CRAN Package Check Results for Package copula

Last updated on 2014-04-17 02:47:44.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.999-8 10.61 113.12 123.73 ERROR
r-devel-linux-x86_64-debian-gcc 0.999-8 10.99 114.45 125.44 ERROR
r-devel-linux-x86_64-fedora-clang 0.999-8 646.98 NOTE
r-devel-linux-x86_64-fedora-gcc 0.999-8 604.10 NOTE
r-devel-macosx-x86_64-clang 0.999-8 480.43 OK
r-devel-macosx-x86_64-gcc 0.999-8 NOTE
r-devel-windows-ix86+x86_64 0.999-8 38.00 172.00 210.00 ERROR
r-patched-linux-x86_64 0.999-8 11.13 114.06 125.18 ERROR
r-patched-solaris-sparc 0.999-8 2586.70 OK --no-vignettes
r-patched-solaris-x86 0.999-8 720.10 OK
r-release-linux-ix86 0.999-8 24.00 164.00 188.00 ERROR
r-release-linux-x86_64 0.999-8 10.75 116.01 126.76 ERROR
r-release-macosx-x86_64 0.999-8 OK
r-release-windows-ix86+x86_64 0.999-8 42.00 180.00 222.00 ERROR
r-oldrel-windows-ix86+x86_64 0.999-8 39.00 167.00 206.00 ERROR

Check Details

Version: 0.999-8
Check: examples
Result: ERROR
    Running examples in ‘copula-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: fitCopula
    > ### Title: Estimation of the Parameters in Copula Models
    > ### Aliases: fitCopula loglikCopula
    > ### Keywords: models multivariate
    >
    > ### ** Examples
    >
    > gumbel.cop <- gumbelCopula(3, dim=2)
    >
    > (Xtras <- copula:::doExtras())
    [1] FALSE
    > n <- if(Xtras) 200 else 64
    >
    > x <- rCopula(n, gumbel.cop)## "true" observations
    > u <- pobs(x) ## pseudo-observations
    > ## inverting Kendall's tau
    > fit.tau <- fitCopula(gumbel.cop, u, method="itau")
    > fit.tau
    fitCopula() estimation based on 'inversion of Kendall's tau'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 2.6457 0.4692 5.639 1.71e-08 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    > coef(fit.tau)# named vector
     param
    2.645669
    > ## inverting Spearman's rho
    > fit.rho <- fitCopula(gumbel.cop, u, method="irho")
    > fit.rho
    fitCopula() estimation based on 'inversion of Spearman's rho'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 2.5309 0.3979 6.36 2.02e-10 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    > ## maximum pseudo-likelihood
    > fit.mpl <- fitCopula(gumbel.cop, u, method="mpl")
    > fit.mpl
    fitCopula() estimation based on 'maximum pseudo-likelihood'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 2.9086 0.5099 5.704 1.17e-08 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    The maximized loglikelihood is 42.28
    Optimization converged
    Number of loglikelihood evaluations:
    function gradient
     22 5
    > ## maximum likelihood
    > fit.ml <- fitCopula(gumbel.cop, x, method="ml")
    > fit.ml # print()ing works via summary() ...
    fitCopula() estimation based on 'maximum likelihood'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 3.2459 0.3418 9.496 <2e-16 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    The maximized loglikelihood is 46.25
    Optimization converged
    Number of loglikelihood evaluations:
    function gradient
     19 6
    > ## and of that, what's the log likelihood (in two different ways):
    > (ll. <- logLik(fit.ml))
    'log Lik.' 46.24639 (df=1)
    > stopifnot(all.equal(as.numeric(ll.),
    + loglikCopula(coef(fit.ml), x=x, copula=gumbel.cop)))
    >
    > ## a multiparameter example
    > normal.cop <- normalCopula(c(0.6,0.36, 0.6),dim=3,dispstr="un")
    > x <- rCopula(n, normal.cop) ## "true" observations
    > u <- pobs(x) ## pseudo-observations
    > ## inverting Kendall's tau
    > fit.tau <- fitCopula(normal.cop, u, method="itau")
    > fit.tau
    fitCopula() estimation based on 'inversion of Kendall's tau'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.4824 0.1232 3.916 8.99e-05 ***
    rho.2 0.2119 0.1417 1.495 0.135
    rho.3 0.5161 0.0957 5.393 6.92e-08 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    > ## inverting Spearman's rho
    > fit.rho <- fitCopula(normal.cop, u, method="irho")
    > fit.rho
    fitCopula() estimation based on 'inversion of Spearman's rho'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.46185 0.11717 3.942 8.09e-05 ***
    rho.2 0.20639 0.13408 1.539 0.124
    rho.3 0.50554 0.09325 5.421 5.91e-08 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    > ## maximum pseudo-likelihood
    > fit.mpl <- fitCopula(normal.cop, u, method="mpl")
    > fit.mpl
    fitCopula() estimation based on 'maximum pseudo-likelihood'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.53658 0.08636 6.213 5.18e-10 ***
    rho.2 0.27081 0.13509 2.005 0.045 *
    rho.3 0.55167 0.10307 5.352 8.68e-08 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    The maximized loglikelihood is 19.42
    Optimization converged
    Number of loglikelihood evaluations:
    function gradient
     36 7
    > coef(fit.mpl) # named vector
     rho.1 rho.2 rho.3
    0.5365824 0.2708146 0.5516729
    > str(sf.mpl <- summary(fit.mpl))
    List of 4
     $ method : chr "maximum pseudo-likelihood"
     $ loglik : num 19.4
     $ convergence : int 0
     $ coefficients: num [1:3, 1:4] 0.5366 0.2708 0.5517 0.0864 0.1351 ...
