CRAN Package Check Results for Package dlmodeler

Last updated on 2018-06-17 19:49:37 CEST.

Check Details

Version: 1.4-2
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: ‘FKF’
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-osx-x86_64

Version: 1.4-2
Check: dependencies in R code
Result: NOTE
    'library' or 'require' calls in package code:
     ‘FKF’ ‘dlm’
     Please use :: or requireNamespace() instead.
     See section 'Suggested packages' in the 'Writing R Extensions' manual.
    Missing or unexported object: ‘dlm::dlmSmooth.dlmFiltered’
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.4-2
Check: S3 generic/method consistency
Result: NOTE
    Found the following apparent S3 methods exported but not registered:
     %%.dlmodeler *.dlmodeler +.dlmodeler
    See section ‘Registering S3 methods’ in the ‘Writing R Extensions’
    manual.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64

Version: 1.4-2
Check: R code for possible problems
Result: NOTE
    dlmodeler.extract: no visible global function definition for ‘qnorm’
    dlmodeler.fit.MAD: no visible global function definition for ‘optim’
    dlmodeler.fit.MAPE: no visible global function definition for ‘optim’
    dlmodeler.fit.MLE: no visible global function definition for ‘optim’
    dlmodeler.fit.MSE: no visible global function definition for ‘optim’
    dlmodeler.fit.sigma : fit.fun: no visible global function definition
     for ‘var’
    dlmodeler.fit.sigma: no visible global function definition for ‘optim’
    dlmodeler.forecast: no visible global function definition for ‘qnorm’
    Undefined global functions or variables:
     optim qnorm var
    Consider adding
     importFrom("stats", "optim", "qnorm", "var")
    to your NAMESPACE file.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64

Version: 1.4-2
Check: examples
Result: ERROR
    Running examples in ‘dlmodeler-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: dlmodeler.forecast
    > ### Title: Forecast function
    > ### Aliases: dlmodeler.forecast
    > ### Keywords: dlm kalman forecast
    >
    > ### ** Examples
    >
    > require(dlmodeler)
    >
    > # generate some quarterly data
    > n <- 80
    > level <- 12
    > sigma <- .75
    > season <- c(5,6,8,2)
    > y <- level + 3*sin((1:n)/10) + rep(season,n/4) + rnorm(n, 0, sigma)
    > y <- matrix(y,nrow=1)
    >
    > # fit a stochastic level + quarterly seasonal model to the data by
    > # maximum likelihood estimation
    > build.fun <- function(p) {
    + sigmaH <- exp(p[1])
    + sigmaQ <- exp(p[2])*sigmaH
    + mod <- dlmodeler.build.polynomial(0,sigmaH=sigmaH,sigmaQ=sigmaQ) +
    + dlmodeler.build.dseasonal(4,sigmaH=0)
    + return(mod)
    + }
    > fit <- dlmodeler.fit.MLE(y, build.fun, c(0,0))
    Error in dlmodeler.build.polynomial(0, sigmaH = sigmaH, sigmaQ = sigmaQ) + :
     non-numeric argument to binary operator
    Calls: dlmodeler.fit.MLE ... optim -> <Anonymous> -> fn -> dlmodeler.filter -> build.fun
    Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.4-2
Check: tests
Result: ERROR
     Running ‘tests.R’ [0s/1s]
    Running the tests in ‘tests/tests.R’ failed.
    Complete output:
     >
     > require(dlmodeler)
     Loading required package: dlmodeler
     >
     > ####################
     > # tests for 'core' #
     > ####################
     >
     > # create a DLM by specifying its vectors and matrices
     > # check if the model is valid
     > mod <- dlmodeler.build(
     + a0 = c(0,0), # initial state: (level, trend)
     + P0 = diag(c(0,0)), # initial state variance set to...
     + P0inf = diag(2), # ...use exact diffuse initialization
     + matrix(c(1,0,1,1),2,2), # state transition matrix
     + diag(c(1,1)), # state disturbance selection matrix
     + diag(c(.5,.05)), # state disturbance variance matrix
     + matrix(c(1,0),1,2), # observation design matrix
     + matrix(1,1,1) # observation disturbance variance matrix
     + )
     >
     > print(mod)
     constant dlmodel(state dim=2, dist dim=2, obs dim=1) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==1)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # an empty DLM with 4 state variables (3 of which are stochastic)
     > # and bi-variate observations, check if the model is valid
     > mod <- dlmodeler.build(dimensions=c(4,3,2))
     >
     > print(mod)
     constant dlmodel(state dim=4, dist dim=3, obs dim=2) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==4)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==3)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # operations on matrices
     > v1 <- matrix(1:9,nrow=3,ncol=3)
     > v2 <- array(1:18,dim=c(3,3,2))
     >
     > m1 <- dlmodeler:::dlmodeler.timevar.fun(v1,v1,function(x,y) x+y)
     > if(!(sum(abs(m1-v1-v1))==0)) stop("unit test failed")
     > m21 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m21[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m21[,,2]-v1-v1-9))==0)) stop("unit test failed")
     > m22 <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,function(x,y) x+y)
     > if(!(sum(abs(m22[,,1]-m21[,,1]))==0)) stop("unit test failed")
     > if(!(sum(abs(m22[,,2]-m21[,,2]))==0)) stop("unit test failed")
     > m3 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m3[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m3[,,2]-v1-matrix(10:18,nrow=3,ncol=3)))==0)) stop("unit test failed")
     >
     > mt <- matrix(c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,9,9,0,0,0,9,9,9,0,0,0,9,9,9),nrow=6,ncol=6)
     > md <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,dlmodeler:::dlmodeler.bdiag)
     > if(!(sum(abs(md[,,2]-md[,,1]-mt))==0)) stop("unit test failed")
     >
     > # operators
     > m1 <- dlmodeler.build.dseasonal(12)
     > m2 <- dlmodeler.build.polynomial(0)
     > m3 <- dlmodeler.build.dseasonal(5)
     > m4 <- dlmodeler.build.constant(4)
     >
     > m1+m2+m3
     Error in m1 + m2 : non-numeric argument to binary operator
     Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.4-2
Check: tests
Result: ERROR
     Running ‘tests.R’ [15s/19s]
    Running the tests in ‘tests/tests.R’ failed.
    Complete output:
     >
     > require(dlmodeler)
     Loading required package: dlmodeler
     >
     > ####################
     > # tests for 'core' #
     > ####################
     >
     > # create a DLM by specifying its vectors and matrices
     > # check if the model is valid
     > mod <- dlmodeler.build(
     + a0 = c(0,0), # initial state: (level, trend)
     + P0 = diag(c(0,0)), # initial state variance set to...
