==585586== Memcheck, a memory error detector
==585586== Copyright (C) 2002-2026, and GNU GPL'd, by Julian Seward et al.
==585586== Using Valgrind-3.27.1 and LibVEX; rerun with -h for copyright info
==585586== Command: /data/localhost/ripley/R/R-devel-vg/bin/exec/R --vanilla
==585586== 

R Under development (unstable) (2026-08-27 r90452) -- "Unsuffered Consequences"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.

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> pkgname <- "layeranalyzer"
> source(file.path(R.home("share"), "R", "examples-header.R"))
> options(warn = 1)
> library('layeranalyzer')
> 
> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("anovalayered")
> ### * anovalayered
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: anova.layered
> ### Title: Analysis of Deviance for return object from 'layer.analyzer'.
> ### Aliases: anova.layered
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Read a simulated dataset with 2 layers:
> test.ex<-read.table("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt")
> names(test.ex)<-c("time","value")
> ts<-layer.data.series(time=test.ex$time, value=test.ex$value,name="X")
> 
> # Calculate classic maximum likelihoods for an OU model and a 2 layered model.
> # Test the number of layers. The dataset itself is simulated using 2 layers:
> 
> 
> 
> cleanEx()
> nameEx("comparelayered")
> ### * comparelayered
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: compare.layered
> ### Title: Perform comparison of several models for the same dataset(s).
> ### Aliases: compare.layered
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Read a simulated dataset with 2 layers:
> test.ex<-read.table("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt")
> names(test.ex)<-c("time","value")
> ts<-layer.data.series(time=test.ex$time, value=test.ex$value,name="X")
> 
> # ML estimation for an OU model, low computer resources (including low pre-optimization MCMC)
> # to speed things up
> 
> 
> 
> 
> cleanEx()
> nameEx("layeranalyzer-package")
> ### * layeranalyzer-package
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layeranalyzer
> ### Title: Time Series Analysis Tool using Linear Layered SDEs
> ### Aliases: layeranalyzer
> ### Keywords: package
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Create a sample from the Ornstein-Uhlenbeck (OU) process with mu=0, 
> # characteristic time=50 and sigma=0.2:
> n=1000
> x=rep(rnorm(1),n)
> for(i in 2:n)
+  x[i]=exp(-1/50)*x[i-1]+0.2*rnorm(1)
> 
> # Create measurement set as a subsample of the process, plus 
> # noise with standard deviation 0.1
> t=sort(sample(1:n,300))
> y=(x+rnorm(length(x),0,0.1))[t]
> 
> # Create the data series object from the time points and "measurements":
> X=layer.data.series(time.points=t, value.points=y,name="X")
> 
> # Specify layered structure. (In this case default structure, one layered=OU):
> X.struct=layer.series.structure(X)
> 
> # Perform Bayesian analysis (low number of MCMCs):
> res<-layer.analyzer(X.struct, num.MCMC=40, burnin=400)
> 
> # Show the results:
> summary(res)
Description:
OU

Coefficients:
                   Mean    Median Lower 95% Upper 95%
mu_X          -0.179781 -0.197132 -0.612322  0.307791
dt_X_1        35.690709 35.186388 21.332573 54.399653
stat_sdev_X_1  0.878059  0.863245  0.745646  1.079676
obs_sd_X       0.074014  0.076355  0.016495  0.119791

Model log-likelihood:  -112.063
> 
> 
> 
> 
> cleanEx()
> nameEx("layeranalyzer")
> ### * layeranalyzer
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.analyzer
> ### Title: Time series analysis tool using linear layered SDEs.
> ### Aliases: layer.analyzer
> 
> ### ** Examples
> 
> library(layeranalyzer)
> # Simulated a sample from the Ornstein-Uhlenbeck (OU) process with mu=0, 
> # characteristic time=50 and sigma=0.2:
> n=1000
> x=rep(rnorm(1),n)
> for(i in 2:n)
+  x[i]=exp(-1/50)*x[i-1]+0.2*rnorm(1)
> 
> # Create measurement set as a subsample of the process, plus 
> # noise with standard deviation 0.1
> t=sort(sample(1:n,300))
> y=(x+rnorm(length(x),0,0.1))[t]
> 
> # Create the data series object from the time points and "measurements":
> X=layer.data.series(time.points=t, value.points=y,name="X")
> 
> # Run with few resources (since this is a test example).
> # PS: ML-based estimates (default is Bayesian)
> res=layer.analyzer(layer.series.structure(X,numlayers=1),
+   num.MCMC=40,burnin=400,
+   do.maximum.likelihood=TRUE,maximum.likelihood.numstart=20)
> 
> # Look at the results:
> summary(res)
Description:
OU

Coefficients:
              ML estimate Bayesian Lower 95% Bayesian Upper 95%
mu_X            -0.150304          -0.612322           0.307791
dt_X_1          31.981498          21.332573          54.399653
stat_sdev_X_1    0.806772           0.745646           1.079676
obs_sd_X         0.102706           0.016495           0.119791

Log-likelihood:   -99.845
AIC :   207.691
AICc:   207.759
BIC :   222.506
> # PS: Uncertainty estimates comes from Bayesian analysis and will
> # in this case not be very stable (since few resources).
> 
> 
> 
> 
> cleanEx()
> nameEx("layeranalyzertimeserieslist")
> ### * layeranalyzertimeserieslist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.analyzer.timeseries.list
> ### Title: Time series analysis tool using linear layered SDEs.
> ### Aliases: layer.analyzer.timeseries.list
> 
> ### ** Examples
> 
> library(layeranalyzer)
> # Simulated a sample from the Ornstein-Uhlenbeck (OU) process with mu=0, 
> # characteristic time=50 and sigma=0.2:
> n=1000
> x=rep(rnorm(1),n)
> for(i in 2:n)
+  x[i]=exp(-1/50)*x[i-1]+0.2*rnorm(1)
> 
> # Create measurement set as a subsample of the process, plus 
> # noise with standard deviation 0.1
> t=sort(sample(1:n,300))
> y=(x+rnorm(length(x),0,0.1))[t]
> 
> # Create the data series object from the time points and "measurements":
> X=layer.data.series(time.points=t, value.points=y,name="X")
> 
> # Run with few resources (since this is a test example).
> # PS: ML-based estimates (default is Bayesian)
> res=layer.analyzer.timeseries.list(list(x1=layer.series.structure(X,numlayers=1)),
+   num.MCMC=40,burnin=400,layer.analyzer.mode="ML-from-MCMC",
+   do.maximum.likelihood=TRUE,maximum.likelihood.numstart=20)
>   
> # Look at the results:
> summary(res)
Description:
OU

Coefficients:
              ML estimate Bayesian Lower 95% Bayesian Upper 95%
mu_X            -0.150304          -0.612322           0.307791
dt_X_1          31.981498          21.332573          54.399653
stat_sdev_X_1    0.806772           0.745646           1.079676
obs_sd_X         0.102706           0.016495           0.119791

Log-likelihood:   -99.845
AIC :   207.691
AICc:   207.759
BIC :   222.506
> # PS: Uncertainty estimates comes from Bayesian analysis and will
> # in this case not be very stable (since few resources).
> 
> 
> 
> 
> cleanEx()
> nameEx("layerconnections")
> ### * layerconnections
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.connections
> ### Title: Alternative connection specification, taking a structure list.
> ### Aliases: layer.connections
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Creates two 2-layered structures based on the two hare/lynx datasets.
> # outputs the connection structure specified by the
> # alternative conenction specification array c(0,1,2,4)
> 
> hare.st=layer.series.structure(hare.norm,numlayers=2)
> lynx.st=layer.series.structure(lynx.norm,numlayers=2)
> 
> layer.connections(hare.st, lynx.st, conn=c(0,1,2,4))
$causal
     [,1] [,2] [,3]
[1,]    1    1    2
[2,]    2    2    2
[3,]    2    2    1
[4,]    1    2    2