     ..- attr(*, "dimnames")=List of 2
     .. ..$ : chr [1:3] "rho.1" "rho.2" "rho.3"
     .. ..$ : chr [1:4] "Estimate" "Std. Error" "z value" "Pr(>|z|)"
     - attr(*, "class")= chr "summary.fitCopula"
    > coef(sf.mpl)# the matrix, with SE, t-value, ...
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.5365824 0.08635867 6.213417 5.184460e-10
    rho.2 0.2708146 0.13509154 2.004674 4.499786e-02
    rho.3 0.5516729 0.10307005 5.352408 8.679160e-08
    >
    > ## maximum likelihood
    > fit.ml <- fitCopula(normal.cop, x, method="ml")
    Error in chol.default(sigma) :
     the leading minor of order 3 is not positive definite
    Calls: fitCopula ... dCopula -> dCopula -> dmvnorm -> chol -> chol.default
    Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 0.999-8
Check: top-level files
Result: NOTE
    Non-standard files/directories found at top level:
     ‘do-now.org’ ‘feature-table.org’
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Version: 0.999-8
Check: package dependencies
Result: NOTE
    Packages suggested but not available for checking: ‘tseries’ ‘zoo’
Flavor: r-devel-macosx-x86_64-gcc

Version: 0.999-8
Check: running examples for arch 'i386'
Result: ERROR
    Running examples in 'copula-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: fitCopula
    > ### Title: Estimation of the Parameters in Copula Models
    > ### Aliases: fitCopula loglikCopula
    > ### Keywords: models multivariate
    >
    > ### ** Examples
    >
    > gumbel.cop <- gumbelCopula(3, dim=2)
    >
    > (Xtras <- copula:::doExtras())
    [1] FALSE
    > n <- if(Xtras) 200 else 64
    >
    > x <- rCopula(n, gumbel.cop)## "true" observations
    > u <- pobs(x) ## pseudo-observations
    > ## inverting Kendall's tau
    > fit.tau <- fitCopula(gumbel.cop, u, method="itau")
    > fit.tau
    fitCopula() estimation based on 'inversion of Kendall's tau'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 2.6457 0.4692 5.639 1.71e-08 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    > coef(fit.tau)# named vector
     param
    2.645669
    > ## inverting Spearman's rho
    > fit.rho <- fitCopula(gumbel.cop, u, method="irho")
    > fit.rho
    fitCopula() estimation based on 'inversion of Spearman's rho'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 2.5309 0.3979 6.36 2.02e-10 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    > ## maximum pseudo-likelihood
    > fit.mpl <- fitCopula(gumbel.cop, u, method="mpl")
    > fit.mpl
    fitCopula() estimation based on 'maximum pseudo-likelihood'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 2.9086 0.5099 5.704 1.17e-08 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    The maximized loglikelihood is 42.28
    Optimization converged
    Number of loglikelihood evaluations:
    function gradient
     22 5
    > ## maximum likelihood
    > fit.ml <- fitCopula(gumbel.cop, x, method="ml")
    > fit.ml # print()ing works via summary() ...