     + P0inf = diag(2), # ...use exact diffuse initialization
     + matrix(c(1,0,1,1),2,2), # state transition matrix
     + diag(c(1,1)), # state disturbance selection matrix
     + diag(c(.5,.05)), # state disturbance variance matrix
     + matrix(c(1,0),1,2), # observation design matrix
     + matrix(1,1,1) # observation disturbance variance matrix
     + )
     >
     > print(mod)
     constant dlmodel(state dim=2, dist dim=2, obs dim=1) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==1)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # an empty DLM with 4 state variables (3 of which are stochastic)
     > # and bi-variate observations, check if the model is valid
     > mod <- dlmodeler.build(dimensions=c(4,3,2))
     >
     > print(mod)
     constant dlmodel(state dim=4, dist dim=3, obs dim=2) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==4)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==3)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # operations on matrices
     > v1 <- matrix(1:9,nrow=3,ncol=3)
     > v2 <- array(1:18,dim=c(3,3,2))
     >
     > m1 <- dlmodeler:::dlmodeler.timevar.fun(v1,v1,function(x,y) x+y)
     > if(!(sum(abs(m1-v1-v1))==0)) stop("unit test failed")
     > m21 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m21[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m21[,,2]-v1-v1-9))==0)) stop("unit test failed")
     > m22 <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,function(x,y) x+y)
     > if(!(sum(abs(m22[,,1]-m21[,,1]))==0)) stop("unit test failed")
     > if(!(sum(abs(m22[,,2]-m21[,,2]))==0)) stop("unit test failed")
     > m3 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m3[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m3[,,2]-v1-matrix(10:18,nrow=3,ncol=3)))==0)) stop("unit test failed")
     >
     > mt <- matrix(c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,9,9,0,0,0,9,9,9,0,0,0,9,9,9),nrow=6,ncol=6)
     > md <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,dlmodeler:::dlmodeler.bdiag)
     > if(!(sum(abs(md[,,2]-md[,,1]-mt))==0)) stop("unit test failed")
     >
     > # operators
     > m1 <- dlmodeler.build.dseasonal(12)
     > m2 <- dlmodeler.build.polynomial(0)
     > m3 <- dlmodeler.build.dseasonal(5)
     > m4 <- dlmodeler.build.constant(4)
     >
     > m1+m2+m3
     constant dlmodel(state dim=16, dist dim=3, obs dim=1) 'dseasonal+level+dseasonal'
     - model has 5 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal
     - model has 1 unknown parameter
     > m1+m2+m3+m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=1) 'dseasonal+level+dseasonal+constant'
     - model has 7 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal, constant, dseasonal+level+dseasonal+constant
     - model has 1 unknown parameter
     > m1%%m2%%m3
     constant dlmodel(state dim=16, dist dim=3, obs dim=3) 'dseasonal&level&dseasonal'
     - model has 5 components: dseasonal, level, dseasonal&level, dseasonal, dseasonal&level&dseasonal
     - model has 3 unknown parameters
     > m1%%m2%%m3%%m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=4) 'dseasonal&level&dseasonal&constant'
     - model has 7 components: dseasonal, level, dseasonal&level, dseasonal, dseasonal&level&dseasonal, constant, dseasonal&level&dseasonal&constant
     - model has 3 unknown parameters
     > dlmodeler.add(m1,1)
     constant dlmodel(state dim=12, dist dim=2, obs dim=1) 'dseasonal+constant'
     - model has 3 components: dseasonal, constant, dseasonal+constant
     - model has 1 unknown parameter
     > m1+1+m2
     constant dlmodel(state dim=13, dist dim=3, obs dim=1) 'dseasonal+constant+level'
     - model has 5 components: dseasonal, constant, dseasonal+constant, level, dseasonal+constant+level
     - model has 1 unknown parameter
     > dlmodeler.add(1,m1)
     constant dlmodel(state dim=12, dist dim=2, obs dim=1) 'constant+dseasonal'
     - model has 3 components: constant, dseasonal, constant+dseasonal
     - model has 1 unknown parameter
     > 1+m1+m2
     constant dlmodel(state dim=13, dist dim=3, obs dim=1) 'constant+dseasonal+level'
     - model has 5 components: constant, dseasonal, constant+dseasonal, level, constant+dseasonal+level
     - model has 1 unknown parameter
     > dlmodeler.multiply(m2,2)
     constant dlmodel(state dim=1, dist dim=1, obs dim=1) 'level'
     - model has 1 component: level
     - model has 1 unknown parameter
     > m1*5
     constant dlmodel(state dim=11, dist dim=1, obs dim=1) 'dseasonal'
     - model has 1 component: dseasonal
     - model has 1 unknown parameter
     > dlmodeler.multiply(2,m2)
     constant dlmodel(state dim=1, dist dim=1, obs dim=1) 'level'
     - model has 1 component: level
     - model has 1 unknown parameter
     > 5*m1
     constant dlmodel(state dim=11, dist dim=1, obs dim=1) 'dseasonal'
     - model has 1 component: dseasonal
     - model has 1 unknown parameter
     > 2*m1+3*m2+4*m3+5*m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=1) 'dseasonal+level+dseasonal+constant'
     - model has 7 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal, constant, dseasonal+level+dseasonal+constant
     - model has 1 unknown parameter
     > (2*m1+3*m2+4*m3+5*m4)$Zt
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
     [1,] 2 0 0 0 0 0 0 0 0 0 0 3 4 0
     [,15] [,16] [,17]
     [1,] 0 0 5
     >
     >
     > #####################################
     > # univariate and time-varying tests #
     > #####################################
     > # this example is fairly complete, covers 'add', 'filter', 'smooth'
     > # 'extract', 'polynomial', 'dseasonal', 'tseasonal', 'regression'
     > # test it with various backends
     >
     > # generate some data
     > set.seed(19820605)
     > N <- 365*10
     > t <- c(1:N,rep(NA,365))
     > a <- rnorm(N+365,0,.5)
     > y <- pi + cos(2*pi*t/365.25) + .25*sin(2*pi*t/365.25*3) +
     + exp(1)*a + rnorm(N+365,0,.5)
     >
     > # build a model for this data
     > m1 <- dlmodeler.build.polynomial(0,sigmaH=.5,name='level')
     > m2 <- dlmodeler.build.dseasonal(7,sigmaH=0,name='week')
     > m3 <- dlmodeler.build.tseasonal(365.25,3,sigmaH=0,name='year')
     > m4 <- dlmodeler.build.regression(a,sigmaH=0,name='reg')
     > m <- m1+m2+m3+m4
     >
     > test.backend <- function(backend)
     + {
     + cat(backend,"\n")
     + test.ok <- TRUE
     +
     + system.time(f <- dlmodeler.filter(y, m, raw.result=TRUE, backend=backend))
     +
     + # extract all the components
     + m.state.mean <- dlmodeler.extract(f,m,type="state",value="mean")
     + m.state.cov <- dlmodeler.extract(f,m,type="state",value="covariance")
     + m.obs.mean <- dlmodeler.extract(f,m,type="observation",value="mean")
     + m.obs.cov <- dlmodeler.extract(f,m,type="observation",value="covariance")
     + m.obs.int <- dlmodeler.extract(f,m,type="observation",value="interval",prob=.01)
     +
     + par(mfrow=c(2,1))
     +
     + # show the one step ahead forecasts & 99\% prediction intervals
     + #plot(y,xlim=c(N-10,N+30))
     + #lines(m.obs.int$mymodel$upper[1,],col='light grey')
     + #lines(m.obs.int$mymodel$lower[1,],col='light grey')
     + #lines(m.obs.int$mymodel$mean[1,],col=2)
     +
     + # see to which values the filter has converged:
     + test.ok <- test.ok & abs(m.state.mean$level[,N]-pi)/pi < .05 # should be close to pi
     + test.ok <- test.ok & abs(mean(abs(m.state.mean$week[,N]))) < .05 # should be close to 0
     + test.ok <- test.ok & abs(m.state.mean$year[1,N]-1) < .05 # should be close to 1
     + test.ok <- test.ok & abs(m.state.mean$year[6,N]-.25) < .05 # should be close to .25
     + test.ok <- test.ok & abs(m.state.mean$reg[,N]-exp(1))/exp(1) < .05 # should be close to e
     +
     + # show the filtered level+year components
     + #plot(m.obs.mean$level[1,]+m.obs.mean$year[1,],
     + # type='l',ylim=c(pi-2,pi+2),col='light green',
     + # ylab="smoothed & filtered level+year")
     +
     + if(backend!='FKF') {
     + system.time(s <- dlmodeler.smooth(f))
     +
     + # show the smoothed level+year components
     + s.obs.mean <- dlmodeler.extract(s,m,type="observation",value="mean")
     + #lines(s.obs.mean$level[1,]+s.obs.mean$year[1,],type='l',
     + # ylim=c(pi-2,pi+2),col='dark green')
     + }
     +
     + return(test.ok)
     + }
     >
     > #if(require('KFAS')) {
     > # cat('Found package KFAS. Testing...\n')
     > if(!(test.backend('KFAS'))) stop("KFAS unit test failed")
     KFAS
     Warning message:
     In KFAS::KFS(filt$raw.result$model, smoothing = "state") :
     Possible error in smoothing: Negative variances in V, check the model or try changing the tolerance parameter tol or P1/P1inf of the model.
     > #}
     >
     > if(require('FKF')) {
     + cat('Found package FKF. Testing...\n')
     + if(!(test.backend('FKF'))) stop("FKF unit test failed")
     + }
     Loading required package: FKF
     Warning message:
     In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
     there is no package called 'FKF'
     >
     > if(require('dlm')) {
     + cat('Found package dlm. Testing...\n')
     + if(!(test.backend('dlm'))) stop("dlm unit test failed")
     + }
     Loading required package: dlm
     Found package dlm. Testing...
     dlm
     Error: 'dlmSmooth.dlmFiltered' is not an exported object from 'namespace:dlm'
     Timing stopped at: 0 0 0.001
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.4-2
Check: tests
Result: ERROR
     Running ‘tests.R’
    Running the tests in ‘tests/tests.R’ failed.
    Complete output:
     >
     > require(dlmodeler)
     Loading required package: dlmodeler
     >
     > ####################
     > # tests for 'core' #
     > ####################
     >
     > # create a DLM by specifying its vectors and matrices
     > # check if the model is valid
     > mod <- dlmodeler.build(
     + a0 = c(0,0), # initial state: (level, trend)
     + P0 = diag(c(0,0)), # initial state variance set to...