$corr
     [,1]
[1,]    1
[2,]    1
[3,]    2
[4,]    2

attr(,"class")
[1] "layer.connections"
> 
> 
> 
> cleanEx()
> nameEx("layerconnectionslist")
> ### * layerconnectionslist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.connections.list
> ### Title: Alternative connection specification, taking a structure list.
> ### Aliases: layer.connections.list
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Creates two 2-layered structures based on the two hare/lynx datasets.
> # outputs the connection structure specified by the
> # alternative conenction specification array c(0,1,2,4)
> 
> hare.st=layer.series.structure(hare.norm,numlayers=2)
> lynx.st=layer.series.structure(lynx.norm,numlayers=2)
> 
> layer.connections.list(list(hare.st, lynx.st), conn=c(0,1,2,4))
$causal
     [,1] [,2] [,3]
[1,]    1    1    2
[2,]    2    2    2
[3,]    2    2    1
[4,]    1    2    2

$corr
     [,1]
[1,]    1
[2,]    1
[3,]    2
[4,]    2

attr(,"class")
[1] "layer.connections"
> 
> 
> 
> cleanEx()
> nameEx("layerdataseries")
> ### * layerdataseries
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.data.series
> ### Title: Represents a measurement time series.
> ### Aliases: layer.data.series
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Create a sample from the Ornstein-Uhlenbeck (OU) process with mu=0, 
> # characteristic time=50 and sigma=0.2:
> n=1000
> x=rep(rnorm(1),n)
> for(i in 2:n)
+  x[i]=exp(-1/50)*x[i-1]+0.2*rnorm(1)
> 
> # Create measurement set as a subsample of the process, plus 
> # noise with standard deviation 0.1
> t=sort(sample(1:n,300))
> y=(x+rnorm(length(x),0,0.1))[t]
> 
> # Create the data series object from the time points and "measurements":
> X=layer.data.series(time.points=t, value.points=y, name="X")
> 
> 
> 
> 
> cleanEx()
> nameEx("layerloadprior")
> ### * layerloadprior
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.load.prior
> ### Title: Reading prior specification for the layer.analyzer from a file.
> ### Aliases: layer.load.prior
> 
> ### ** Examples
> 
> library(layeranalyzer)
> pr=layer.load.prior("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_prior.txt")
> 
> 
> 
> cleanEx()
> nameEx("layerpairs")
> ### * layerpairs
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.pairs
> ### Title: Alternative connection specification, taking a structure list.
> ### Aliases: layer.pairs
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Creates two 2-layered structures based on the two hare/lynx datasets.
> # outputs the pairs and a description
> 
> hare.st=layer.series.structure(hare.norm,numlayers=2)
> lynx.st=layer.series.structure(lynx.norm,numlayers=2)
> 
> layer.pairs(hare.st, lynx.st)
$pairs
      comp1.series comp1.layer comp2.series comp2.layer
pair1            1           1            2           1
pair2            1           1            2           2
pair3            1           2            2           1
pair4            1           2            2           2

$description
[1] "Pair 1: first process is series 1 (hare), layer 1, second process is series 1 (lynx), layer 1"
[2] "Pair 2: first process is series 1 (hare), layer 1, second process is series 1 (lynx), layer 2"
[3] "Pair 3: first process is series 1 (hare), layer 2, second process is series 1 (lynx), layer 1"
[4] "Pair 4: first process is series 1 (hare), layer 2, second process is series 1 (lynx), layer 2"

attr(,"class")
[1] "layer.pairs"
> 
> 
> 
> cleanEx()
> nameEx("layerpairslist")
> ### * layerpairslist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.pairs.list
> ### Title: Alternative connection specification, taking a structure list.
> ### Aliases: layer.pairs.list
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Creates two 2-layered structures based on the two hare/lynx datasets.
> # outputs the pairs and a description
> 
> hare.st=layer.series.structure(hare.norm,numlayers=2)
> lynx.st=layer.series.structure(lynx.norm,numlayers=2)
> 
> layer.pairs.list(list(hare.st, lynx.st))
$pairs
      comp1.series comp1.layer comp2.series comp2.layer
pair1            1           1            2           1
pair2            1           1            2           2
pair3            1           2            2           1
pair4            1           2            2           2

$description
[1] "Pair 1: first process is series 1 (hare), layer 1, second process is series 1 (lynx), layer 1"
[2] "Pair 2: first process is series 1 (hare), layer 1, second process is series 1 (lynx), layer 2"
[3] "Pair 3: first process is series 1 (hare), layer 2, second process is series 1 (lynx), layer 1"
[4] "Pair 4: first process is series 1 (hare), layer 2, second process is series 1 (lynx), layer 2"

attr(,"class")
[1] "layer.pairs"
> 
> 
> 
> cleanEx()
> nameEx("layerparamloglik")
> ### * layerparamloglik
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.param.loglik
> ### Title: Returning log-likelihood for a set of parameter values.
> ### Aliases: layer.param.loglik
> 
> ### ** Examples
> 
> # Define time series (time points, values and in this case also
> # sample standard deviations and sample size, used for calculating
> # standard errors for each measurement).
> # install.packages("../www_docs/R/layeranalyzer_0.2.1.tar.gz",type="source")
> library(layeranalyzer)
> X=layer.data.series(time.points=malta$Time.Year,
+   value.points=malta$Mean..log.body.mass.,
+   std.dev=sqrt(malta$Variance.calculated.from.the.data), 
+   num.meas.per.value=malta$Sample.size,name="log.body.size")
> 
> # Define process structure for analysis purposes:
> ser=layer.series.structure(X,numlayers=1)
> 
> # Perform Bayesian analysis:
> res=layer.analyzer(ser,num.MCMC=100,burnin=1000)
==585586== Conditional jump or move depends on uninitialised value(s)
==585586==    at 0x120840BD: layeranalyzer (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:19254)
==585586==    by 0x4968DF: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:918)
==585586==    by 0x497343: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437)
==585586==    by 0x4D8F3D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==585586==    by 0x4E448A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==585586==    by 0x4E6326: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==585586==    by 0x4E70BF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==585586==    by 0x4E4698: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==585586==    by 0x4E4698: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==585586==    by 0x4E9539: do_set (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:3585)
==585586==    by 0x4E484D: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1230)
==585586==    by 0x51D093: Rf_ReplIteration (/data/localhost/ripley/R/svn/R-devel/src/main/main.c:264)
==585586==  Uninitialised value was created by a heap allocation
==585586==    at 0x48439B2: operator new[](unsigned long) (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:717)
==585586==    by 0x1204B680: loglik(double*, int, int, int, char*, int, char**, int, double**, double***, double***, int*, int*) (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:10455)
==585586==    by 0x120657C1: layer_mcmc(unsigned int, unsigned int, unsigned int, unsigned int, bool, int, int, char*, double****, double*, double, double*, double*, double*, double*, double*) (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:16528)
==585586==    by 0x12085C6F: layeranalyzer (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:18757)
==585586==    by 0x4968DF: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:918)
==585586==    by 0x497343: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437)
==585586==    by 0x4D8F3D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==585586==    by 0x4E448A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==585586==    by 0x4E6326: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==585586==    by 0x4E70BF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==585586==    by 0x4E4698: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==585586==    by 0x4E4698: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==585586== 
> 
> # Run with debug info:
> layer.param.loglik(res, silent.mode=FALSE)
0.001 10
prior_obs1=0.001 prior_obs2=10
num_layers=1
0.001 10
mu         in (-10.000, 10.000), m =  0.000 s =  5.102),
dt         in (  0.001,1000.000), lm=  0.000 ls=  3.524),
sigma      in (  0.001,100.000), lm= -1.151 ls=  2.937),
stat_sdev  in (  0.001, 10.000), lm= -2.303 ls=  2.350),
lin        in (-10.000, 10.000), m =  0.000 s =  5.102),
beta       in (-10.000, 10.000), m =  0.000 s =  5.102),
init       in (-100.000,100.000), m =  0.000 s = 51.020),
obs        in (  0.001, 10.000), lm= -2.303 ls=  2.350).
d.falloff  in (  0.000,1000000.000), lm=  0.000 ls=  7.049).
is_log=0.
0
extseries.size=0
numpar=3
1 mu_log.body.size
2 dt_log.body.size_1
3 stat_sdev_log.body.size_1
*********************************
 Starting loglik treatment
*********************************
currparams=(2.41324  2032.41  0.600547  )
loglik started
start reading parameters
start useext
end useext, start init treatment
start correlations
start feedback
timer stuff starting
numpar=3
1 mu_log.body.size
2 dt_log.body.size_1
3 stat_sdev_log.body.size_1
useext checking starting
correlation2 starting
filling in matrices
eigenvector stuff
m vector stuff
i=0 m_prev[i]=2.41324 pull=0.000492026 m_after[i]=0.00118738
m=(0.00118738  )
dosmooth=0
me=0x15feee68 meas_smooth=0 meas_tot=0x15feee68
 me[0].tm=1996
omega/Lambda matrix stuff
Making P, Q, x matrices
Making residual and prior expectancy matrices
Making smoothing matrices
Correlation3 starting
loglik init2
loglik init3
Sigma matrix starting
Finding V*Sigma^2
Finding V*Sigma^2*V'
Initialize x,u,P,F
num_states=1 num_series=1
mean time 0:2005.18
is_complex=0
num_states=1
W initialized
W,u initialized
meantime filled
meantime=2005.18
Fill W, initialize x, u
t00=1996 first_init_time=-1e+07 meantime=2005.18
W_r=(2.41324  )
Fill u
useext2 starting
sigma^2=(0.000354905  )