    fitCopula() estimation based on 'maximum likelihood'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 3.2459 0.3418 9.496 <2e-16 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    The maximized loglikelihood is 46.25
    Optimization converged
    Number of loglikelihood evaluations:
    function gradient
     21 6
    > ## and of that, what's the log likelihood (in two different ways):
    > (ll. <- logLik(fit.ml))
    'log Lik.' 46.24639 (df=1)
    > stopifnot(all.equal(as.numeric(ll.),
    + loglikCopula(coef(fit.ml), x=x, copula=gumbel.cop)))
    >
    > ## a multiparameter example
    > normal.cop <- normalCopula(c(0.6,0.36, 0.6),dim=3,dispstr="un")
    > x <- rCopula(n, normal.cop) ## "true" observations
    > u <- pobs(x) ## pseudo-observations
    > ## inverting Kendall's tau
    > fit.tau <- fitCopula(normal.cop, u, method="itau")
    > fit.tau
    fitCopula() estimation based on 'inversion of Kendall's tau'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.4824 0.1232 3.916 8.99e-05 ***
    rho.2 0.2119 0.1417 1.495 0.135
    rho.3 0.5161 0.0957 5.393 6.92e-08 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    > ## inverting Spearman's rho
    > fit.rho <- fitCopula(normal.cop, u, method="irho")
    > fit.rho
    fitCopula() estimation based on 'inversion of Spearman's rho'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.46185 0.11717 3.942 8.09e-05 ***
    rho.2 0.20639 0.13408 1.539 0.124
    rho.3 0.50554 0.09325 5.421 5.91e-08 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    > ## maximum pseudo-likelihood
    > fit.mpl <- fitCopula(normal.cop, u, method="mpl")
    > fit.mpl
    fitCopula() estimation based on 'maximum pseudo-likelihood'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.53658 0.08636 6.213 5.18e-10 ***
    rho.2 0.27081 0.13509 2.005 0.045 *
    rho.3 0.55167 0.10307 5.352 8.68e-08 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    The maximized loglikelihood is 19.42
    Optimization converged
    Number of loglikelihood evaluations:
    function gradient
     33 7
    > coef(fit.mpl) # named vector
     rho.1 rho.2 rho.3
    0.5365824 0.2708146 0.5516729
    > str(sf.mpl <- summary(fit.mpl))
    List of 4
     $ method : chr "maximum pseudo-likelihood"
     $ loglik : num 19.4
     $ convergence : int 0
     $ coefficients: num [1:3, 1:4] 0.5366 0.2708 0.5517 0.0864 0.1351 ...
     ..- attr(*, "dimnames")=List of 2
     .. ..$ : chr [1:3] "rho.1" "rho.2" "rho.3"
     .. ..$ : chr [1:4] "Estimate" "Std. Error" "z value" "Pr(>|z|)"
     - attr(*, "class")= chr "summary.fitCopula"
    > coef(sf.mpl)# the matrix, with SE, t-value, ...
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.5365824 0.08635867 6.213417 5.184460e-10
    rho.2 0.2708146 0.13509154 2.004674 4.499786e-02
    rho.3 0.5516729 0.10307005 5.352408 8.679160e-08
    >
    > ## maximum likelihood
    > fit.ml <- fitCopula(normal.cop, x, method="ml")
    Error in chol.default(sigma) :
     the leading minor of order 3 is not positive definite
    Calls: fitCopula ... dCopula -> dCopula -> dmvnorm -> chol -> chol.default
    Execution halted
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64

Version: 0.999-8
Check: running examples for arch 'x64'
Result: ERROR
    Running examples in 'copula-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: fitCopula
    > ### Title: Estimation of the Parameters in Copula Models
    > ### Aliases: fitCopula loglikCopula
    > ### Keywords: models multivariate
    >
    > ### ** Examples
    >
    > gumbel.cop <- gumbelCopula(3, dim=2)
    >
    > (Xtras <- copula:::doExtras())
    [1] FALSE
    > n <- if(Xtras) 200 else 64
    >
    > x <- rCopula(n, gumbel.cop)## "true" observations
    > u <- pobs(x) ## pseudo-observations
    > ## inverting Kendall's tau
    > fit.tau <- fitCopula(gumbel.cop, u, method="itau")
    > fit.tau
    fitCopula() estimation based on 'inversion of Kendall's tau'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 2.6457 0.4692 5.639 1.71e-08 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    > coef(fit.tau)# named vector
     param
    2.645669
    > ## inverting Spearman's rho
    > fit.rho <- fitCopula(gumbel.cop, u, method="irho")
    > fit.rho
    fitCopula() estimation based on 'inversion of Spearman's rho'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 2.5309 0.3979 6.36 2.02e-10 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    > ## maximum pseudo-likelihood
    > fit.mpl <- fitCopula(gumbel.cop, u, method="mpl")
    > fit.mpl
    fitCopula() estimation based on 'maximum pseudo-likelihood'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 2.9086 0.5099 5.704 1.17e-08 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    The maximized loglikelihood is 42.28
    Optimization converged
    Number of loglikelihood evaluations:
    function gradient
     22 5
    > ## maximum likelihood
    > fit.ml <- fitCopula(gumbel.cop, x, method="ml")
    > fit.ml # print()ing works via summary() ...