     + P0inf = diag(2), # ...use exact diffuse initialization
     + matrix(c(1,0,1,1),2,2), # state transition matrix
     + diag(c(1,1)), # state disturbance selection matrix
     + diag(c(.5,.05)), # state disturbance variance matrix
     + matrix(c(1,0),1,2), # observation design matrix
     + matrix(1,1,1) # observation disturbance variance matrix
     + )
     >
     > print(mod)
     constant dlmodel(state dim=2, dist dim=2, obs dim=1) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==1)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # an empty DLM with 4 state variables (3 of which are stochastic)
     > # and bi-variate observations, check if the model is valid
     > mod <- dlmodeler.build(dimensions=c(4,3,2))
     >
     > print(mod)
     constant dlmodel(state dim=4, dist dim=3, obs dim=2) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==4)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==3)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # operations on matrices
     > v1 <- matrix(1:9,nrow=3,ncol=3)
     > v2 <- array(1:18,dim=c(3,3,2))
     >
     > m1 <- dlmodeler:::dlmodeler.timevar.fun(v1,v1,function(x,y) x+y)
     > if(!(sum(abs(m1-v1-v1))==0)) stop("unit test failed")
     > m21 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m21[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m21[,,2]-v1-v1-9))==0)) stop("unit test failed")
     > m22 <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,function(x,y) x+y)
     > if(!(sum(abs(m22[,,1]-m21[,,1]))==0)) stop("unit test failed")
     > if(!(sum(abs(m22[,,2]-m21[,,2]))==0)) stop("unit test failed")
     > m3 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m3[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m3[,,2]-v1-matrix(10:18,nrow=3,ncol=3)))==0)) stop("unit test failed")
     >
     > mt <- matrix(c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,9,9,0,0,0,9,9,9,0,0,0,9,9,9),nrow=6,ncol=6)
     > md <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,dlmodeler:::dlmodeler.bdiag)
     > if(!(sum(abs(md[,,2]-md[,,1]-mt))==0)) stop("unit test failed")
     >
     > # operators
     > m1 <- dlmodeler.build.dseasonal(12)
     > m2 <- dlmodeler.build.polynomial(0)
     > m3 <- dlmodeler.build.dseasonal(5)
     > m4 <- dlmodeler.build.constant(4)
     >
     > m1+m2+m3
     constant dlmodel(state dim=16, dist dim=3, obs dim=1) 'dseasonal+level+dseasonal'
     - model has 5 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal
     - model has 1 unknown parameter
     > m1+m2+m3+m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=1) 'dseasonal+level+dseasonal+constant'
     - model has 7 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal, constant, dseasonal+level+dseasonal+constant
     - model has 1 unknown parameter
     > m1%%m2%%m3
     constant dlmodel(state dim=16, dist dim=3, obs dim=3) 'dseasonal&level&dseasonal'
     - model has 5 components: dseasonal, level, dseasonal&level, dseasonal, dseasonal&level&dseasonal
     - model has 3 unknown parameters
     > m1%%m2%%m3%%m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=4) 'dseasonal&level&dseasonal&constant'
     - model has 7 components: dseasonal, level, dseasonal&level, dseasonal, dseasonal&level&dseasonal, constant, dseasonal&level&dseasonal&constant
     - model has 3 unknown parameters
     > dlmodeler.add(m1,1)
     constant dlmodel(state dim=12, dist dim=2, obs dim=1) 'dseasonal+constant'
     - model has 3 components: dseasonal, constant, dseasonal+constant
     - model has 1 unknown parameter
     > m1+1+m2
     constant dlmodel(state dim=13, dist dim=3, obs dim=1) 'dseasonal+constant+level'
     - model has 5 components: dseasonal, constant, dseasonal+constant, level, dseasonal+constant+level
     - model has 1 unknown parameter
     > dlmodeler.add(1,m1)
     constant dlmodel(state dim=12, dist dim=2, obs dim=1) 'constant+dseasonal'
     - model has 3 components: constant, dseasonal, constant+dseasonal
     - model has 1 unknown parameter
     > 1+m1+m2
     constant dlmodel(state dim=13, dist dim=3, obs dim=1) 'constant+dseasonal+level'
     - model has 5 components: constant, dseasonal, constant+dseasonal, level, constant+dseasonal+level
     - model has 1 unknown parameter
     > dlmodeler.multiply(m2,2)
     constant dlmodel(state dim=1, dist dim=1, obs dim=1) 'level'
     - model has 1 component: level
     - model has 1 unknown parameter
     > m1*5
     constant dlmodel(state dim=11, dist dim=1, obs dim=1) 'dseasonal'
     - model has 1 component: dseasonal
     - model has 1 unknown parameter
     > dlmodeler.multiply(2,m2)
     constant dlmodel(state dim=1, dist dim=1, obs dim=1) 'level'
     - model has 1 component: level
     - model has 1 unknown parameter
     > 5*m1
     constant dlmodel(state dim=11, dist dim=1, obs dim=1) 'dseasonal'
     - model has 1 component: dseasonal
     - model has 1 unknown parameter
     > 2*m1+3*m2+4*m3+5*m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=1) 'dseasonal+level+dseasonal+constant'
     - model has 7 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal, constant, dseasonal+level+dseasonal+constant
     - model has 1 unknown parameter
     > (2*m1+3*m2+4*m3+5*m4)$Zt
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
     [1,] 2 0 0 0 0 0 0 0 0 0 0 3 4 0
     [,15] [,16] [,17]
     [1,] 0 0 5
     >
     >
     > #####################################
     > # univariate and time-varying tests #
     > #####################################
     > # this example is fairly complete, covers 'add', 'filter', 'smooth'
     > # 'extract', 'polynomial', 'dseasonal', 'tseasonal', 'regression'
     > # test it with various backends
     >
     > # generate some data
     > set.seed(19820605)
     > N <- 365*10
     > t <- c(1:N,rep(NA,365))
     > a <- rnorm(N+365,0,.5)
     > y <- pi + cos(2*pi*t/365.25) + .25*sin(2*pi*t/365.25*3) +
     + exp(1)*a + rnorm(N+365,0,.5)
     >
     > # build a model for this data
     > m1 <- dlmodeler.build.polynomial(0,sigmaH=.5,name='level')
     > m2 <- dlmodeler.build.dseasonal(7,sigmaH=0,name='week')
     > m3 <- dlmodeler.build.tseasonal(365.25,3,sigmaH=0,name='year')
     > m4 <- dlmodeler.build.regression(a,sigmaH=0,name='reg')
     > m <- m1+m2+m3+m4
     >
     > test.backend <- function(backend)
     + {
     + cat(backend,"\n")
     + test.ok <- TRUE
     +
     + system.time(f <- dlmodeler.filter(y, m, raw.result=TRUE, backend=backend))
     +
     + # extract all the components
     + m.state.mean <- dlmodeler.extract(f,m,type="state",value="mean")
     + m.state.cov <- dlmodeler.extract(f,m,type="state",value="covariance")
     + m.obs.mean <- dlmodeler.extract(f,m,type="observation",value="mean")
     + m.obs.cov <- dlmodeler.extract(f,m,type="observation",value="covariance")
     + m.obs.int <- dlmodeler.extract(f,m,type="observation",value="interval",prob=.01)
     +
     + par(mfrow=c(2,1))
     +
     + # show the one step ahead forecasts & 99\% prediction intervals
     + #plot(y,xlim=c(N-10,N+30))
     + #lines(m.obs.int$mymodel$upper[1,],col='light grey')
     + #lines(m.obs.int$mymodel$lower[1,],col='light grey')
     + #lines(m.obs.int$mymodel$mean[1,],col=2)
     +
     + # see to which values the filter has converged:
     + test.ok <- test.ok & abs(m.state.mean$level[,N]-pi)/pi < .05 # should be close to pi
     + test.ok <- test.ok & abs(mean(abs(m.state.mean$week[,N]))) < .05 # should be close to 0
     + test.ok <- test.ok & abs(m.state.mean$year[1,N]-1) < .05 # should be close to 1
     + test.ok <- test.ok & abs(m.state.mean$year[6,N]-.25) < .05 # should be close to .25
     + test.ok <- test.ok & abs(m.state.mean$reg[,N]-exp(1))/exp(1) < .05 # should be close to e
     +
     + # show the filtered level+year components
     + #plot(m.obs.mean$level[1,]+m.obs.mean$year[1,],
     + # type='l',ylim=c(pi-2,pi+2),col='light green',
     + # ylab="smoothed & filtered level+year")
     +
     + if(backend!='FKF') {
     + system.time(s <- dlmodeler.smooth(f))
     +
     + # show the smoothed level+year components
     + s.obs.mean <- dlmodeler.extract(s,m,type="observation",value="mean")
     + #lines(s.obs.mean$level[1,]+s.obs.mean$year[1,],type='l',
     + # ylim=c(pi-2,pi+2),col='dark green')
     + }
     +
     + return(test.ok)
     + }
     >
     > #if(require('KFAS')) {
     > # cat('Found package KFAS. Testing...\n')
     > if(!(test.backend('KFAS'))) stop("KFAS unit test failed")
     KFAS
     Warning message:
     In KFAS::KFS(filt$raw.result$model, smoothing = "state") :
     Possible error in smoothing: Negative variances in V, check the model or try changing the tolerance parameter tol or P1/P1inf of the model.
