Last updated on 2018-02-25 19:49:22 CET.

Flavor | Version | T_{install} | T_{check} | T_{total} | Status | Flags |
---|---|---|---|---|---|---|

r-devel-linux-x86_64-debian-clang | 3.4.1 | 12.37 | 632.57 | 644.94 | OK | |

r-devel-linux-x86_64-debian-gcc | 3.4.1 | 11.07 | 443.50 | 454.57 | OK | |

r-devel-linux-x86_64-fedora-clang | 3.4.1 | 655.55 | OK | |||

r-devel-linux-x86_64-fedora-gcc | 3.4.1 | 888.88 | OK | |||

r-devel-windows-ix86+x86_64 | 3.4.1 | 39.00 | 396.00 | 435.00 | OK | --no-vignettes |

r-patched-linux-x86_64 | 3.4.1 | 9.10 | 865.82 | 874.92 | WARN | |

r-patched-solaris-x86 | 3.4.1 | 1465.40 | OK | |||

r-release-linux-x86_64 | 3.4.1 | 9.40 | 593.68 | 603.08 | OK | |

r-release-windows-ix86+x86_64 | 3.4.1 | 38.00 | 511.00 | 549.00 | OK | --no-vignettes |

r-release-osx-x86_64 | 3.4.1 | OK | ||||

r-oldrel-windows-ix86+x86_64 | 3.4.1 | 25.00 | 483.00 | 508.00 | OK | --no-vignettes |

r-oldrel-osx-x86_64 | 3.4.1 | OK |

Version: 3.4.1

Check: re-building of vignette outputs

Result: WARN

Error in re-building vignettes:

...

Loading required package: statnet.common

Attaching package: ‘statnet.common’

The following object is masked from ‘package:base’:

order

Loading required package: ergm

Loading required package: network

network: Classes for Relational Data

Version 1.13.0 created on 2015-08-31.

copyright (c) 2005, Carter T. Butts, University of California-Irvine

Mark S. Handcock, University of California -- Los Angeles

David R. Hunter, Penn State University

Martina Morris, University of Washington

Skye Bender-deMoll, University of Washington

For citation information, type citation("network").

Type help("network-package") to get started.

ergm: version 3.8.0, created on 2017-08-18

Copyright (c) 2017, Mark S. Handcock, University of California -- Los Angeles

David R. Hunter, Penn State University

Carter T. Butts, University of California -- Irvine

Steven M. Goodreau, University of Washington

Pavel N. Krivitsky, University of Wollongong

Martina Morris, University of Washington

with contributions from

Li Wang

Kirk Li, University of Washington

Skye Bender-deMoll, University of Washington

Based on "statnet" project software (statnet.org).

For license and citation information see statnet.org/attribution

or type citation("ergm").

NOTE: Versions before 3.6.1 had a bug in the

implementation of the bd() constriant which distorted

the sampled distribution somewhat. In addition,

Sampson's Monks datasets had mislabeled vertices. See

the NEWS and the documentation for more details.

Loading required package: networkDynamic

networkDynamic: version 0.9.0, created on 2016-01-12

Copyright (c) 2016, Carter T. Butts, University of California -- Irvine

Ayn Leslie-Cook, University of Washington

Pavel N. Krivitsky, University of Wollongong

Skye Bender-deMoll, University of Washington

with contributions from

Zack Almquist, University of California -- Irvine

David R. Hunter, Penn State University

Li Wang

Kirk Li, University of Washington

Steven M. Goodreau, University of Washington

Jeffrey Horner

Martina Morris, University of Washington

Based on "statnet" project software (statnet.org).

For license and citation information see statnet.org/attribution

or type citation("networkDynamic").

tergm: version 3.4.1, created on 2017-09-12

Copyright (c) 2017, Pavel N. Krivitsky, University of Wollongong

Mark S. Handcock, University of California -- Los Angeles

with contributions from

David R. Hunter, Penn State University

Steven M. Goodreau, University of Washington

Martina Morris, University of Washington

Nicole Bohme Carnegie, New York University

Carter T. Butts, University of California -- Irvine

Ayn Leslie-Cook, University of Washington

Skye Bender-deMoll

Li Wang

Kirk Li, University of Washington

Based on "statnet" project software (statnet.org).

For license and citation information see statnet.org/attribution

or type citation("tergm").

Starting maximum likelihood estimation via MCMLE:

Iteration 1 of at most 20:

Optimizing with step length 1.

The log-likelihood improved by 0.2685.

Step length converged once. Increasing MCMC sample size.

Iteration 2 of at most 20:

Optimizing with step length 1.

The log-likelihood improved by 0.005925.

Step length converged twice. Stopping.

Evaluating log-likelihood at the estimate. Using 20 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 .

This model was fit using MCMC. To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.

Starting maximum likelihood estimation via MCMLE:

Iteration 1 of at most 20:

Optimizing with step length 1.

The log-likelihood improved by 0.2595.

Step length converged once. Increasing MCMC sample size.

Iteration 2 of at most 20:

Optimizing with step length 1.

The log-likelihood improved by 0.0009669.

Step length converged twice. Stopping.

This model was fit using MCMC. To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.

Warning in formals(fun) : argument is not a function

Warning in stergm.CMLE(nw, formation, dissolution, constraints, times, offset.coef.form, :

'times' argument was not provided to specify sampling time points for a list. Modeling transition between successive networks jointly. This behavior may change in the future.

Starting maximum likelihood estimation via MCMLE:

Iteration 1 of at most 20:

Optimizing with step length 1.

The log-likelihood improved by 0.3683.

Step length converged once. Increasing MCMC sample size.

Iteration 2 of at most 20:

Optimizing with step length 1.

The log-likelihood improved by 0.01588.

Step length converged twice. Stopping.

Evaluating log-likelihood at the estimate. Using 20 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 .

This model was fit using MCMC. To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.

Starting maximum likelihood estimation via MCMLE:

Iteration 1 of at most 20:

Optimizing with step length 1.

The log-likelihood improved by 0.3339.

Step length converged once. Increasing MCMC sample size.

Iteration 2 of at most 20:

Optimizing with step length 1.

The log-likelihood improved by 0.002221.

Step length converged twice. Stopping.

Evaluating log-likelihood at the estimate. Using 20 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 .

This model was fit using MCMC. To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.

Starting maximum likelihood estimation via MCMLE:

Iteration 1 of at most 20:

Optimizing with step length 1.

The log-likelihood improved by 0.4482.

Step length converged once. Increasing MCMC sample size.

Iteration 2 of at most 20:

Optimizing with step length 1.

The log-likelihood improved by 0.007716.

Step length converged twice. Stopping.

This model was fit using MCMC. To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.

Killed

Flavor: r-patched-linux-x86_64