CRAN Package Check Results for Package CompRandFld

Last updated on 2018-05-21 04:48:59 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0.3-4 12.55 39.48 52.03 ERROR
r-devel-linux-x86_64-debian-gcc 1.0.3-4 11.16 35.50 46.66 ERROR
r-devel-linux-x86_64-fedora-clang 1.0.3-4 88.90 WARN
r-devel-linux-x86_64-fedora-gcc 1.0.3-4 87.22 NOTE
r-devel-windows-ix86+x86_64 1.0.3-4 44.00 99.00 143.00 NOTE
r-patched-linux-x86_64 1.0.3-4 13.54 45.19 58.73 ERROR
r-patched-solaris-x86 1.0.3-4 132.40 NOTE
r-release-linux-x86_64 1.0.3-4 14.67 45.41 60.08 ERROR
r-release-windows-ix86+x86_64 1.0.3-4 29.00 91.00 120.00 NOTE
r-release-osx-x86_64 1.0.3-4 WARN
r-oldrel-windows-ix86+x86_64 1.0.3-4 25.00 95.00 120.00 NOTE
r-oldrel-osx-x86_64 1.0.3-4 NOTE

Check Details

Version: 1.0.3-4
Check: whether package can be installed
Result: WARN
    Found the following significant warnings:
     Utility.c:162:8: warning: explicitly assigning value of variable of type 'int' to itself [-Wself-assign]
     Utility.c:187:8: warning: explicitly assigning value of variable of type 'int' to itself [-Wself-assign]
     Utility.c:211:8: warning: explicitly assigning value of variable of type 'int' to itself [-Wself-assign]
     Utility.c:392:8: warning: explicitly assigning value of variable of type 'int' to itself [-Wself-assign]
     Utility.c:417:8: warning: explicitly assigning value of variable of type 'int' to itself [-Wself-assign]
     Utility.c:442:8: warning: explicitly assigning value of variable of type 'int' to itself [-Wself-assign]
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-fedora-clang, r-release-osx-x86_64

Version: 1.0.3-4
Check: R code for possible problems
Result: NOTE
    CompLikelihood: no visible global function definition for ‘optim’
    Covariogram: no visible global function definition for ‘uniroot’
    Covariogram: no visible global function definition for ‘pnorm’
    Covariogram: no visible global function definition for ‘par’
    Covariogram: no visible global function definition for ‘persp’
    Covariogram: no visible global function definition for ‘plot’
    Covariogram: no visible global function definition for ‘dev.cur’
    Covariogram: no visible global function definition for ‘lines’
    Covariogram: no visible global function definition for ‘abline’
    Covariogram: no visible global function definition for ‘points’
    Covmatrix : Cmatrix: no visible global function definition for ‘new’
    FitGev: no visible global function definition for ‘optim’
    HypoTest: no visible global function definition for ‘pchisq’
    InitParam: no visible global function definition for ‘var’
    InitParam: no visible global function definition for ‘qnorm’
    Kri: no visible global function definition for ‘window’
    Likelihood : LogNormDenTap: no visible global function definition for
     ‘new’
    Likelihood : LogNormDenTap: no visible global function definition for
     ‘slot<-’
    Likelihood: no visible global function definition for ‘optim’
    Likelihood: no visible global function definition for ‘new’
    Likelihood: no visible global function definition for ‘slot<-’
    MomEst: no visible global function definition for ‘var’
    RFsim : CholRFsim: no visible global function definition for ‘rnorm’
    WLeastSquare: no visible global function definition for ‘optim’
    WlsInit: no visible global function definition for ‘optim’
    Undefined global functions or variables:
     abline dev.cur lines new optim par pchisq persp plot pnorm points
     qnorm rnorm slot<- uniroot var window
    Consider adding
     importFrom("grDevices", "dev.cur")
     importFrom("graphics", "abline", "lines", "par", "persp", "plot",
     "points")
     importFrom("methods", "new", "slot<-")
     importFrom("stats", "optim", "pchisq", "pnorm", "qnorm", "rnorm",
     "uniroot", "var", "window")
    to your NAMESPACE file (and ensure that your DESCRIPTION Imports field
    contains 'methods').
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64

Version: 1.0.3-4
Check: compiled code
Result: NOTE
    File ‘CompRandFld/libs/CompRandFld.so’:
     Found no calls to: ‘R_registerRoutines’, ‘R_useDynamicSymbols’
    
    It is good practice to register native routines and to disable symbol
    search.
    