     > #}
     >
     > if(require('FKF')) {
     + cat('Found package FKF. Testing...\n')
     + if(!(test.backend('FKF'))) stop("FKF unit test failed")
     + }
     Loading required package: FKF
     Warning message:
     In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
     there is no package called 'FKF'
     >
     > if(require('dlm')) {
     + cat('Found package dlm. Testing...\n')
     + if(!(test.backend('dlm'))) stop("dlm unit test failed")
     + }
     Loading required package: dlm
     Found package dlm. Testing...
     dlm
     Error: 'dlmSmooth.dlmFiltered' is not an exported object from 'namespace:dlm'
     Timing stopped at: 0 0.001 0
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 1.4-2
Check: tests
Result: ERROR
     Running 'tests.R' [16s]
    Running the tests in 'tests/tests.R' failed.
    Complete output:
     >
     > require(dlmodeler)
     Loading required package: dlmodeler
     >
     > ####################
     > # tests for 'core' #
     > ####################
     >
     > # create a DLM by specifying its vectors and matrices
     > # check if the model is valid
     > mod <- dlmodeler.build(
     + a0 = c(0,0), # initial state: (level, trend)
     + P0 = diag(c(0,0)), # initial state variance set to...
     + P0inf = diag(2), # ...use exact diffuse initialization
     + matrix(c(1,0,1,1),2,2), # state transition matrix
     + diag(c(1,1)), # state disturbance selection matrix
     + diag(c(.5,.05)), # state disturbance variance matrix
     + matrix(c(1,0),1,2), # observation design matrix
     + matrix(1,1,1) # observation disturbance variance matrix
     + )
     >
     > print(mod)
     constant dlmodel(state dim=2, dist dim=2, obs dim=1) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==1)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # an empty DLM with 4 state variables (3 of which are stochastic)
     > # and bi-variate observations, check if the model is valid
     > mod <- dlmodeler.build(dimensions=c(4,3,2))
     >
     > print(mod)
     constant dlmodel(state dim=4, dist dim=3, obs dim=2) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==4)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==3)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # operations on matrices
     > v1 <- matrix(1:9,nrow=3,ncol=3)
     > v2 <- array(1:18,dim=c(3,3,2))
     >
     > m1 <- dlmodeler:::dlmodeler.timevar.fun(v1,v1,function(x,y) x+y)
     > if(!(sum(abs(m1-v1-v1))==0)) stop("unit test failed")
     > m21 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m21[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m21[,,2]-v1-v1-9))==0)) stop("unit test failed")
     > m22 <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,function(x,y) x+y)
     > if(!(sum(abs(m22[,,1]-m21[,,1]))==0)) stop("unit test failed")
     > if(!(sum(abs(m22[,,2]-m21[,,2]))==0)) stop("unit test failed")
     > m3 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m3[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m3[,,2]-v1-matrix(10:18,nrow=3,ncol=3)))==0)) stop("unit test failed")
     >
     > mt <- matrix(c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,9,9,0,0,0,9,9,9,0,0,0,9,9,9),nrow=6,ncol=6)
     > md <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,dlmodeler:::dlmodeler.bdiag)
     > if(!(sum(abs(md[,,2]-md[,,1]-mt))==0)) stop("unit test failed")
     >
     > # operators
     > m1 <- dlmodeler.build.dseasonal(12)
     > m2 <- dlmodeler.build.polynomial(0)
     > m3 <- dlmodeler.build.dseasonal(5)
     > m4 <- dlmodeler.build.constant(4)
     >
     > m1+m2+m3
     constant dlmodel(state dim=16, dist dim=3, obs dim=1) 'dseasonal+level+dseasonal'
     - model has 5 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal
     - model has 1 unknown parameter
     > m1+m2+m3+m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=1) 'dseasonal+level+dseasonal+constant'
     - model has 7 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal, constant, dseasonal+level+dseasonal+constant
     - model has 1 unknown parameter
     > m1%%m2%%m3
     constant dlmodel(state dim=16, dist dim=3, obs dim=3) 'dseasonal&level&dseasonal'
     - model has 5 components: dseasonal, level, dseasonal&level, dseasonal, dseasonal&level&dseasonal
     - model has 3 unknown parameters
     > m1%%m2%%m3%%m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=4) 'dseasonal&level&dseasonal&constant'
     - model has 7 components: dseasonal, level, dseasonal&level, dseasonal, dseasonal&level&dseasonal, constant, dseasonal&level&dseasonal&constant
     - model has 3 unknown parameters
     > dlmodeler.add(m1,1)
     constant dlmodel(state dim=12, dist dim=2, obs dim=1) 'dseasonal+constant'
     - model has 3 components: dseasonal, constant, dseasonal+constant
     - model has 1 unknown parameter
     > m1+1+m2
     constant dlmodel(state dim=13, dist dim=3, obs dim=1) 'dseasonal+constant+level'
     - model has 5 components: dseasonal, constant, dseasonal+constant, level, dseasonal+constant+level
     - model has 1 unknown parameter
     > dlmodeler.add(1,m1)
     constant dlmodel(state dim=12, dist dim=2, obs dim=1) 'constant+dseasonal'
     - model has 3 components: constant, dseasonal, constant+dseasonal
     - model has 1 unknown parameter
     > 1+m1+m2
     constant dlmodel(state dim=13, dist dim=3, obs dim=1) 'constant+dseasonal+level'
     - model has 5 components: constant, dseasonal, constant+dseasonal, level, constant+dseasonal+level
     - model has 1 unknown parameter
     > dlmodeler.multiply(m2,2)
     constant dlmodel(state dim=1, dist dim=1, obs dim=1) 'level'
     - model has 1 component: level
     - model has 1 unknown parameter
     > m1*5
     constant dlmodel(state dim=11, dist dim=1, obs dim=1) 'dseasonal'
     - model has 1 component: dseasonal
     - model has 1 unknown parameter
     > dlmodeler.multiply(2,m2)
     constant dlmodel(state dim=1, dist dim=1, obs dim=1) 'level'
     - model has 1 component: level
     - model has 1 unknown parameter
     > 5*m1
     constant dlmodel(state dim=11, dist dim=1, obs dim=1) 'dseasonal'
     - model has 1 component: dseasonal
     - model has 1 unknown parameter
     > 2*m1+3*m2+4*m3+5*m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=1) 'dseasonal+level+dseasonal+constant'
     - model has 7 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal, constant, dseasonal+level+dseasonal+constant
     - model has 1 unknown parameter
     > (2*m1+3*m2+4*m3+5*m4)$Zt
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
     [1,] 2 0 0 0 0 0 0 0 0 0 0 3 4 0
     [,15] [,16] [,17]
     [1,] 0 0 5
     >
     >
     > #####################################
     > # univariate and time-varying tests #
     > #####################################
     > # this example is fairly complete, covers 'add', 'filter', 'smooth'
     > # 'extract', 'polynomial', 'dseasonal', 'tseasonal', 'regression'
     > # test it with various backends
     >
     > # generate some data
     > set.seed(19820605)
     > N <- 365*10
     > t <- c(1:N,rep(NA,365))
     > a <- rnorm(N+365,0,.5)
     > y <- pi + cos(2*pi*t/365.25) + .25*sin(2*pi*t/365.25*3) +
     + exp(1)*a + rnorm(N+365,0,.5)
     >
     > # build a model for this data
     > m1 <- dlmodeler.build.polynomial(0,sigmaH=.5,name='level')
     > m2 <- dlmodeler.build.dseasonal(7,sigmaH=0,name='week')
     > m3 <- dlmodeler.build.tseasonal(365.25,3,sigmaH=0,name='year')
     > m4 <- dlmodeler.build.regression(a,sigmaH=0,name='reg')
     > m <- m1+m2+m3+m4
     >
     > test.backend <- function(backend)
     + {
     + cat(backend,"\n")
     + test.ok <- TRUE
     +
     + system.time(f <- dlmodeler.filter(y, m, raw.result=TRUE, backend=backend))
     +
     + # extract all the components
     + m.state.mean <- dlmodeler.extract(f,m,type="state",value="mean")
     + m.state.cov <- dlmodeler.extract(f,m,type="state",value="covariance")
     + m.obs.mean <- dlmodeler.extract(f,m,type="observation",value="mean")
     + m.obs.cov <- dlmodeler.extract(f,m,type="observation",value="covariance")
     + m.obs.int <- dlmodeler.extract(f,m,type="observation",value="interval",prob=.01)
     +
     + par(mfrow=c(2,1))
     +
     + # show the one step ahead forecasts & 99\% prediction intervals
     + #plot(y,xlim=c(N-10,N+30))
     + #lines(m.obs.int$mymodel$upper[1,],col='light grey')
     + #lines(m.obs.int$mymodel$lower[1,],col='light grey')
     + #lines(m.obs.int$mymodel$mean[1,],col=2)
     +
     + # see to which values the filter has converged:
     + test.ok <- test.ok & abs(m.state.mean$level[,N]-pi)/pi < .05 # should be close to pi
     + test.ok <- test.ok & abs(mean(abs(m.state.mean$week[,N]))) < .05 # should be close to 0
     + test.ok <- test.ok & abs(m.state.mean$year[1,N]-1) < .05 # should be close to 1
     + test.ok <- test.ok & abs(m.state.mean$year[6,N]-.25) < .05 # should be close to .25
     + test.ok <- test.ok & abs(m.state.mean$reg[,N]-exp(1))/exp(1) < .05 # should be close to e
     +
     + # show the filtered level+year components
     + #plot(m.obs.mean$level[1,]+m.obs.mean$year[1,],
     + # type='l',ylim=c(pi-2,pi+2),col='light green',
     + # ylab="smoothed & filtered level+year")
     +
     + if(backend!='FKF') {
     + system.time(s <- dlmodeler.smooth(f))
     +
     + # show the smoothed level+year components
     + s.obs.mean <- dlmodeler.extract(s,m,type="observation",value="mean")
     + #lines(s.obs.mean$level[1,]+s.obs.mean$year[1,],type='l',
     + # ylim=c(pi-2,pi+2),col='dark green')
     + }
     +
     + return(test.ok)
     + }
     >
     > #if(require('KFAS')) {
     > # cat('Found package KFAS. Testing...\n')
     > if(!(test.backend('KFAS'))) stop("KFAS unit test failed")
     KFAS
     Warning message:
     In KFAS::KFS(filt$raw.result$model, smoothing = "state") :
     Possible error in smoothing: Negative variances in V, check the model or try changing the tolerance parameter tol or P1/P1inf of the model.