Omega_r=(0.000354905  )

A=(-0.000492026  )

lambda_r=(-0.000492026  )
V_r=(1  )

Vinv_r=(1  )

k=0
u_k=(2.41324  )
x_k_prev=(2.41324  )
       Q_k=(  0.3606565365   )

       F_k=(  0.0000000000   )

x_k_now[prev]=(0  )
P_k_now[prev]=(  0.0000000000   )

x_k_prev=(2.41324  )
  P_k_prev=(  0.3606565365   )

s= site=0 numsites=1 index=0 k=0 y_k=0.164032 S_k=0.363503 y_k/sqrt(S_k)=0.272066
k=0 meas=2.57728 x_k_prev[0]=2.41324 y_k=0.164032 S_k= 0.363503 ll= -0.449965 P_k_prev=0.360657 u_k=2.41324
k=1
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.57591  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.57599  )
P_k_now[prev]=(  0.0028243171   )

x_k_prev=(2.57591  )
  P_k_prev=(  0.0031762697   )

s= site=0 numsites=1 index=0 k=1 y_k=-0.0510384 S_k=0.00435055 y_k/sqrt(S_k)=-0.773794
k=1 meas=2.52487 x_k_prev[0]=2.57591 y_k=-0.0510384 S_k= 0.00435055 ll= 1.05045 P_k_prev=0.00317627 u_k=0.00118709
k=2
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.53859  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.53865  )
P_k_now[prev]=(  0.0008573219   )

x_k_prev=(2.53859  )
  P_k_prev=(  0.0012112092   )

s= site=0 numsites=1 index=0 k=2 y_k=-0.0472003 S_k=0.00283255 y_k/sqrt(S_k)=-0.886862
k=2 meas=2.49139 x_k_prev[0]=2.53859 y_k=-0.0472003 S_k= 0.00283255 ll= 2.67153 P_k_prev=0.00121121 u_k=0.00118709
k=3
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.51835  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.5184  )
P_k_now[prev]=(  0.0006932922   )

x_k_prev=(2.51835  )
  P_k_prev=(  0.0010473408   )

s= site=0 numsites=1 index=0 k=3 y_k=-0.122384 S_k=0.00206263 y_k/sqrt(S_k)=-2.69471
k=3 meas=2.39597 x_k_prev[0]=2.51835 y_k=-0.122384 S_k= 0.00206263 ll= 1.21374 P_k_prev=0.00104734 u_k=0.00118709
k=4
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.45619  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.45621  )
P_k_now[prev]=(  0.0005155332   )

x_k_prev=(2.45619  )
  P_k_prev=(  0.0008697567   )

s= site=0 numsites=1 index=0 k=4 y_k=-0.106726 S_k=0.00128868 y_k/sqrt(S_k)=-2.97302
k=4 meas=2.34946 x_k_prev[0]=2.45619 y_k=-0.106726 S_k= 0.00128868 ll= -0.797536 P_k_prev=0.000869757 u_k=0.00118709
k=5
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.38417  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.38416  )
P_k_now[prev]=(  0.0002827394   )

x_k_prev=(2.38417  )
  P_k_prev=(  0.0006371918   )

s= site=0 numsites=1 index=0 k=5 y_k=-0.0231986 S_k=0.00148283 y_k/sqrt(S_k)=-0.602444
k=5 meas=2.36097 x_k_prev[0]=2.38417 y_k=-0.0231986 S_k= 0.00148283 ll= 1.35896 P_k_prev=0.000637192 u_k=0.00118709
k=6
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.37422  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.3742  )
P_k_now[prev]=(  0.0003633814   )

x_k_prev=(2.37422  )
  P_k_prev=(  0.0007177545   )

s= site=0 numsites=1 index=0 k=6 y_k=-0.062093 S_k=0.000906907 y_k/sqrt(S_k)=-2.06187
k=6 meas=2.31213 x_k_prev[0]=2.37422 y_k=-0.062093 S_k= 0.000906907 ll= 1.8171 P_k_prev=0.000717755 u_k=0.00118709
k=7
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.998525 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00355951
after i=0 W_r=0.00355951
u_k=(0.00355951  )
x_k_prev=(2.32521  )
       Q_k=(  0.0010631452   )

       F_k=(  0.9985250096   )

x_k_now[prev]=(2.32508  )
P_k_now[prev]=(  0.0001497014   )

x_k_prev=(2.32521  )
  P_k_prev=(  0.0012124052   )

s= site=0 numsites=1 index=0 k=7 y_k=-0.0114067 S_k=0.00138638 y_k/sqrt(S_k)=-0.306352
k=7 meas=2.3138 x_k_prev[0]=2.32521 y_k=-0.0114067 S_k= 0.00138638 ll= 4.14177 P_k_prev=0.00121241 u_k=0.00355951
k=8
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.31528  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.31523  )
P_k_now[prev]=(  0.0001521418   )

x_k_prev=(2.31528  )
  P_k_prev=(  0.0005067227   )

s= site=0 numsites=1 index=0 k=8 y_k=0.0387434 S_k=0.000835229 y_k/sqrt(S_k)=1.34059
k=8 meas=2.35403 x_k_prev[0]=2.31528 y_k=0.0387434 S_k= 0.000835229 ll= 5.86814 P_k_prev=0.000506723 u_k=0.00118709
k=9
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.33882  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.33879  )
P_k_now[prev]=(  0.0001993006   )

x_k_prev=(2.33882  )
  P_k_prev=(  0.0005538351   )

s= site=0 numsites=1 index=0 k=9 y_k=0.0811406 S_k=0.00263868 y_k/sqrt(S_k)=1.57959
k=9 meas=2.41996 x_k_prev[0]=2.33882 y_k=0.0811406 S_k= 0.00263868 ll= 6.67039 P_k_prev=0.000553835 u_k=0.00118709
k=10
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.35588  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.35586  )
P_k_now[prev]=(  0.0004375903   )

x_k_prev=(2.35588  )
  P_k_prev=(  0.0007918904   )

s= site=0 numsites=1 index=0 k=10 y_k=0.00263976 S_k=0.00148108 y_k/sqrt(S_k)=0.0685924
k=10 meas=2.35852 x_k_prev[0]=2.35588 y_k=0.00263976 S_k= 0.00148108 ll= 9.00659 P_k_prev=0.00079189 u_k=0.00118709
k=11
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.35732  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.35729  )
P_k_now[prev]=(  0.0003684889   )

x_k_prev=(2.35732  )
  P_k_prev=(  0.0007228570   )

s= site=0 numsites=1 index=0 k=11 y_k=0.00656383 S_k=0.00118614 y_k/sqrt(S_k)=0.190585
k=11 meas=2.36389 x_k_prev[0]=2.35732 y_k=0.00656383 S_k= 0.00118614 ll= 11.438 P_k_prev=0.000722857 u_k=0.00118709
k=12
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.36135  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.36132  )
P_k_now[prev]=(  0.0002823342   )

x_k_prev=(2.36135  )
  P_k_prev=(  0.0006367870   )

s= site=0 numsites=1 index=0 k=12 y_k=-0.0183908 S_k=0.00097064 y_k/sqrt(S_k)=-0.590299
k=12 meas=2.34296 x_k_prev[0]=2.36135 y_k=-0.0183908 S_k= 0.00097064 ll= 13.8136 P_k_prev=0.000636787 u_k=0.00118709
k=13
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.34931  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.34928  )
P_k_now[prev]=(  0.0002190237   )

x_k_prev=(2.34931  )
  P_k_prev=(  0.0005735388   )

