintainer: Ivan Svetunkov <ivan@svetunkov.ru>
 Repository: CRAN
-Date/Publication: 2018-08-25 20:30:03 UTC
+Date/Publication: 2018-12-02 17:00:03 UTC

MD5

@@ -1,80 +1,82 @@
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-ff51e58657dba340acc5d41e277b9644 *NAMESPACE
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-a34fbc6a7f7369dc8f7c2c73fcbfb91c *man/graphmaker.Rd
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 fd6e4f065a394c2448e457b72ee7fa1a *man/gum.Rd
 f821975364d4bcbf53d31f60f3465eeb *man/hm.Rd
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 0f3268c8a858c058a3fa8b8939767d2e *man/orders.Rd
@@ -85,34 +87,36 @@ e3fa8863cbd2961adf92020758b9023f *man/sim.es.Rd
 d40bde53f07922f4644b9c4085ec462f *man/sim.gum.Rd
 faae0aea4ae613e13182b7d58fbb09aa *man/sim.sma.Rd
 91cc377a2c8c0be063e1c33d9b5dadd2 *man/sim.ssarima.Rd
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 b0fc4c221b2ce4041102b564fcc9ec41 *man/sma.Rd
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 a0993d7de7f0b96415bb7b69f66b60ab *man/sowhat.Rd
 c367f5db5c64590c66409c72dc7c276d *man/ssarima.Rd
-bdcb405265aa21122486b35bf23203fc *man/ves.Rd
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 9ceb1e025b3bd37d79eb21bc0c86608f *man/viss.Rd
 9859afefaf6500832d7469cadbbb28c8 *src/Makevars
 a6850c2998c396b505104b800670480e *src/Makevars.win
-04fa2a152380d558869cd7f82bc8c13e *src/RcppExports.cpp
+a6173d40c122e266ed41576c49f4f337 *src/RcppExports.cpp
 9fb0915a4e7ffe192150808e5e890c69 *src/registerDynamicSymbol.c
-8a9d40377eb3431437af2dcd5444309b *src/ssgeneralfun.cpp
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 71a168fa8cd1314f43dd0aeeea113dc9 *src/vSimulator.cpp
-4119c3d7a998e2e8fcea82df2990622a *src/vssGeneral.cpp
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-d0802fb797489260bbc5d5c35df72182 *tests/testthat/test_ges.R
+3d75b6cb57922b211cb49f57c8a460f3 *tests/testthat/test_ges.R
 a312218ee6efc438b2d1c151666e13be *tests/testthat/test_iss.R
 69e2784165cabce3cf19514c0907df6c *tests/testthat/test_simulate.R
 5903710aa5287a11fcfba27b84b5e1a7 *tests/testthat/test_ssarima.R
 9e08bd4ab8213411a400e978ca465058 *tests/testthat/test_ves.R
 9a6306fb84d1f4cf62fdecb1966aa0ab *vignettes/ces.Rmd
 aee1e5b2cf8c8bbc33a28000ea3e3489 *vignettes/es.Rmd
-4f19f338bd996138d1e342a101f09434 *vignettes/ges.Rmd
-85ff7ec2014bf6182412035bd2f907bb *vignettes/simulate.Rmd
+ef1761018718beea9ca5db7d97e308e8 *vignettes/gum.Rmd
+41c18d298a5e11c969396e8130fdbea2 *vignettes/library.bib
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 8cb859d21072dff613ae6439ef6bdd91 *vignettes/sma.Rmd
-1242fd5c9a50aa7079d1b1a4be05ec45 *vignettes/smooth.Rmd
+f2be0cff7be52faff06f2a377333a8c6 *vignettes/smooth-Documentation.pdf
+27e418430dc1bec1d4e1f894bd65603d *vignettes/smooth.Rmd
 12ee105ca06d918914d7e00cd9164fc3 *vignettes/ssarima.Rmd
-8819bbfe9236db20d7bb0aee23a8787a *vignettes/ves.Rmd
+f6d22009d7bf0a0f8bae6f4327f945f0 *vignettes/ves.Rmd