     > #}
     >
     > if(require('FKF')) {
     + cat('Found package FKF. Testing...\n')
     + if(!(test.backend('FKF'))) stop("FKF unit test failed")
     + }
     Loading required package: FKF
     Loading required package: RUnit
     Found package FKF. Testing...
     FKF
     >
     > if(require('dlm')) {
     + cat('Found package dlm. Testing...\n')
     + if(!(test.backend('dlm'))) stop("dlm unit test failed")
     + }
     Loading required package: dlm
     Found package dlm. Testing...
     dlm
     Error: 'dlmSmooth.dlmFiltered' is not an exported object from 'namespace:dlm'
     Timing stopped at: 0 0 0
     Execution halted
Flavor: r-devel-windows-ix86+x86_64

Version: 1.4-2
Check: tests
Result: ERROR
     Running ‘tests.R’ [20s/23s]
    Running the tests in ‘tests/tests.R’ failed.
    Complete output:
     >
     > require(dlmodeler)
     Loading required package: dlmodeler
     >
     > ####################
     > # tests for 'core' #
     > ####################
     >
     > # create a DLM by specifying its vectors and matrices
     > # check if the model is valid
     > mod <- dlmodeler.build(
     + a0 = c(0,0), # initial state: (level, trend)
     + P0 = diag(c(0,0)), # initial state variance set to...
     + P0inf = diag(2), # ...use exact diffuse initialization
     + matrix(c(1,0,1,1),2,2), # state transition matrix
     + diag(c(1,1)), # state disturbance selection matrix
     + diag(c(.5,.05)), # state disturbance variance matrix
     + matrix(c(1,0),1,2), # observation design matrix
     + matrix(1,1,1) # observation disturbance variance matrix
     + )
     >
     > print(mod)
     constant dlmodel(state dim=2, dist dim=2, obs dim=1) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==1)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # an empty DLM with 4 state variables (3 of which are stochastic)
     > # and bi-variate observations, check if the model is valid
     > mod <- dlmodeler.build(dimensions=c(4,3,2))
     >
     > print(mod)
     constant dlmodel(state dim=4, dist dim=3, obs dim=2) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==4)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==3)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # operations on matrices
     > v1 <- matrix(1:9,nrow=3,ncol=3)
     > v2 <- array(1:18,dim=c(3,3,2))
     >
     > m1 <- dlmodeler:::dlmodeler.timevar.fun(v1,v1,function(x,y) x+y)
     > if(!(sum(abs(m1-v1-v1))==0)) stop("unit test failed")
     > m21 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m21[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m21[,,2]-v1-v1-9))==0)) stop("unit test failed")
     > m22 <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,function(x,y) x+y)
     > if(!(sum(abs(m22[,,1]-m21[,,1]))==0)) stop("unit test failed")
     > if(!(sum(abs(m22[,,2]-m21[,,2]))==0)) stop("unit test failed")
     > m3 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m3[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m3[,,2]-v1-matrix(10:18,nrow=3,ncol=3)))==0)) stop("unit test failed")
     >
     > mt <- matrix(c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,9,9,0,0,0,9,9,9,0,0,0,9,9,9),nrow=6,ncol=6)
     > md <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,dlmodeler:::dlmodeler.bdiag)
     > if(!(sum(abs(md[,,2]-md[,,1]-mt))==0)) stop("unit test failed")
     >
     > # operators
     > m1 <- dlmodeler.build.dseasonal(12)
     > m2 <- dlmodeler.build.polynomial(0)
     > m3 <- dlmodeler.build.dseasonal(5)
     > m4 <- dlmodeler.build.constant(4)
     >
     > m1+m2+m3
     constant dlmodel(state dim=16, dist dim=3, obs dim=1) 'dseasonal+level+dseasonal'
     - model has 5 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal
     - model has 1 unknown parameter
     > m1+m2+m3+m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=1) 'dseasonal+level+dseasonal+constant'
     - model has 7 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal, constant, dseasonal+level+dseasonal+constant
     - model has 1 unknown parameter
     > m1%%m2%%m3
     constant dlmodel(state dim=16, dist dim=3, obs dim=3) 'dseasonal&level&dseasonal'
     - model has 5 components: dseasonal, level, dseasonal&level, dseasonal, dseasonal&level&dseasonal
     - model has 3 unknown parameters
     > m1%%m2%%m3%%m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=4) 'dseasonal&level&dseasonal&constant'
     - model has 7 components: dseasonal, level, dseasonal&level, dseasonal, dseasonal&level&dseasonal, constant, dseasonal&level&dseasonal&constant
     - model has 3 unknown parameters
     > dlmodeler.add(m1,1)
     constant dlmodel(state dim=12, dist dim=2, obs dim=1) 'dseasonal+constant'
     - model has 3 components: dseasonal, constant, dseasonal+constant
     - model has 1 unknown parameter
     > m1+1+m2
     constant dlmodel(state dim=13, dist dim=3, obs dim=1) 'dseasonal+constant+level'
     - model has 5 components: dseasonal, constant, dseasonal+constant, level, dseasonal+constant+level
     - model has 1 unknown parameter
     > dlmodeler.add(1,m1)
     constant dlmodel(state dim=12, dist dim=2, obs dim=1) 'constant+dseasonal'
     - model has 3 components: constant, dseasonal, constant+dseasonal
     - model has 1 unknown parameter
     > 1+m1+m2
     constant dlmodel(state dim=13, dist dim=3, obs dim=1) 'constant+dseasonal+level'
     - model has 5 components: constant, dseasonal, constant+dseasonal, level, constant+dseasonal+level
     - model has 1 unknown parameter
     > dlmodeler.multiply(m2,2)
     constant dlmodel(state dim=1, dist dim=1, obs dim=1) 'level'
     - model has 1 component: level
     - model has 1 unknown parameter
     > m1*5
     constant dlmodel(state dim=11, dist dim=1, obs dim=1) 'dseasonal'
     - model has 1 component: dseasonal
     - model has 1 unknown parameter
     > dlmodeler.multiply(2,m2)
     constant dlmodel(state dim=1, dist dim=1, obs dim=1) 'level'
     - model has 1 component: level
     - model has 1 unknown parameter
     > 5*m1
     constant dlmodel(state dim=11, dist dim=1, obs dim=1) 'dseasonal'
     - model has 1 component: dseasonal
     - model has 1 unknown parameter
     > 2*m1+3*m2+4*m3+5*m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=1) 'dseasonal+level+dseasonal+constant'
     - model has 7 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal, constant, dseasonal+level+dseasonal+constant
     - model has 1 unknown parameter
     > (2*m1+3*m2+4*m3+5*m4)$Zt
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
     [1,] 2 0 0 0 0 0 0 0 0 0 0 3 4 0
     [,15] [,16] [,17]
     [1,] 0 0 5
     >
     >
     > #####################################
     > # univariate and time-varying tests #
     > #####################################
     > # this example is fairly complete, covers 'add', 'filter', 'smooth'
     > # 'extract', 'polynomial', 'dseasonal', 'tseasonal', 'regression'
     > # test it with various backends
     >
     > # generate some data
     > set.seed(19820605)
     > N <- 365*10
     > t <- c(1:N,rep(NA,365))
     > a <- rnorm(N+365,0,.5)
     > y <- pi + cos(2*pi*t/365.25) + .25*sin(2*pi*t/365.25*3) +
     + exp(1)*a + rnorm(N+365,0,.5)