s= site=0 numsites=1 index=0 k=13 y_k=0.062433 S_k=0.00142915 y_k/sqrt(S_k)=1.65149
k=13 meas=2.41175 x_k_prev[0]=2.34931 y_k=0.062433 S_k= 0.00142915 ll= 14.8063 P_k_prev=0.000573539 u_k=0.00118709
k=14
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.37439  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.37437  )
P_k_now[prev]=(  0.0003433698   )

x_k_prev=(2.37439  )
  P_k_prev=(  0.0006977626   )

s= site=0 numsites=1 index=0 k=14 y_k=-0.0519964 S_k=0.0029132 y_k/sqrt(S_k)=-0.963359
k=14 meas=2.32239 x_k_prev[0]=2.37439 y_k=-0.0519964 S_k= 0.0029132 ll= 16.3426 P_k_prev=0.000697763 u_k=0.00118709
k=15
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.36196  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.36193  )
P_k_now[prev]=(  0.0005306359   )

x_k_prev=(2.36196  )
  P_k_prev=(  0.0008848445   )

s= site=0 numsites=1 index=0 k=15 y_k=-0.00364153 S_k=0.00160062 y_k/sqrt(S_k)=-0.0910206
k=15 meas=2.35832 x_k_prev[0]=2.36196 y_k=-0.00364153 S_k= 0.00160062 ll= 18.6382 P_k_prev=0.000884845 u_k=0.00118709
k=16
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.35997  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.35995  )
P_k_now[prev]=(  0.0003956913   )

x_k_prev=(2.35997  )
  P_k_prev=(  0.0007500326   )

s= site=0 numsites=1 index=0 k=16 y_k=0.00273234 S_k=0.00126284 y_k/sqrt(S_k)=0.0768882
k=16 meas=2.36271 x_k_prev[0]=2.35997 y_k=0.00273234 S_k= 0.00126284 ll= 21.0535 P_k_prev=0.000750033 u_k=0.00118709
loglik started
start reading parameters
start useext
end useext, start init treatment
start correlations
start feedback
timer stuff starting
numpar=3
1 mu_log.body.size
2 dt_log.body.size_1
3 stat_sdev_log.body.size_1
useext checking starting
correlation2 starting
filling in matrices
eigenvector stuff
m vector stuff
i=0 m_prev[i]=2.41324 pull=0.000492026 m_after[i]=0.00118738
m=(0.00118738  )
dosmooth=0
me=0x15feee68 meas_smooth=0 meas_tot=0x15feee68
 me[0].tm=1996
omega/Lambda matrix stuff
Making P, Q, x matrices
Making residual and prior expectancy matrices
Making smoothing matrices
Correlation3 starting
loglik init2
loglik init3
Sigma matrix starting
Finding V*Sigma^2
Finding V*Sigma^2*V'
Initialize x,u,P,F
num_states=1 num_series=1
mean time 0:2005.18
is_complex=0
num_states=1
W initialized
W,u initialized
meantime filled
meantime=2005.18
Fill W, initialize x, u
t00=1996 first_init_time=-1e+07 meantime=2005.18
W_r=(2.41324  )
Fill u
useext2 starting
sigma^2=(0.000354905  )

Omega_r=(0.000354905  )

A=(-0.000492026  )

lambda_r=(-0.000492026  )
V_r=(1  )

Vinv_r=(1  )

k=0
u_k=(2.41324  )
x_k_prev=(2.41324  )
       Q_k=(  0.3606565365   )

       F_k=(  0.0000000000   )

x_k_now[prev]=(0  )
P_k_now[prev]=(  0.0000000000   )

x_k_prev=(2.41324  )
  P_k_prev=(  0.3606565365   )

s= site=0 numsites=1 index=0 k=0 y_k=0.164032 S_k=0.363503 y_k/sqrt(S_k)=0.272066
k=0 meas=2.57728 x_k_prev[0]=2.41324 y_k=0.164032 S_k= 0.363503 ll= -0.449965 P_k_prev=0.360657 u_k=2.41324
k=1
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.57591  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.57599  )
P_k_now[prev]=(  0.0028243171   )

x_k_prev=(2.57591  )
  P_k_prev=(  0.0031762697   )

s= site=0 numsites=1 index=0 k=1 y_k=-0.0510384 S_k=0.00435055 y_k/sqrt(S_k)=-0.773794
k=1 meas=2.52487 x_k_prev[0]=2.57591 y_k=-0.0510384 S_k= 0.00435055 ll= 1.05045 P_k_prev=0.00317627 u_k=0.00118709
k=2
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.53859  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.53865  )
P_k_now[prev]=(  0.0008573219   )

x_k_prev=(2.53859  )
  P_k_prev=(  0.0012112092   )

s= site=0 numsites=1 index=0 k=2 y_k=-0.0472003 S_k=0.00283255 y_k/sqrt(S_k)=-0.886862
k=2 meas=2.49139 x_k_prev[0]=2.53859 y_k=-0.0472003 S_k= 0.00283255 ll= 2.67153 P_k_prev=0.00121121 u_k=0.00118709
k=3
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.51835  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.5184  )
P_k_now[prev]=(  0.0006932922   )

x_k_prev=(2.51835  )
  P_k_prev=(  0.0010473408   )

s= site=0 numsites=1 index=0 k=3 y_k=-0.122384 S_k=0.00206263 y_k/sqrt(S_k)=-2.69471
k=3 meas=2.39597 x_k_prev[0]=2.51835 y_k=-0.122384 S_k= 0.00206263 ll= 1.21374 P_k_prev=0.00104734 u_k=0.00118709
k=4
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.45619  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.45621  )
P_k_now[prev]=(  0.0005155332   )

x_k_prev=(2.45619  )
  P_k_prev=(  0.0008697567   )

s= site=0 numsites=1 index=0 k=4 y_k=-0.106726 S_k=0.00128868 y_k/sqrt(S_k)=-2.97302
k=4 meas=2.34946 x_k_prev[0]=2.45619 y_k=-0.106726 S_k= 0.00128868 ll= -0.797536 P_k_prev=0.000869757 u_k=0.00118709
k=5
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.38417  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.38416  )
P_k_now[prev]=(  0.0002827394   )

x_k_prev=(2.38417  )
  P_k_prev=(  0.0006371918   )

s= site=0 numsites=1 index=0 k=5 y_k=-0.0231986 S_k=0.00148283 y_k/sqrt(S_k)=-0.602444
k=5 meas=2.36097 x_k_prev[0]=2.38417 y_k=-0.0231986 S_k= 0.00148283 ll= 1.35896 P_k_prev=0.000637192 u_k=0.00118709
k=6
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.37422  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.3742  )
P_k_now[prev]=(  0.0003633814   )

x_k_prev=(2.37422  )
  P_k_prev=(  0.0007177545   )

s= site=0 numsites=1 index=0 k=6 y_k=-0.062093 S_k=0.000906907 y_k/sqrt(S_k)=-2.06187
k=6 meas=2.31213 x_k_prev[0]=2.37422 y_k=-0.062093 S_k= 0.000906907 ll= 1.8171 P_k_prev=0.000717755 u_k=0.00118709
k=7
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.998525 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00355951
after i=0 W_r=0.00355951
u_k=(0.00355951  )
x_k_prev=(2.32521  )
       Q_k=(  0.0010631452   )