    fitCopula() estimation based on 'maximum likelihood'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 3.2459 0.3418 9.496 <2e-16 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    The maximized loglikelihood is 46.25
    Optimization converged
    Number of loglikelihood evaluations:
    function gradient
     19 6
    > ## and of that, what's the log likelihood (in two different ways):
    > (ll. <- logLik(fit.ml))
    'log Lik.' 46.24639 (df=1)
    > stopifnot(all.equal(as.numeric(ll.),
    + loglikCopula(coef(fit.ml), x=x, copula=gumbel.cop)))
    >
    > ## a multiparameter example
    > normal.cop <- normalCopula(c(0.6,0.36, 0.6),dim=3,dispstr="un")
    > x <- rCopula(n, normal.cop) ## "true" observations
    > u <- pobs(x) ## pseudo-observations
    > ## inverting Kendall's tau
    > fit.tau <- fitCopula(normal.cop, u, method="itau")
    > fit.tau
    fitCopula() estimation based on 'inversion of Kendall's tau'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.4824 0.1232 3.916 8.99e-05 ***
    rho.2 0.2119 0.1417 1.495 0.135
    rho.3 0.5161 0.0957 5.393 6.92e-08 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    > ## inverting Spearman's rho
    > fit.rho <- fitCopula(normal.cop, u, method="irho")
    > fit.rho
    fitCopula() estimation based on 'inversion of Spearman's rho'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.46185 0.11717 3.942 8.09e-05 ***
    rho.2 0.20639 0.13408 1.539 0.124
    rho.3 0.50554 0.09325 5.421 5.91e-08 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    > ## maximum pseudo-likelihood
    > fit.mpl <- fitCopula(normal.cop, u, method="mpl")
    > fit.mpl
    fitCopula() estimation based on 'maximum pseudo-likelihood'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.53658 0.08636 6.213 5.18e-10 ***
    rho.2 0.27081 0.13509 2.005 0.045 *
    rho.3 0.55167 0.10307 5.352 8.68e-08 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    The maximized loglikelihood is 19.42
    Optimization converged
    Number of loglikelihood evaluations:
    function gradient
     36 7
    > coef(fit.mpl) # named vector
     rho.1 rho.2 rho.3
    0.5365824 0.2708146 0.5516729
    > str(sf.mpl <- summary(fit.mpl))
    List of 4
     $ method : chr "maximum pseudo-likelihood"
     $ loglik : num 19.4
     $ convergence : int 0
     $ coefficients: num [1:3, 1:4] 0.5366 0.2708 0.5517 0.0864 0.1351 ...
     ..- attr(*, "dimnames")=List of 2
     .. ..$ : chr [1:3] "rho.1" "rho.2" "rho.3"
     .. ..$ : chr [1:4] "Estimate" "Std. Error" "z value" "Pr(>|z|)"
     - attr(*, "class")= chr "summary.fitCopula"
    > coef(sf.mpl)# the matrix, with SE, t-value, ...
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.5365824 0.08635867 6.213417 5.184460e-10
    rho.2 0.2708146 0.13509154 2.004674 4.499786e-02
    rho.3 0.5516729 0.10307005 5.352408 8.679160e-08
    >
    > ## maximum likelihood
    > fit.ml <- fitCopula(normal.cop, x, method="ml")
    Error in chol.default(sigma) :
     the leading minor of order 3 is not positive definite
    Calls: fitCopula ... dCopula -> dCopula -> dmvnorm -> chol -> chol.default
    Execution halted
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64

Version: 0.999-8
Check: examples
Result: ERROR
    Running examples in ‘copula-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: fitCopula
    > ### Title: Estimation of the Parameters in Copula Models
    > ### Aliases: fitCopula loglikCopula
    > ### Keywords: models multivariate
    >
    > ### ** Examples
    >
    > gumbel.cop <- gumbelCopula(3, dim=2)
    >
    > (Xtras <- copula:::doExtras())
    [1] FALSE
    > n <- if(Xtras) 200 else 64
    >
    > x <- rCopula(n, gumbel.cop)## "true" observations
    > u <- pobs(x) ## pseudo-observations
    > ## inverting Kendall's tau
    > fit.tau <- fitCopula(gumbel.cop, u, method="itau")
    > fit.tau
    fitCopula() estimation based on 'inversion of Kendall's tau'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 2.6457 0.4692 5.639 1.71e-08 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    > coef(fit.tau)# named vector
     param
    2.645669
    > ## inverting Spearman's rho
    > fit.rho <- fitCopula(gumbel.cop, u, method="irho")
    > fit.rho
    fitCopula() estimation based on 'inversion of Spearman's rho'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 2.5309 0.3979 6.36 2.02e-10 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    > ## maximum pseudo-likelihood
    > fit.mpl <- fitCopula(gumbel.cop, u, method="mpl")
    > fit.mpl
    fitCopula() estimation based on 'maximum pseudo-likelihood'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 2.9086 0.5099 5.704 1.17e-08 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    The maximized loglikelihood is 42.28
    Optimization converged
    Number of loglikelihood evaluations:
    function gradient
     22 5
    > ## maximum likelihood
    > fit.ml <- fitCopula(gumbel.cop, x, method="ml")
    > fit.ml # print()ing works via summary() ...