     >
     > # build a model for this data
     > m1 <- dlmodeler.build.polynomial(0,sigmaH=.5,name='level')
     > m2 <- dlmodeler.build.dseasonal(7,sigmaH=0,name='week')
     > m3 <- dlmodeler.build.tseasonal(365.25,3,sigmaH=0,name='year')
     > m4 <- dlmodeler.build.regression(a,sigmaH=0,name='reg')
     > m <- m1+m2+m3+m4
     >
     > test.backend <- function(backend)
     + {
     + cat(backend,"\n")
     + test.ok <- TRUE
     +
     + system.time(f <- dlmodeler.filter(y, m, raw.result=TRUE, backend=backend))
     +
     + # extract all the components
     + m.state.mean <- dlmodeler.extract(f,m,type="state",value="mean")
     + m.state.cov <- dlmodeler.extract(f,m,type="state",value="covariance")
     + m.obs.mean <- dlmodeler.extract(f,m,type="observation",value="mean")
     + m.obs.cov <- dlmodeler.extract(f,m,type="observation",value="covariance")
     + m.obs.int <- dlmodeler.extract(f,m,type="observation",value="interval",prob=.01)
     +
     + par(mfrow=c(2,1))
     +
     + # show the one step ahead forecasts & 99\% prediction intervals
     + #plot(y,xlim=c(N-10,N+30))
     + #lines(m.obs.int$mymodel$upper[1,],col='light grey')
     + #lines(m.obs.int$mymodel$lower[1,],col='light grey')
     + #lines(m.obs.int$mymodel$mean[1,],col=2)
     +
     + # see to which values the filter has converged:
     + test.ok <- test.ok & abs(m.state.mean$level[,N]-pi)/pi < .05 # should be close to pi
     + test.ok <- test.ok & abs(mean(abs(m.state.mean$week[,N]))) < .05 # should be close to 0
     + test.ok <- test.ok & abs(m.state.mean$year[1,N]-1) < .05 # should be close to 1
     + test.ok <- test.ok & abs(m.state.mean$year[6,N]-.25) < .05 # should be close to .25
     + test.ok <- test.ok & abs(m.state.mean$reg[,N]-exp(1))/exp(1) < .05 # should be close to e
     +
     + # show the filtered level+year components
     + #plot(m.obs.mean$level[1,]+m.obs.mean$year[1,],
     + # type='l',ylim=c(pi-2,pi+2),col='light green',
     + # ylab="smoothed & filtered level+year")
     +
     + if(backend!='FKF') {
     + system.time(s <- dlmodeler.smooth(f))
     +
     + # show the smoothed level+year components
     + s.obs.mean <- dlmodeler.extract(s,m,type="observation",value="mean")
     + #lines(s.obs.mean$level[1,]+s.obs.mean$year[1,],type='l',
     + # ylim=c(pi-2,pi+2),col='dark green')
     + }
     +
     + return(test.ok)
     + }
     >
     > #if(require('KFAS')) {
     > # cat('Found package KFAS. Testing...\n')
     > if(!(test.backend('KFAS'))) stop("KFAS unit test failed")
     KFAS
     Warning message:
     In KFAS::KFS(filt$raw.result$model, smoothing = "state") :
     Possible error in smoothing: Negative variances in V, check the model or try changing the tolerance parameter tol or P1/P1inf of the model.
     > #}
     >
     > if(require('FKF')) {
     + cat('Found package FKF. Testing...\n')
     + if(!(test.backend('FKF'))) stop("FKF unit test failed")
     + }
     Loading required package: FKF
     Warning message:
     In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
     there is no package called 'FKF'
     >
     > if(require('dlm')) {
     + cat('Found package dlm. Testing...\n')
     + if(!(test.backend('dlm'))) stop("dlm unit test failed")
     + }
     Loading required package: dlm
     Found package dlm. Testing...
     dlm
     Error: 'dlmSmooth.dlmFiltered' is not an exported object from 'namespace:dlm'
     Timing stopped at: 0.001 0.001 0.002
     Execution halted
Flavor: r-patched-solaris-x86

Version: 1.4-2
Check: tests
Result: ERROR
     Running 'tests.R' [24s]
    Running the tests in 'tests/tests.R' failed.
    Complete output:
     >
     > require(dlmodeler)
     Loading required package: dlmodeler
     >
     > ####################
     > # tests for 'core' #
     > ####################
     >
     > # create a DLM by specifying its vectors and matrices
     > # check if the model is valid
     > mod <- dlmodeler.build(
     + a0 = c(0,0), # initial state: (level, trend)
     + P0 = diag(c(0,0)), # initial state variance set to...
     + P0inf = diag(2), # ...use exact diffuse initialization
     + matrix(c(1,0,1,1),2,2), # state transition matrix
     + diag(c(1,1)), # state disturbance selection matrix
     + diag(c(.5,.05)), # state disturbance variance matrix
     + matrix(c(1,0),1,2), # observation design matrix
     + matrix(1,1,1) # observation disturbance variance matrix
     + )
     >
     > print(mod)
     constant dlmodel(state dim=2, dist dim=2, obs dim=1) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==1)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # an empty DLM with 4 state variables (3 of which are stochastic)
     > # and bi-variate observations, check if the model is valid
     > mod <- dlmodeler.build(dimensions=c(4,3,2))
     >
     > print(mod)
     constant dlmodel(state dim=4, dist dim=3, obs dim=2) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==4)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==3)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # operations on matrices
     > v1 <- matrix(1:9,nrow=3,ncol=3)
     > v2 <- array(1:18,dim=c(3,3,2))
     >
     > m1 <- dlmodeler:::dlmodeler.timevar.fun(v1,v1,function(x,y) x+y)
     > if(!(sum(abs(m1-v1-v1))==0)) stop("unit test failed")
     > m21 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m21[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m21[,,2]-v1-v1-9))==0)) stop("unit test failed")
     > m22 <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,function(x,y) x+y)
     > if(!(sum(abs(m22[,,1]-m21[,,1]))==0)) stop("unit test failed")
     > if(!(sum(abs(m22[,,2]-m21[,,2]))==0)) stop("unit test failed")
     > m3 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m3[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m3[,,2]-v1-matrix(10:18,nrow=3,ncol=3)))==0)) stop("unit test failed")
     >
     > mt <- matrix(c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,9,9,0,0,0,9,9,9,0,0,0,9,9,9),nrow=6,ncol=6)
     > md <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,dlmodeler:::dlmodeler.bdiag)
     > if(!(sum(abs(md[,,2]-md[,,1]-mt))==0)) stop("unit test failed")
     >
     > # operators
     > m1 <- dlmodeler.build.dseasonal(12)
     > m2 <- dlmodeler.build.polynomial(0)
     > m3 <- dlmodeler.build.dseasonal(5)
     > m4 <- dlmodeler.build.constant(4)
     >
     > m1+m2+m3
     constant dlmodel(state dim=16, dist dim=3, obs dim=1) 'dseasonal+level+dseasonal'
     - model has 5 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal
     - model has 1 unknown parameter
     > m1+m2+m3+m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=1) 'dseasonal+level+dseasonal+constant'
     - model has 7 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal, constant, dseasonal+level+dseasonal+constant
     - model has 1 unknown parameter
     > m1%%m2%%m3
     constant dlmodel(state dim=16, dist dim=3, obs dim=3) 'dseasonal&level&dseasonal'
     - model has 5 components: dseasonal, level, dseasonal&level, dseasonal, dseasonal&level&dseasonal
     - model has 3 unknown parameters