       F_k=(  0.9985250096   )

x_k_now[prev]=(2.32508  )
P_k_now[prev]=(  0.0001497014   )

x_k_prev=(2.32521  )
  P_k_prev=(  0.0012124052   )

s= site=0 numsites=1 index=0 k=7 y_k=-0.0114067 S_k=0.00138638 y_k/sqrt(S_k)=-0.306352
k=7 meas=2.3138 x_k_prev[0]=2.32521 y_k=-0.0114067 S_k= 0.00138638 ll= 4.14177 P_k_prev=0.00121241 u_k=0.00355951
k=8
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.31528  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.31523  )
P_k_now[prev]=(  0.0001521418   )

x_k_prev=(2.31528  )
  P_k_prev=(  0.0005067227   )

s= site=0 numsites=1 index=0 k=8 y_k=0.0387434 S_k=0.000835229 y_k/sqrt(S_k)=1.34059
k=8 meas=2.35403 x_k_prev[0]=2.31528 y_k=0.0387434 S_k= 0.000835229 ll= 5.86814 P_k_prev=0.000506723 u_k=0.00118709
k=9
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.33882  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.33879  )
P_k_now[prev]=(  0.0001993006   )

x_k_prev=(2.33882  )
  P_k_prev=(  0.0005538351   )

s= site=0 numsites=1 index=0 k=9 y_k=0.0811406 S_k=0.00263868 y_k/sqrt(S_k)=1.57959
k=9 meas=2.41996 x_k_prev[0]=2.33882 y_k=0.0811406 S_k= 0.00263868 ll= 6.67039 P_k_prev=0.000553835 u_k=0.00118709
k=10
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.35588  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.35586  )
P_k_now[prev]=(  0.0004375903   )

x_k_prev=(2.35588  )
  P_k_prev=(  0.0007918904   )

s= site=0 numsites=1 index=0 k=10 y_k=0.00263976 S_k=0.00148108 y_k/sqrt(S_k)=0.0685924
k=10 meas=2.35852 x_k_prev[0]=2.35588 y_k=0.00263976 S_k= 0.00148108 ll= 9.00659 P_k_prev=0.00079189 u_k=0.00118709
k=11
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.35732  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.35729  )
P_k_now[prev]=(  0.0003684889   )

x_k_prev=(2.35732  )
  P_k_prev=(  0.0007228570   )

s= site=0 numsites=1 index=0 k=11 y_k=0.00656383 S_k=0.00118614 y_k/sqrt(S_k)=0.190585
k=11 meas=2.36389 x_k_prev[0]=2.35732 y_k=0.00656383 S_k= 0.00118614 ll= 11.438 P_k_prev=0.000722857 u_k=0.00118709
k=12
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.36135  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.36132  )
P_k_now[prev]=(  0.0002823342   )

x_k_prev=(2.36135  )
  P_k_prev=(  0.0006367870   )

s= site=0 numsites=1 index=0 k=12 y_k=-0.0183908 S_k=0.00097064 y_k/sqrt(S_k)=-0.590299
k=12 meas=2.34296 x_k_prev[0]=2.36135 y_k=-0.0183908 S_k= 0.00097064 ll= 13.8136 P_k_prev=0.000636787 u_k=0.00118709
k=13
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.34931  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.34928  )
P_k_now[prev]=(  0.0002190237   )

x_k_prev=(2.34931  )
  P_k_prev=(  0.0005735388   )

s= site=0 numsites=1 index=0 k=13 y_k=0.062433 S_k=0.00142915 y_k/sqrt(S_k)=1.65149
k=13 meas=2.41175 x_k_prev[0]=2.34931 y_k=0.062433 S_k= 0.00142915 ll= 14.8063 P_k_prev=0.000573539 u_k=0.00118709
k=14
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.37439  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.37437  )
P_k_now[prev]=(  0.0003433698   )

x_k_prev=(2.37439  )
  P_k_prev=(  0.0006977626   )

s= site=0 numsites=1 index=0 k=14 y_k=-0.0519964 S_k=0.0029132 y_k/sqrt(S_k)=-0.963359
k=14 meas=2.32239 x_k_prev[0]=2.37439 y_k=-0.0519964 S_k= 0.0029132 ll= 16.3426 P_k_prev=0.000697763 u_k=0.00118709
k=15
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.36196  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.36193  )
P_k_now[prev]=(  0.0005306359   )

x_k_prev=(2.36196  )
  P_k_prev=(  0.0008848445   )

s= site=0 numsites=1 index=0 k=15 y_k=-0.00364153 S_k=0.00160062 y_k/sqrt(S_k)=-0.0910206
k=15 meas=2.35832 x_k_prev[0]=2.36196 y_k=-0.00364153 S_k= 0.00160062 ll= 18.6382 P_k_prev=0.000884845 u_k=0.00118709
k=16
start i=0 W_r=0
b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0
m/B   i=0 W_r=0.00118709
after i=0 W_r=0.00118709
u_k=(0.00118709  )
x_k_prev=(2.35997  )
       Q_k=(  0.0003547305   )

       F_k=(  0.9995080946   )

x_k_now[prev]=(2.35995  )
P_k_now[prev]=(  0.0003956913   )

x_k_prev=(2.35997  )
  P_k_prev=(  0.0007500326   )

s= site=0 numsites=1 index=0 k=16 y_k=0.00273234 S_k=0.00126284 y_k/sqrt(S_k)=0.0768882
k=16 meas=2.36271 x_k_prev[0]=2.35997 y_k=0.00273234 S_k= 0.00126284 ll= 21.0535 P_k_prev=0.000750033 u_k=0.00118709
[1] 21.05351
> 
> 
> 
> 
> cleanEx()
> nameEx("layerparamlogliks")
> ### * layerparamlogliks
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.param.logliks
> ### Title: Returning log-likelihoods for a set of parameter value sets.
> ### Aliases: layer.param.logliks
> 
> ### ** Examples
> 
> # Define time series (time points, values and in this case also
> # sample standard deviations and sample size, used for calculating
> # standard errors for each measurement).
> library(layeranalyzer)
> X=layer.data.series(time.points=malta$Time.Year,
+   value.points=malta$Mean..log.body.mass.,
+   std.dev=sqrt(malta$Variance.calculated.from.the.data), 
+   num.meas.per.value=malta$Sample.size,name="log.body.size")
> 
> # Define process structure for analysis purposes:
> ser=layer.series.structure(X,numlayers=1)
> 
> # Perform Bayesian analysis, short run:
> res=layer.analyzer(ser,num.MCMC=100,burnin=1000) 
> 
> # Check one specific parameter set, then the same without
> # mu (expected value), dt (characteristic time) and
> # stat-sdev (stationary standaard deviation) specified. (Unspecified
> # parameters are optimized over.)
> sets=rbind(c(2.4, 300, 0.04), c(NA, 300, 0.04), c(2.4, NA, 0.04), c(2.4,300,NA))
> lls=layer.param.logliks(res, sets, silent.mode=FALSE)
0.001 10
prior_obs1=0.001 prior_obs2=10
num_layers=1
0.001 10
mu         in (-10.000, 10.000), m =  0.000 s =  5.102),
dt         in (  0.001,1000.000), lm=  0.000 ls=  3.524),
sigma      in (  0.001,100.000), lm= -1.151 ls=  2.937),
stat_sdev  in (  0.001, 10.000), lm= -2.303 ls=  2.350),
lin        in (-10.000, 10.000), m =  0.000 s =  5.102),
beta       in (-10.000, 10.000), m =  0.000 s =  5.102),
init       in (-100.000,100.000), m =  0.000 s = 51.020),
obs        in (  0.001, 10.000), lm= -2.303 ls=  2.350).
d.falloff  in (  0.000,1000000.000), lm=  0.000 ls=  7.049).
is_log=0.
0
extseries.size=0
numpar=3
1 mu_log.body.size
2 dt_log.body.size_1
3 stat_sdev_log.body.size_1
*********************************
 Starting loglik treatment
*********************************
currparams=(2.4  300  0.04  )
currparams=(-1e+07  300  0.04  )
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
currparams=(2.4  -1e+07  0.04  )
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
currparams=(2.4  300  -1e+07  )
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead",  :
  one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
> 
> 
> 
> 
> cleanEx()
> nameEx("layerpredictestimate")
> ### * layerpredictestimate
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.predict.estimate
> ### Title: Prediction based on previously performed analysis using single
> ###   point estimate for the parameter set
> ### Aliases: layer.predict.estimate
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Define time series (time points, values and in this case also
> # sample standard deviations and sample size, used for calculating
> # standard errors for each measurement). 
> X=layer.data.series(time.points=malta$Time.Year,
+   value.points=malta$Mean..log.body.mass.,
+   std.dev=sqrt(malta$Variance.calculated.from.the.data), 
+   num.meas.per.value=malta$Sample.size,name="log.body.size")
> 
> # Define process structure for analysis purposes:
> ser=layer.series.structure(X,numlayers=1)
> 
> # Perform Bayesian analysis:
> res=layer.analyzer(ser,mcmc=TRUE,num.MCMC=200,burnin=800)
> 
> # Now make up some new values much later:
> t2=2100:2140
> v2=rnorm(length(t2),2+0.1*sin(2*pi*t2/50),0.02)
> X2=layer.data.series(time.points=t2,
+    value.points=v2, std.dev=rep(0.02,length(t2)),
+    num.meas.per.value=rep(1,length(t2)),
+    name="log.body.size")
> 
> # Perform predictions (old data not conditioned on):
> p3=layer.predict.estimate(X2, analysis=res,  smoothing.time.diff=0.25,
+    smoothing.start=1900,smoothing.end=2300)
> 
> # Fetch the results
> t3=p3$process.time.points
> m3=p3$process.mean[1,]
> l3=p3$process.lower95[1,]
> u3=p3$process.upper95[1,]
> 
> # Plot the results
> plot(t3,m3,type="l",ylim=c(min(l3),max(u3)))
> points(t2,v2)
> lines(t3,l3,col="red")
> lines(t3,u3,col="red")
> 
>  
> 
> 
> 
> 
> cleanEx()
> nameEx("layerpredictmcmc")
> ### * layerpredictmcmc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.predict.mcmc
> ### Title: Prediction based on previously performed MCMC analysis
> ### Aliases: layer.predict.mcmc
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Define time series (time points, values and in this case also
> # sample standard deviations and sample size, used for calculating
> # standard errors for each measurement). 
> X<-layer.data.series(time.points=malta$Time.Year,
+   value.points=malta$Mean..log.body.mass.,
+   std.dev=sqrt(malta$Variance.calculated.from.the.data), 
+   num.meas.per.value=malta$Sample.size,name="log.body.size")
> 
> # Define process structure for analysis purposes:
> ser<-layer.series.structure(X,numlayers=1)
> 
> # Perform analysis and make sure MCMC samples are returned:
> 
> 
> 
> 
> cleanEx()
> nameEx("layerpredictmcmclist")
> ### * layerpredictmcmclist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.predict.mcmc.list
> ### Title: Prediction based on previously performed MCMC analysis
> ### Aliases: layer.predict.mcmc.list
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Define time series (time points, values and in this case also
> # sample standard deviations and sample size, used for calculating
> # standard errors for each measurement). 
> X<-layer.data.series(time.points=malta$Time.Year,
+   value.points=malta$Mean..log.body.mass.,
+   std.dev=sqrt(malta$Variance.calculated.from.the.data), 
+   num.meas.per.value=malta$Sample.size,name="log.body.size")
> 
> # Define process structure for analysis purposes:
> ser<-layer.series.structure(X,numlayers=1)
> 
> # Perform analysis and make sure MCMC samples are returned:
> 
> 
> 
> 
> cleanEx()
> nameEx("layerprior")
> ### * layerprior
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.prior
> ### Title: Specifying a prior distribution for the layer.analyzer.
> ### Aliases: layer.prior
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> pr=layer.prior(mu=c(-10,10),dt=c(0.001,1000),sigma=c(0.01,10),
+ 	                         lin=c(-1,1),beta=c(-1,1),init=c(-10,10),
+                                  obs=c(0.01,1),islog=0)
> 
> 
> 
> cleanEx()
> nameEx("layerseriesstructure")
> ### * layerseriesstructure
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.series.structure
> ### Title: Represents a measurement time series.
> ### Aliases: layer.series.structure
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Will look at the structure of a single simulated
> # time series (simulated using a 2-layered model)
> 
> # Read a simulated dataset with 2 layers:
> test.ex=read.table("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt")
> names(test.ex)<-c("time","value")
> ts<-layer.data.series(time=test.ex$time, value=test.ex$value,name="X")
> 
> # Look at 2 models, where the only difference is the structure
> # specified in 'layer.series.structure':
> 
> # ML estimates for an OU model:
> 
> # ML estimates for an 2-layered model model:
> 
> # PS: The models can then be compared, see 'compare.layered'.
> 
> 
> 
> cleanEx()
> nameEx("layerstandardlogprior")
> ### * layerstandardlogprior
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.standard.log.prior
> ### Title: Specifying a suggestion for prior distribution, for data that
> ###   will be log-transformed.
> ### Aliases: layer.standard.log.prior
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Output the hyperparameters of the standard prior for
> # log-transformed measurements
> layer.standard.log.prior
$mu
[1] -10  10