    fitCopula() estimation based on 'maximum likelihood'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    param 3.2459 0.3418 9.496 <2e-16 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    The maximized loglikelihood is 46.25
    Optimization converged
    Number of loglikelihood evaluations:
    function gradient
     19 6
    > ## and of that, what's the log likelihood (in two different ways):
    > (ll. <- logLik(fit.ml))
    'log Lik.' 46.24639 (df=1)
    > stopifnot(all.equal(as.numeric(ll.),
    + loglikCopula(coef(fit.ml), x=x, copula=gumbel.cop)))
    >
    > ## a multiparameter example
    > normal.cop <- normalCopula(c(0.6,0.36, 0.6),dim=3,dispstr="un")
    > x <- rCopula(n, normal.cop) ## "true" observations
    > u <- pobs(x) ## pseudo-observations
    > ## inverting Kendall's tau
    > fit.tau <- fitCopula(normal.cop, u, method="itau")
    > fit.tau
    fitCopula() estimation based on 'inversion of Kendall's tau'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.4824 0.1232 3.916 8.99e-05 ***
    rho.2 0.2119 0.1417 1.495 0.135
    rho.3 0.5161 0.0957 5.393 6.92e-08 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    > ## inverting Spearman's rho
    > fit.rho <- fitCopula(normal.cop, u, method="irho")
    > fit.rho
    fitCopula() estimation based on 'inversion of Spearman's rho'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.46185 0.11717 3.942 8.09e-05 ***
    rho.2 0.20639 0.13408 1.539 0.124
    rho.3 0.50554 0.09325 5.421 5.91e-08 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    > ## maximum pseudo-likelihood
    > fit.mpl <- fitCopula(normal.cop, u, method="mpl")
    > fit.mpl
    fitCopula() estimation based on 'maximum pseudo-likelihood'
    and a sample of size 64.
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.53658 0.08636 6.213 5.18e-10 ***
    rho.2 0.27081 0.13509 2.005 0.045 *
    rho.3 0.55167 0.10307 5.352 8.68e-08 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    The maximized loglikelihood is 19.42
    Optimization converged
    Number of loglikelihood evaluations:
    function gradient
     31 7
    > coef(fit.mpl) # named vector
     rho.1 rho.2 rho.3
    0.5365824 0.2708146 0.5516729
    > str(sf.mpl <- summary(fit.mpl))
    List of 4
     $ method : chr "maximum pseudo-likelihood"
     $ loglik : num 19.4
     $ convergence : int 0
     $ coefficients: num [1:3, 1:4] 0.5366 0.2708 0.5517 0.0864 0.1351 ...
     ..- attr(*, "dimnames")=List of 2
     .. ..$ : chr [1:3] "rho.1" "rho.2" "rho.3"
     .. ..$ : chr [1:4] "Estimate" "Std. Error" "z value" "Pr(>|z|)"
     - attr(*, "class")= chr "summary.fitCopula"
    > coef(sf.mpl)# the matrix, with SE, t-value, ...
     Estimate Std. Error z value Pr(>|z|)
    rho.1 0.5365824 0.08635867 6.213417 5.184460e-10
    rho.2 0.2708146 0.13509154 2.004674 4.499786e-02
    rho.3 0.5516729 0.10307005 5.352408 8.679160e-08
    >
    > ## maximum likelihood
    > fit.ml <- fitCopula(normal.cop, x, method="ml")
    Error in chol.default(sigma) :
     the leading minor of order 3 is not positive definite
    Calls: fitCopula ... dCopula -> dCopula -> dmvnorm -> chol -> chol.default
    Execution halted
Flavor: r-release-linux-ix86