     > m1%%m2%%m3%%m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=4) 'dseasonal&level&dseasonal&constant'
     - model has 7 components: dseasonal, level, dseasonal&level, dseasonal, dseasonal&level&dseasonal, constant, dseasonal&level&dseasonal&constant
     - model has 3 unknown parameters
     > dlmodeler.add(m1,1)
     constant dlmodel(state dim=12, dist dim=2, obs dim=1) 'dseasonal+constant'
     - model has 3 components: dseasonal, constant, dseasonal+constant
     - model has 1 unknown parameter
     > m1+1+m2
     constant dlmodel(state dim=13, dist dim=3, obs dim=1) 'dseasonal+constant+level'
     - model has 5 components: dseasonal, constant, dseasonal+constant, level, dseasonal+constant+level
     - model has 1 unknown parameter
     > dlmodeler.add(1,m1)
     constant dlmodel(state dim=12, dist dim=2, obs dim=1) 'constant+dseasonal'
     - model has 3 components: constant, dseasonal, constant+dseasonal
     - model has 1 unknown parameter
     > 1+m1+m2
     constant dlmodel(state dim=13, dist dim=3, obs dim=1) 'constant+dseasonal+level'
     - model has 5 components: constant, dseasonal, constant+dseasonal, level, constant+dseasonal+level
     - model has 1 unknown parameter
     > dlmodeler.multiply(m2,2)
     constant dlmodel(state dim=1, dist dim=1, obs dim=1) 'level'
     - model has 1 component: level
     - model has 1 unknown parameter
     > m1*5
     constant dlmodel(state dim=11, dist dim=1, obs dim=1) 'dseasonal'
     - model has 1 component: dseasonal
     - model has 1 unknown parameter
     > dlmodeler.multiply(2,m2)
     constant dlmodel(state dim=1, dist dim=1, obs dim=1) 'level'
     - model has 1 component: level
     - model has 1 unknown parameter
     > 5*m1
     constant dlmodel(state dim=11, dist dim=1, obs dim=1) 'dseasonal'
     - model has 1 component: dseasonal
     - model has 1 unknown parameter
     > 2*m1+3*m2+4*m3+5*m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=1) 'dseasonal+level+dseasonal+constant'
     - model has 7 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal, constant, dseasonal+level+dseasonal+constant
     - model has 1 unknown parameter
     > (2*m1+3*m2+4*m3+5*m4)$Zt
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
     [1,] 2 0 0 0 0 0 0 0 0 0 0 3 4 0
     [,15] [,16] [,17]
     [1,] 0 0 5
     >
     >
     > #####################################
     > # univariate and time-varying tests #
     > #####################################
     > # this example is fairly complete, covers 'add', 'filter', 'smooth'
     > # 'extract', 'polynomial', 'dseasonal', 'tseasonal', 'regression'
     > # test it with various backends
     >
     > # generate some data
     > set.seed(19820605)
     > N <- 365*10
     > t <- c(1:N,rep(NA,365))
     > a <- rnorm(N+365,0,.5)
     > y <- pi + cos(2*pi*t/365.25) + .25*sin(2*pi*t/365.25*3) +
     + exp(1)*a + rnorm(N+365,0,.5)
     >
     > # build a model for this data
     > m1 <- dlmodeler.build.polynomial(0,sigmaH=.5,name='level')
     > m2 <- dlmodeler.build.dseasonal(7,sigmaH=0,name='week')
     > m3 <- dlmodeler.build.tseasonal(365.25,3,sigmaH=0,name='year')
     > m4 <- dlmodeler.build.regression(a,sigmaH=0,name='reg')
     > m <- m1+m2+m3+m4
     >
     > test.backend <- function(backend)
     + {
     + cat(backend,"\n")
     + test.ok <- TRUE
     +
     + system.time(f <- dlmodeler.filter(y, m, raw.result=TRUE, backend=backend))
     +
     + # extract all the components
     + m.state.mean <- dlmodeler.extract(f,m,type="state",value="mean")
     + m.state.cov <- dlmodeler.extract(f,m,type="state",value="covariance")
     + m.obs.mean <- dlmodeler.extract(f,m,type="observation",value="mean")
     + m.obs.cov <- dlmodeler.extract(f,m,type="observation",value="covariance")
     + m.obs.int <- dlmodeler.extract(f,m,type="observation",value="interval",prob=.01)
     +
     + par(mfrow=c(2,1))
     +
     + # show the one step ahead forecasts & 99\% prediction intervals
     + #plot(y,xlim=c(N-10,N+30))
     + #lines(m.obs.int$mymodel$upper[1,],col='light grey')
     + #lines(m.obs.int$mymodel$lower[1,],col='light grey')
     + #lines(m.obs.int$mymodel$mean[1,],col=2)
     +
     + # see to which values the filter has converged:
     + test.ok <- test.ok & abs(m.state.mean$level[,N]-pi)/pi < .05 # should be close to pi
     + test.ok <- test.ok & abs(mean(abs(m.state.mean$week[,N]))) < .05 # should be close to 0
     + test.ok <- test.ok & abs(m.state.mean$year[1,N]-1) < .05 # should be close to 1
     + test.ok <- test.ok & abs(m.state.mean$year[6,N]-.25) < .05 # should be close to .25
     + test.ok <- test.ok & abs(m.state.mean$reg[,N]-exp(1))/exp(1) < .05 # should be close to e
     +
     + # show the filtered level+year components
     + #plot(m.obs.mean$level[1,]+m.obs.mean$year[1,],
     + # type='l',ylim=c(pi-2,pi+2),col='light green',
     + # ylab="smoothed & filtered level+year")
     +
     + if(backend!='FKF') {
     + system.time(s <- dlmodeler.smooth(f))
     +
     + # show the smoothed level+year components
     + s.obs.mean <- dlmodeler.extract(s,m,type="observation",value="mean")
     + #lines(s.obs.mean$level[1,]+s.obs.mean$year[1,],type='l',
     + # ylim=c(pi-2,pi+2),col='dark green')
     + }
     +
     + return(test.ok)
     + }
     >
     > #if(require('KFAS')) {
     > # cat('Found package KFAS. Testing...\n')
     > if(!(test.backend('KFAS'))) stop("KFAS unit test failed")
     KFAS
     Warning message:
     In KFAS::KFS(filt$raw.result$model, smoothing = "state") :
     Possible error in smoothing: Negative variances in V, check the model or try changing the tolerance parameter tol or P1/P1inf of the model.
     > #}
     >
     > if(require('FKF')) {
     + cat('Found package FKF. Testing...\n')
     + if(!(test.backend('FKF'))) stop("FKF unit test failed")
     + }
     Loading required package: FKF
     Loading required package: RUnit
     Found package FKF. Testing...
     FKF
     >
     > if(require('dlm')) {
     + cat('Found package dlm. Testing...\n')
     + if(!(test.backend('dlm'))) stop("dlm unit test failed")
     + }
     Loading required package: dlm
     Found package dlm. Testing...
     dlm
     Error: 'dlmSmooth.dlmFiltered' is not an exported object from 'namespace:dlm'
     Timing stopped at: 0 0 0
     Execution halted
Flavor: r-release-windows-ix86+x86_64

Version: 1.4-2
Check: dependencies in R code
Result: NOTE
    'library' or 'require' calls in package code:
     ‘FKF’ ‘dlm’
     Please use :: or requireNamespace() instead.
     See section 'Suggested packages' in the 'Writing R Extensions' manual.
Flavors: r-release-osx-x86_64, r-oldrel-osx-x86_64

Version: 1.4-2
Check: tests
Result: ERROR
     Running 'tests.R' [20s]
    Running the tests in 'tests/tests.R' failed.
    Complete output:
     >
     > require(dlmodeler)
     Loading required package: dlmodeler
     >
     > ####################
     > # tests for 'core' #
     > ####################
     >
     > # create a DLM by specifying its vectors and matrices
     > # check if the model is valid
     > mod <- dlmodeler.build(
     + a0 = c(0,0), # initial state: (level, trend)
     + P0 = diag(c(0,0)), # initial state variance set to...