$dt
[1] 1e-03 1e+03

$sigma
[1] 1e-04 1e+02

$stat.sdev
[1] 1e-03 1e+01

$init
[1] -100  100

$lin
[1] -10  10

$beta
[1] -10  10

$dist.corr.falloff
[1] 1e-06 1e+06

$obs
[1] 1e-03 1e+01

$islog
[1] 1

attr(,"class")
[1] "layer.prior"
> 
> 
> 
> cleanEx()
> nameEx("layerstandardprior")
> ### * layerstandardprior
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.standard.prior
> ### Title: Specifying a default prior distribution for the layer.analyzer.
> ### Aliases: layer.standard.prior
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Output the hyperparameters of the standard (default) prior
> layer.standard.prior
$mu
[1] -10  10

$dt
[1] 1e-03 1e+03

$sigma
[1] 1e-03 1e+02

$stat.sdev
[1] 1e-03 1e+01

$init
[1] -100  100

$lin
[1] -10  10

$beta
[1] -10  10

$dist.corr.falloff
[1] 1e-06 1e+06

$obs
[1] 1e-03 1e+01

$islog
[1] 0

attr(,"class")
[1] "layer.prior"
> 
> 
> 
> cleanEx()
> nameEx("layerstructname")
> ### * layerstructname
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.struct.name
> ### Title: Generate a description of the process structure of a time
> ###   series.
> ### Aliases: layer.struct.name
> 
> ### ** Examples
> 
> library(layeranalyzer)
> X<-read.layer.data.series(
+  "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt",
+  name="X")
> 
> # Insert the data intro a structure specificatiion
> # with the default structure (OU):
> struct1=layer.series.structure(X) 
> layer.struct.name(struct1)
[1] "OU"
> # Output should be "OU"
> 
> # Insert the data into a structure with 2 layers, where the
> # lowest layer has no pull (i.e. Brownian motion, here also called
> # "Random Walk" which is abbreviated to "RW"). Note that when the
> # lowest layer is Brownian, the processes are nonstationary and
> # need initial value treatment (init.0=TRUE).
> # The top layer is specified to have no stochasticity
> # (i.e. deterministic given the layer below it). 
> struct2=layer.series.structure(X,numlayers=2, no.pull=TRUE, no.sigma=1,
+    init.0=TRUE) 
> layer.struct.name(struct2)
[1] "2-layered: Layer 2: RW , Layer 1: deterministic tracking"
> # Output should be "2-layered: Layer 2: RW , Layer 1: deterministic tracking"
> 
> 
> 
> 
> cleanEx()
> nameEx("layerwideprior")
> ### * layerwideprior
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: layer.wide.prior
> ### Title: Specifying a suggestion for a wide (little information) prior
> ###   distribution.
> ### Aliases: layer.wide.prior
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Output the hyperparameters of a prior distribution that is assumes
> # very little (little information):
> layer.wide.prior
$mu
[1] -10000  10000

$dt
[1] 1e-06 1e+06

$sigma
[1] 1e-03 1e+03

$stat.sdev
[1] 1e-05 1e+04

$init
[1] -1e+05  1e+05

$lin
[1] -1000  1000

$beta
[1] -1000  1000

$dist.corr.falloff
[1] 1e-20 1e+20

$obs
[1] 1e-05 1e+03

$islog
[1] 0

attr(,"class")
[1] "layer.prior"
> 
> 
> 
> cleanEx()
> nameEx("logliklayered")
> ### * logliklayered
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: logLik.layered
> ### Title: Return maximum likelihood on the logarithmic scale for return
> ###   object from 'layer.analyzer'.
> ### Aliases: logLik.layered
> 
> ### ** Examples
> 
> library(layeranalyzer)
> pr=layer.load.prior("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_prior.txt")
> X<-read.layer.data.series(
+  "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt",
+  name="X")
> 
> # Calculate classic maximum likelihoods for an OU model, a 2 layered model
> # and a 3 layered model. Look at log-likelihoods:
> 
> 
> 
> cleanEx()
> nameEx("lratepr")
> ### * lratepr
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lrate.pr
> ### Title: A "layer.prior" object used for diversifications rate analysis.
> ### Aliases: lrate.pr
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Output the hyperparameters of a prior distribution we have used
> # for diversification rate datasets:
> lrate.pr
$mu
[1] -20  20