NAMESPACE

@@ -25,6 +25,7 @@ S3method(logLik,iss)
 S3method(logLik,smooth)
 S3method(logLik,smooth.sim)
 S3method(logLik,viss)
+S3method(logLik,vsmooth)
 S3method(modelName,Arima)
 S3method(modelName,ar)
 S3method(modelName,default)
@@ -66,7 +67,6 @@ S3method(print,smooth.forecast)
 S3method(print,smooth.sim)
 S3method(print,viss)
 S3method(print,vsmooth)
-S3method(sigma,ets)
 S3method(sigma,smooth)
 S3method(sigma,vsmooth)
 S3method(simulate,smooth)
@@ -100,10 +100,19 @@ export(forecast)
 export(forecast.smooth)
 export(ges)
 export(getResponse)
-export(graphmaker)
+export(gsi)
 export(gum)
 export(ham)
 export(hm)
+export(is.iss)
+export(is.msarima)
+export(is.smooth)
+export(is.smooth.forecast)
+export(is.smooth.sim)
+export(is.smoothC)
+export(is.viss)
+export(is.vsmooth)
+export(is.vsmooth.sim)
 export(iss)
 export(lags)
 export(modelName)
@@ -130,6 +139,7 @@ import(Rcpp)
 import(zoo)
 importFrom(forecast,forecast)
 importFrom(forecast,getResponse)
+importFrom(forecast,is.ets)
 importFrom(graphics,abline)
 importFrom(graphics,layout)
 importFrom(graphics,legend)
@@ -143,6 +153,7 @@ importFrom(greybox,BICc)
 importFrom(greybox,dlaplace)
 importFrom(greybox,ds)
 importFrom(greybox,errorType)
+importFrom(greybox,graphmaker)
 importFrom(greybox,nParam)
 importFrom(greybox,plaplace)
 importFrom(greybox,pointLik)

NEWS

@@ -1,3 +1,24 @@
+smooth v2.4.7 (Release data: 2018-11-30)
+==============
+
+Changes:
+* graphmaker() is now moved to greybox package.
+* Moved sigma.ets() to greybox package.
+* pls.smooth() returns more or less reasonable values, when the model overfits the data.
+* A set of is-functions, checking whether the object is produced by smooth functions.
+* New function - gsi() - ETS with Group Seasonal Indices. This is based on VES with some restrictions. The work in progress...
+* The number of degrees of freedom in vector models (ves and gsi) is now calculated per series, not overall. This corresponds to Lutkepohl (2005), p.75.
+* Minor updates in the vignettes for ves with references to Lutkepohl and a couple of papers.
+* ETS(M,Z,Z) now returns correct semi and non parametric intervals in case of intermittent data.
+* Given the recent update in greybox, changed the "b" to "scale" in distribution functions.
+
+Bugfixes
+* ETS(M,X,X) sometimes could not be optimised because of the too high smoothing parameters values and negative initials. This should be fixed now.
+* sim.ves() wasn't working correctly in case of AAA models and provided initials.
+* The error generated in sim.ves() was always multiplied by the initial state, so if it was zero, then nothing would happen.
+* Found bug due to which the exogenous variables wer ignored for the forecasts from ETS(M,Z,Z) models.
+
+
 smooth v2.4.6 (Release data: 2018-08-25)
 ==============
 

R/RcppExports.R

@@ -9,8 +9,8 @@ polyMultwrap <- function(polyVec1, polyVec2) {
     .Call('_smooth_polyMultwrap', PACKAGE = 'smooth', polyVec1, polyVec2)
 }
 
-initparams <- function(Ttype, Stype, datafreq, obsR, obsallR, yt, damped, phi, smoothingparameters, initialstates, seasonalcoefs) {
-    .Call('_smooth_initparams', PACKAGE = 'smooth', Ttype, Stype, datafreq, obsR, obsallR, yt, damped, phi, smoothingparameters, initialstates, seasonalcoefs)
+initparams <- function(Etype, Ttype, Stype, datafreq, obsR, obsallR, yt, damped, phi, smoothingparameters, initialstates, seasonalcoefs) {
+    .Call('_smooth_initparams', PACKAGE = 'smooth', Etype, Ttype, Stype, datafreq, obsR, obsallR, yt, damped, phi, smoothingparameters, initialstates, seasonalcoefs)
 }
 
 etsmatrices <- function(matvt, vecg, phi, Cvalues, ncomponentsR, modellags, fittertype, Ttype, Stype, nexovars, matat, estimpersistence, estimphi, estiminit, estiminitseason, estimxreg, matFX, vecgX, gowild, estimFX, estimgX, estiminitX) {

R/automsarima.R

@@ -728,7 +728,7 @@ auto.msarima <- function(data, orders=list(ar=c(3,3),i=c(2,1),ma=c(3,3)), lags=c
                           updateX=updateX, persistenceX=persistenceX, transitionX=transitionX,
                           ICs=ICs,ICw=icWeights,cf=NULL,cfType=cfType,accuracy=errormeasures);
 
-        bestModel <- structure(bestModel,class="smooth");
+        bestModel <- structure(bestModel,class=c("smooth","msarima"));
     }
     else{
         #### Reestimate the best model in order to get rid of bias ####

R/cma.R

@@ -2,7 +2,7 @@
 #'
 #' Function constructs centered moving average based on state space SMA
 #'
-#' If the otder is odd, then the function constructs SMA(order) and
+#' If the order is odd, then the function constructs SMA(order) and
 #' shifts it back in time. Otherwise an AR(order+1) model is constructed
 #' with the preset parameters:
 #'
@@ -90,7 +90,7 @@ cma <- function(data, order=NULL, silent=TRUE){
     }
 
     ##### data #####
-    if(any(class(data)=="smooth.sim")){
+    if(any(is.smooth.sim(data))){
         data <- data$data;
     }
     else if(class(data)=="Mdata"){