     + P0inf = diag(2), # ...use exact diffuse initialization
     + matrix(c(1,0,1,1),2,2), # state transition matrix
     + diag(c(1,1)), # state disturbance selection matrix
     + diag(c(.5,.05)), # state disturbance variance matrix
     + matrix(c(1,0),1,2), # observation design matrix
     + matrix(1,1,1) # observation disturbance variance matrix
     + )
     >
     > print(mod)
     constant dlmodel(state dim=2, dist dim=2, obs dim=1) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==1)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # an empty DLM with 4 state variables (3 of which are stochastic)
     > # and bi-variate observations, check if the model is valid
     > mod <- dlmodeler.build(dimensions=c(4,3,2))
     >
     > print(mod)
     constant dlmodel(state dim=4, dist dim=3, obs dim=2) 'noname'
     - model has 1 component: noname
     > if(!(dlmodeler.check(mod)$status==TRUE)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$m==4)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$r==3)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$d==2)) stop("unit test failed")
     > if(!(dlmodeler.check(mod)$timevar==FALSE)) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Tt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Rt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Qt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Zt))) stop("unit test failed")
     > if(!(is.na(dlmodeler.check(mod)$timevar.Ht))) stop("unit test failed")
     >
     >
     > # operations on matrices
     > v1 <- matrix(1:9,nrow=3,ncol=3)
     > v2 <- array(1:18,dim=c(3,3,2))
     >
     > m1 <- dlmodeler:::dlmodeler.timevar.fun(v1,v1,function(x,y) x+y)
     > if(!(sum(abs(m1-v1-v1))==0)) stop("unit test failed")
     > m21 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m21[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m21[,,2]-v1-v1-9))==0)) stop("unit test failed")
     > m22 <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,function(x,y) x+y)
     > if(!(sum(abs(m22[,,1]-m21[,,1]))==0)) stop("unit test failed")
     > if(!(sum(abs(m22[,,2]-m21[,,2]))==0)) stop("unit test failed")
     > m3 <- dlmodeler:::dlmodeler.timevar.fun(v2,v1,function(x,y) x+y)
     > if(!(sum(abs(m3[,,1]-v1-v1))==0)) stop("unit test failed")
     > if(!(sum(abs(m3[,,2]-v1-matrix(10:18,nrow=3,ncol=3)))==0)) stop("unit test failed")
     >
     > mt <- matrix(c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,9,9,0,0,0,9,9,9,0,0,0,9,9,9),nrow=6,ncol=6)
     > md <- dlmodeler:::dlmodeler.timevar.fun(v1,v2,dlmodeler:::dlmodeler.bdiag)
     > if(!(sum(abs(md[,,2]-md[,,1]-mt))==0)) stop("unit test failed")
     >
     > # operators
     > m1 <- dlmodeler.build.dseasonal(12)
     > m2 <- dlmodeler.build.polynomial(0)
     > m3 <- dlmodeler.build.dseasonal(5)
     > m4 <- dlmodeler.build.constant(4)
     >
     > m1+m2+m3
     constant dlmodel(state dim=16, dist dim=3, obs dim=1) 'dseasonal+level+dseasonal'
     - model has 5 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal
     - model has 1 unknown parameter
     > m1+m2+m3+m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=1) 'dseasonal+level+dseasonal+constant'
     - model has 7 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal, constant, dseasonal+level+dseasonal+constant
     - model has 1 unknown parameter
     > m1%%m2%%m3
     constant dlmodel(state dim=16, dist dim=3, obs dim=3) 'dseasonal&level&dseasonal'
     - model has 5 components: dseasonal, level, dseasonal&level, dseasonal, dseasonal&level&dseasonal
     - model has 3 unknown parameters
     > m1%%m2%%m3%%m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=4) 'dseasonal&level&dseasonal&constant'
     - model has 7 components: dseasonal, level, dseasonal&level, dseasonal, dseasonal&level&dseasonal, constant, dseasonal&level&dseasonal&constant
     - model has 3 unknown parameters
     > dlmodeler.add(m1,1)
     constant dlmodel(state dim=12, dist dim=2, obs dim=1) 'dseasonal+constant'
     - model has 3 components: dseasonal, constant, dseasonal+constant
     - model has 1 unknown parameter
     > m1+1+m2
     constant dlmodel(state dim=13, dist dim=3, obs dim=1) 'dseasonal+constant+level'
     - model has 5 components: dseasonal, constant, dseasonal+constant, level, dseasonal+constant+level
     - model has 1 unknown parameter
     > dlmodeler.add(1,m1)
     constant dlmodel(state dim=12, dist dim=2, obs dim=1) 'constant+dseasonal'
     - model has 3 components: constant, dseasonal, constant+dseasonal
     - model has 1 unknown parameter
     > 1+m1+m2
     constant dlmodel(state dim=13, dist dim=3, obs dim=1) 'constant+dseasonal+level'
     - model has 5 components: constant, dseasonal, constant+dseasonal, level, constant+dseasonal+level
     - model has 1 unknown parameter
     > dlmodeler.multiply(m2,2)
     constant dlmodel(state dim=1, dist dim=1, obs dim=1) 'level'
     - model has 1 component: level
     - model has 1 unknown parameter
     > m1*5
     constant dlmodel(state dim=11, dist dim=1, obs dim=1) 'dseasonal'
     - model has 1 component: dseasonal
     - model has 1 unknown parameter
     > dlmodeler.multiply(2,m2)
     constant dlmodel(state dim=1, dist dim=1, obs dim=1) 'level'
     - model has 1 component: level
     - model has 1 unknown parameter
     > 5*m1
     constant dlmodel(state dim=11, dist dim=1, obs dim=1) 'dseasonal'
     - model has 1 component: dseasonal
     - model has 1 unknown parameter
     > 2*m1+3*m2+4*m3+5*m4
     constant dlmodel(state dim=17, dist dim=4, obs dim=1) 'dseasonal+level+dseasonal+constant'
     - model has 7 components: dseasonal, level, dseasonal+level, dseasonal, dseasonal+level+dseasonal, constant, dseasonal+level+dseasonal+constant
     - model has 1 unknown parameter
     > (2*m1+3*m2+4*m3+5*m4)$Zt
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
     [1,] 2 0 0 0 0 0 0 0 0 0 0 3 4 0
     [,15] [,16] [,17]
     [1,] 0 0 5
     >
     >
     > #####################################
     > # univariate and time-varying tests #
     > #####################################
     > # this example is fairly complete, covers 'add', 'filter', 'smooth'
     > # 'extract', 'polynomial', 'dseasonal', 'tseasonal', 'regression'
     > # test it with various backends
     >
     > # generate some data
     > set.seed(19820605)
     > N <- 365*10
     > t <- c(1:N,rep(NA,365))
     > a <- rnorm(N+365,0,.5)
     > y <- pi + cos(2*pi*t/365.25) + .25*sin(2*pi*t/365.25*3) +
     + exp(1)*a + rnorm(N+365,0,.5)
     >
     > # build a model for this data
     > m1 <- dlmodeler.build.polynomial(0,sigmaH=.5,name='level')
     > m2 <- dlmodeler.build.dseasonal(7,sigmaH=0,name='week')
     > m3 <- dlmodeler.build.tseasonal(365.25,3,sigmaH=0,name='year')
     > m4 <- dlmodeler.build.regression(a,sigmaH=0,name='reg')
     > m <- m1+m2+m3+m4
     >
     > test.backend <- function(backend)
     + {
     + cat(backend,"\n")
     + test.ok <- TRUE
     +
     + system.time(f <- dlmodeler.filter(y, m, raw.result=TRUE, backend=backend))
     +
     + # extract all the components
     + m.state.mean <- dlmodeler.extract(f,m,type="state",value="mean")
     + m.state.cov <- dlmodeler.extract(f,m,type="state",value="covariance")
     + m.obs.mean <- dlmodeler.extract(f,m,type="observation",value="mean")
     + m.obs.cov <- dlmodeler.extract(f,m,type="observation",value="covariance")
     + m.obs.int <- dlmodeler.extract(f,m,type="observation",value="interval",prob=.01)
     +
     + par(mfrow=c(2,1))
     +
     + # show the one step ahead forecasts & 99\% prediction intervals
     + #plot(y,xlim=c(N-10,N+30))
     + #lines(m.obs.int$mymodel$upper[1,],col='light grey')
     + #lines(m.obs.int$mymodel$lower[1,],col='light grey')
     + #lines(m.obs.int$mymodel$mean[1,],col=2)
     +
     + # see to which values the filter has converged:
     + test.ok <- test.ok & abs(m.state.mean$level[,N]-pi)/pi < .05 # should be close to pi
     + test.ok <- test.ok & abs(mean(abs(m.state.mean$week[,N]))) < .05 # should be close to 0
     + test.ok <- test.ok & abs(m.state.mean$year[1,N]-1) < .05 # should be close to 1
     + test.ok <- test.ok & abs(m.state.mean$year[6,N]-.25) < .05 # should be close to .25
     + test.ok <- test.ok & abs(m.state.mean$reg[,N]-exp(1))/exp(1) < .05 # should be close to e
     +
     + # show the filtered level+year components
     + #plot(m.obs.mean$level[1,]+m.obs.mean$year[1,],
     + # type='l',ylim=c(pi-2,pi+2),col='light green',
     + # ylab="smoothed & filtered level+year")
     +
     + if(backend!='FKF') {
     + system.time(s <- dlmodeler.smooth(f))
     +
     + # show the smoothed level+year components
     + s.obs.mean <- dlmodeler.extract(s,m,type="observation",value="mean")
     + #lines(s.obs.mean$level[1,]+s.obs.mean$year[1,],type='l',
     + # ylim=c(pi-2,pi+2),col='dark green')
     + }
     +
     + return(test.ok)
     + }
     >
     > #if(require('KFAS')) {
     > # cat('Found package KFAS. Testing...\n')
     > if(!(test.backend('KFAS'))) stop("KFAS unit test failed")
     KFAS
     Warning message:
     In KFAS::KFS(filt$raw.result$model, smoothing = "state") :
     Possible error in smoothing: Negative variances in V, check the model or try changing the tolerance parameter tol or P1/P1inf of the model.
     > #}
     >
     > if(require('FKF')) {
     + cat('Found package FKF. Testing...\n')
     + if(!(test.backend('FKF'))) stop("FKF unit test failed")
     + }
     Loading required package: FKF
     Loading required package: RUnit
     Found package FKF. Testing...
     FKF
     >
     > if(require('dlm')) {
     + cat('Found package dlm. Testing...\n')
     + if(!(test.backend('dlm'))) stop("dlm unit test failed")
     + }
     Loading required package: dlm
     Found package dlm. Testing...
     dlm
     Error: 'dlmSmooth.dlmFiltered' is not an exported object from 'namespace:dlm'
     Timing stopped at: 0 0 0
     Execution halted
Flavor: r-oldrel-windows-ix86+x86_64