CRAN Package Check Results for Package tergm

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

Flavor Version Tinstall Tcheck Ttotal 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

Check Details

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