$dt
[1] 1e-03 1e+03

$sigma
[1] 1e-02 1e+02

$init
[1] -20  20

$lin
[1] -10  10

$beta
[1] -10  10

$islog
[1] 0

attr(,"class")
[1] "layer.prior"
> 
> 
> 
> cleanEx()
> nameEx("ncombinations")
> ### * ncombinations
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: n.combinations
> ### Title: Returns all ways of combining 'n' objects going form 0 to
> ###   <base>.
> ### Aliases: n.combinations
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # List the set of combinations up to (but not including) 2
> # of 3 elements (used by the model traversal algorithms):
> n.combinations(3,2)
     [,1] [,2] [,3]
[1,]    0    0    0
[2,]    0    0    1
[3,]    0    1    0
[4,]    0    1    1
[5,]    1    0    0
[6,]    1    0    1
[7,]    1    1    0
[8,]    1    1    1
> # This should be the output:
> #    [,1] [,2] [,3]
> #[1,]    0    0    0
> #[2,]    0    0    1
> #[3,]    0    1    0
> #[4,]    0    1    1
> #[5,]    1    0    0
> #[6,]    1    0    1
> #[7,]    1    1    0
> #[8,]    1    1    1
> 
> 
> 
> 
> cleanEx()
> nameEx("nobslayered")
> ### * nobslayered
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: nobs.layered
> ### Title: Return number of observations for return object from
> ###   'layer.analyzer'.
> ### Aliases: nobs.layered
> 
> ### ** Examples
> 
> library(layeranalyzer)
> # Read prior and data:
> pr=layer.load.prior("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_prior.txt")
> X<-read.layer.data.series(
+  "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt",
+  name="X")
> 
> # Do a very short analysis, jsut to get a "layered" object we can run
> # "nobs" on.
> res1<-layer.analyzer(layer.series.structure(X,numlayers=1,prior=pr),
+   num.MCMC=40,burnin=400,do.model.likelihood=FALSE)
> nobs(res1)
[1] 400
> # Output should be: 400.
> 
> 
> 
> cleanEx()
> nameEx("printlayerseriesstructure")
> ### * printlayerseriesstructure
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.layer.series.structure
> ### Title: Outputs the description of a 'layer'series'structure' object.
> ### Aliases: print.layer.series.structure
> 
> ### ** Examples
> 
> library(layeranalyzer)
> # Read an example dataset:
> X<-read.layer.data.series(
+   "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt",
+   "X")
> 
> # Make a structure specification that happens to coincide with the way
> # the data was simulated, namely as 2 layers with OU as the lowest layer:
> struct=layer.series.structure(X,numlayers=2)
> 
> # Show a description of the structure:
> print.layer.series.structure(struct)
Timeseries: X, structure:
2-layered: Layer 2: OU , Layer 1: OU-like tracking
> # Should output:
> # Timeseries: X, structure:
> # 2-layered: Layer 2: OU , Layer 1: OU-like tracking
> 
> 
> 
> 
> cleanEx()
> nameEx("printsummarylayered")
> ### * printsummarylayered
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.summary.layered
> ### Title: Summarizes the return object from 'layer.analyzer'.
> ### Aliases: print.summary.layered
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Read prior and data:
> pr<-layer.load.prior("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_prior.txt")
> X<-read.layer.data.series(
+  "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_1layer.txt",
+  "X")
> 
> # Calculate Bayesian model likelihood for an OU model.
> # (Devotes a large amount of computer resources in the form of MCMC
> # samples, in order to get a stable results).
> 
> 
> 
> 
> cleanEx()
> nameEx("readlayerdataseries")
> ### * readlayerdataseries
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: read.layer.data.series
> ### Title: Read a series representing a measurement time series.
> ### Aliases: read.layer.data.series
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Read a simulated 2 layer data set from an external web site, call it "Xseries":
> X<-read.layer.data.series(
+  "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt",
+  name="X")
> 
> 
> 
> 
> 
> cleanEx()
> nameEx("stepwiseconnectionslayered")
> ### * stepwiseconnectionslayered
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: stepwise.connections.layered
> ### Title: Time series analysis tool using linear layered SDEs.
> ### Aliases: stepwise.connections.layered
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Read data:
> X1<-read.layer.data.series(
+  "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_cause.txt",
+  name="cause")
> X2<-read.layer.data.series(
+  "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_effect.txt",
+  name="effect")
> 
> # Set structure:
> X1struct=layer.series.structure(X1, numlayers=1)
> X2struct=layer.series.structure(X2, numlayers=1)
> 
> # Perform traversal of analyses. (PS: this will take some time!)
> 
> # PS: Since only the model of the last iteration is available,
> # model comparison is not a priority and the method simply returns
> # the best model according to the model selection criterion.
> 
> # Show the estiamtes of the best model:
> # Highlight of summary:
> # Causal links:
> #   cause->effect
> # As it should be.
> 
> 
> 
> 
> 
> cleanEx()
> nameEx("summarylayered")
> ### * summarylayered
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.layered
> ### Title: Summarizes the return object from 'layer.analyzer'.
> ### Aliases: summary.layered
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Read prior and dataset:
> pr=layer.load.prior("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_prior.txt")
> X<-read.layer.data.series(
+  "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_1layer.txt",
+  "X")
> 
> # Calculate Bayesian model likelihood for an OU model.
> # (PS: The MCMC is restricted, to save time in the example).
> res1<-layer.analyzer(layer.series.structure(X,numlayers=1,prior=pr),
+   num.MCMC=60,burnin=600)
> summary(res1)
Description:
OU

Coefficients:
                   Mean    Median Lower 95% Upper 95%
mu_X           1.702512  1.716915  0.075254  3.424567
dt_X_1        12.510531 11.721703  4.087112 23.321316
stat_sdev_X_1  1.957960  1.967744  1.132939  2.828860
obs_sd_X       0.087047  0.084585  0.044445  0.141681

Model log-likelihood:  -136.032
> 
> # PS: There will be moderately-sized numeric variation in the estimates
> # due to the randomness of the MCMC algorithm. A longer MCMC (or
> # multiple of them) would yield more stable results.
> 
> 
> 
> cleanEx()
> nameEx("traverseconnections")
> ### * traverseconnections
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: traverse.connections.layered
> ### Title: Time series analysis tool using linear layered SDEs.
> ### Aliases: traverse.connections.layered
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Read data:
> X1<-read.layer.data.series(
+  "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_cause.txt",
+  name="cause")
> X2<-read.layer.data.series(
+  "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_effect.txt",
+  name="effect")
> 
> # Set structure:
> X1struct<-layer.series.structure(X1, numlayers=1)
> X2struct<-layer.series.structure(X2, numlayers=1)
> 
> # Perform traversal of analyses (but note that this will take some time):
> 
> # Compare analyses:
> 
> 
> 
> cleanEx()
> nameEx("traversestandalone")
> ### * traversestandalone
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: traverse.standalone.layered
> ### Title: Model traversal tool for standalone time series.
> ### Aliases: traverse.standalone.layered
> 
> ### ** Examples
> 
> library(layeranalyzer)
> 
> # Specify the prior distribution by giving 95% credibility bands
> # for the parameter types:
> p<-layer.prior(mu=c(-5,5),init=c(-100,100),dt=c(0.5,200),sigma=c(0.01,2),obs=c(0.01,1))
> 
> # Read the data, a simulated OU (1-layered) process:
> X<-read.layer.data.series(
+  "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_1layer.txt",
+  "X")
> 
> # Traverse the possible standalone models up to 2 layers:
> 
> 
> 
> 
> ### * <FOOTER>
> ###
> cleanEx()
> options(digits = 7L)
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
Time elapsed:  709.948 2.527 721.951 0.09 0.29 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')
==585586== 
==585586== HEAP SUMMARY:
==585586==     in use at exit: 66,852,509 bytes in 21,168 blocks
==585586==   total heap usage: 139,038,239 allocs, 139,017,071 frees, 2,454,565,940 bytes allocated
==585586== 
==585586== 24 bytes in 1 blocks are definitely lost in loss record 569 of 2,563
==585586==    at 0x48439B2: operator new[](unsigned long) (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:717)
==585586==    by 0x1208019A: layeranalyzer (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:19311)
==585586==    by 0x4968DF: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:918)
==585586==    by 0x497343: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437)
==585586==    by 0x4D8F3D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==585586==    by 0x4E448A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==585586==    by 0x4E6326: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==585586==    by 0x4E70BF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==585586==    by 0x4E4698: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==585586==    by 0x4E4698: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==585586==    by 0x51D093: Rf_ReplIteration (/data/localhost/ripley/R/svn/R-devel/src/main/main.c:264)
==585586==    by 0x51D477: R_ReplConsole (/data/localhost/ripley/R/svn/R-devel/src/main/main.c:317)
==585586== 
==585586== 24 bytes in 1 blocks are definitely lost in loss record 570 of 2,563
==585586==    at 0x48439B2: operator new[](unsigned long) (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:717)
==585586==    by 0x1208179D: layeranalyzer (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:19623)
==585586==    by 0x4968DF: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:918)
==585586==    by 0x497343: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437)
==585586==    by 0x4D8F3D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==585586==    by 0x4E448A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==585586==    by 0x4E6326: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==585586==    by 0x4E70BF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==585586==    by 0x4E4698: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==585586==    by 0x4E4698: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==585586==    by 0x51D093: Rf_ReplIteration (/data/localhost/ripley/R/svn/R-devel/src/main/main.c:264)
==585586==    by 0x51D477: R_ReplConsole (/data/localhost/ripley/R/svn/R-devel/src/main/main.c:317)
==585586== 
==585586== 32 bytes in 8 blocks are definitely lost in loss record 620 of 2,563
==585586==    at 0x48439B2: operator new[](unsigned long) (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:717)
==585586==    by 0x12065910: layer_mcmc(unsigned int, unsigned int, unsigned int, unsigned int, bool, int, int, char*, double****, double*, double, double*, double*, double*, double*, double*) (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:16553)
==585586==    by 0x12085C6F: layeranalyzer (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:18757)
==585586==    by 0x4968DF: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:918)
==585586==    by 0x497343: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437)
==585586==    by 0x4D8F3D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==585586==    by 0x4E448A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==585586==    by 0x4E6326: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==585586==    by 0x4E70BF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==585586==    by 0x4E4698: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==585586==    by 0x4E4698: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==585586==    by 0x4E9539: do_set (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:3585)
==585586== 
==585586== 48 bytes in 2 blocks are definitely lost in loss record 687 of 2,563
==585586==    at 0x48439B2: operator new[](unsigned long) (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:717)
==585586==    by 0x1208019A: layeranalyzer (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:19311)
==585586==    by 0x4968DF: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:918)
==585586==    by 0x497343: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437)
==585586==    by 0x4D8F3D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==585586==    by 0x4E448A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==585586==    by 0x4E6326: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==585586==    by 0x4E70BF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==585586==    by 0x4E4698: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==585586==    by 0x4E4698: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==585586==    by 0x4E9539: do_set (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:3585)
==585586==    by 0x4E484D: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1230)
==585586== 
==585586== 48 bytes in 2 blocks are definitely lost in loss record 688 of 2,563
==585586==    at 0x48439B2: operator new[](unsigned long) (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:717)
==585586==    by 0x1208179D: layeranalyzer (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:19623)
==585586==    by 0x4968DF: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:918)
==585586==    by 0x497343: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437)
==585586==    by 0x4D8F3D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==585586==    by 0x4E448A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==585586==    by 0x4E6326: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==585586==    by 0x4E70BF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==585586==    by 0x4E4698: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==585586==    by 0x4E4698: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==585586==    by 0x4E9539: do_set (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:3585)
==585586==    by 0x4E484D: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1230)
==585586== 
==585586== 64 bytes in 8 blocks are definitely lost in loss record 737 of 2,563
==585586==    at 0x48439B2: operator new[](unsigned long) (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:717)
==585586==    by 0x120658E8: layer_mcmc(unsigned int, unsigned int, unsigned int, unsigned int, bool, int, int, char*, double****, double*, double, double*, double*, double*, double*, double*) (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:16543)
==585586==    by 0x12085C6F: layeranalyzer (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:18757)
==585586==    by 0x4968DF: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:918)
==585586==    by 0x497343: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437)
==585586==    by 0x4D8F3D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==585586==    by 0x4E448A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==585586==    by 0x4E6326: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==585586==    by 0x4E70BF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==585586==    by 0x4E4698: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==585586==    by 0x4E4698: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==585586==    by 0x4E9539: do_set (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:3585)
==585586== 
==585586== 64 bytes in 8 blocks are definitely lost in loss record 738 of 2,563
==585586==    at 0x48439B2: operator new[](unsigned long) (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:717)
==585586==    by 0x120658FC: layer_mcmc(unsigned int, unsigned int, unsigned int, unsigned int, bool, int, int, char*, double****, double*, double, double*, double*, double*, double*, double*) (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:16550)
==585586==    by 0x12085C6F: layeranalyzer (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:18757)
==585586==    by 0x4968DF: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:918)
==585586==    by 0x497343: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437)
==585586==    by 0x4D8F3D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==585586==    by 0x4E448A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==585586==    by 0x4E6326: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==585586==    by 0x4E70BF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==585586==    by 0x4E4698: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==585586==    by 0x4E4698: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==585586==    by 0x4E9539: do_set (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:3585)
==585586== 
==585586== 1,256 (1,024 direct, 232 indirect) bytes in 8 blocks are definitely lost in loss record 1,114 of 2,563
==585586==    at 0x48439B2: operator new[](unsigned long) (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:717)
==585586==    by 0x1206586A: layer_mcmc(unsigned int, unsigned int, unsigned int, unsigned int, bool, int, int, char*, double****, double*, double, double*, double*, double*, double*, double*) (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:16539)
==585586==    by 0x12085C6F: layeranalyzer (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:18757)
==585586==    by 0x4968DF: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:918)
==585586==    by 0x497343: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437)
==585586==    by 0x4D8F3D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==585586==    by 0x4E448A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==585586==    by 0x4E6326: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==585586==    by 0x4E70BF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==585586==    by 0x4E4698: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==585586==    by 0x4E4698: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==585586==    by 0x4E9539: do_set (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:3585)
==585586== 
==585586== 4,960 bytes in 8 blocks are definitely lost in loss record 1,236 of 2,563
==585586==    at 0x48439B2: operator new[](unsigned long) (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:717)
==585586==    by 0x12065834: layer_mcmc(unsigned int, unsigned int, unsigned int, unsigned int, bool, int, int, char*, double****, double*, double, double*, double*, double*, double*, double*) (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:16534)
==585586==    by 0x12085C6F: layeranalyzer (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:18757)
==585586==    by 0x4968DF: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:918)
==585586==    by 0x497343: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437)
==585586==    by 0x4D8F3D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==585586==    by 0x4E448A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==585586==    by 0x4E6326: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==585586==    by 0x4E70BF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==585586==    by 0x4E4698: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==585586==    by 0x4E4698: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==585586==    by 0x4E9539: do_set (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:3585)
==585586== 
==585586== 4,960 bytes in 8 blocks are definitely lost in loss record 1,237 of 2,563
==585586==    at 0x48439B2: operator new[](unsigned long) (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:717)
==585586==    by 0x12065843: layer_mcmc(unsigned int, unsigned int, unsigned int, unsigned int, bool, int, int, char*, double****, double*, double, double*, double*, double*, double*, double*) (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:16535)
==585586==    by 0x12085C6F: layeranalyzer (/data/localhost/ripley/R/packages/tests-vg/layeranalyzer/src/layeranalyzer.cpp:18757)
==585586==    by 0x4968DF: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:918)
==585586==    by 0x497343: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437)
==585586==    by 0x4D8F3D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==585586==    by 0x4E40C1: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==585586==    by 0x4E448A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==585586==    by 0x4E6326: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==585586==    by 0x4E70BF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==585586==    by 0x4E4698: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==585586==    by 0x4E4698: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==585586==    by 0x4E9539: do_set (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:3585)
==585586== 
==585586== LEAK SUMMARY:
==585586==    definitely lost: 11,248 bytes in 54 blocks
==585586==    indirectly lost: 232 bytes in 8 blocks
==585586==      possibly lost: 0 bytes in 0 blocks
==585586==    still reachable: 66,841,029 bytes in 21,106 blocks
==585586==         suppressed: 0 bytes in 0 blocks
==585586== Reachable blocks (those to which a pointer was found) are not shown.
==585586== To see them, rerun with: --leak-check=full --show-leak-kinds=all
==585586== 
==585586== For lists of detected and suppressed errors, rerun with: -s
==585586== ERROR SUMMARY: 13 errors from 11 contexts (suppressed: 0 from 0)