    See ‘Writing portable packages’ in the ‘Writing R Extensions’ manual.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.0.3-4
Check: examples
Result: ERROR
    Running examples in ‘CompRandFld-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: FitComposite
    > ### Title: Max-Likelihood-Based Fitting of Gaussian, Binary and Max-Stable
    > ### Fields
    > ### Aliases: FitComposite print.FitComposite
    > ### Keywords: Composite
    >
    > ### ** Examples
    >
    > library(CompRandFld)
    > library(RandomFields)
    Loading required package: sp
    Loading required package: RandomFieldsUtils
    
    Attaching package: ‘RandomFields’
    
    The following object is masked from ‘package:RandomFieldsUtils’:
    
     RFoptions
    
    The following objects are masked from ‘package:base’:
    
     abs, acosh, asin, asinh, atan, atan2, atanh, cos, cosh, exp, expm1,
     floor, gamma, lgamma, log, log1p, log2, logb, max, min, round, sin,
     sinh, sqrt, tan, tanh, trunc
    
    > library(spam)
    Loading required package: dotCall64
    Loading required package: grid
    Spam version 2.1-4 (2018-04-12) is loaded.
    Type 'help( Spam)' or 'demo( spam)' for a short introduction
    and overview of this package.
    Help for individual functions is also obtained by adding the
    suffix '.spam' to the function name, e.g. 'help( chol.spam)'.
    
    Attaching package: ‘spam’
    
    The following objects are masked from ‘package:base’:
    
     backsolve, forwardsolve
    
    > set.seed(3132)
    >
    > ###############################################################
    > ############ Examples of spatial random fields ################
    > ###############################################################
    >
    > # Define the spatial-coordinates of the points:
    > x <- runif(100, 0, 10)
    > y <- runif(100, 0, 10)
    >
    > # Set the covariance model's parameters:
    > corrmodel <- "exponential"
    > mean <- 0
    > sill <- 1
    > nugget <- 0
    > scale <- 1.5
    > param<-list(mean=mean,sill=sill,nugget=nugget,scale=scale)
    > coords<-cbind(x,y)
    > # Simulation of the spatial Gaussian random field:
    > data <- RFsim(coordx=coords, corrmodel=corrmodel, param=param)$data
    >
    > # Fixed parameters
    > fixed<-list(mean=mean,nugget=nugget)
    >
    > # Starting value for the estimated parameters
    > start<-list(scale=scale,sill=sill)
    >
    >
    > ################################################################
    > ###
    > ### Example 1. Maximum likelihood fitting of
    > ### Gaussian random fields with exponential correlation.
    > ### One spatial replication.
    > ### Likelihood setting: composite with
    > ### marginal pairwise likelihood objects.
    > ###
    > ###############################################################
    >
    >
    > # Maximum composite-likelihood fitting of the random field:
    > fit <- FitComposite(data, coordx=coords, corrmodel=corrmodel, maxdist=2,
    + likelihood="Marginal",type="Pairwise",varest=TRUE,
    + start=start,fixed=fixed)
    >
    > # Results:
    > print(fit)
    
    ##################################################################
    Maximum Composite-Likelihood Fitting of Gaussian Random Fields
    
    Setting: Marginal Composite-Likelihood
    
    Model associated to the likelihood objects: Gaussian
    
    Type of the likelihood objects: Pairwise
    
    Covariance model: exponential
    Number of spatial coordinates: 100
    Number of dependent temporal realisations: 1
    Number of replicates of the random field: 1
    Number of estimated parameters: 2
    
    Maximum log-Composite-Likelihood value: -1677.49
    CLIC : 3385
    
    Estimated parameters:
    scale sill
    1.761 1.366
    
    Standard errors:
     scale sill
    0.4213 0.2273
    
    Variance-covariance matrix of the estimates:
     scale sill
    scale 0.1775 0.08610
    sill 0.0861 0.05165
    
    ##################################################################
    >
    > ################################################################
    > ###
    > ### Example 2. Maximum likelihood fitting of
    > ### Gaussian random fields with exponential correlation.
    > ### One spatial replication.
    > ### Likelihood setting: standard full likelihood.
    > ###
    > ###############################################################
    >
    > # Maximum composite-likelihood fitting of the random field:
    > fit <- FitComposite(data, coordx=coords, corrmodel=corrmodel,likelihood="Full",
    + type="Standard",varest=TRUE,start=start,fixed=fixed)
    > # Results:
    > print(fit)
    
    ##################################################################
    Maximum Likelihood Fitting of Gaussian Random Fields
    
    Setting: Full Likelihood
    
    Model associated to the likelihood objects: Gaussian
    
    Type of the likelihood objects: Standard
    
    Covariance model: exponential
    Number of spatial coordinates: 100
    Number of dependent temporal realisations: 1
    Number of replicates of the random field: 1
    Number of estimated parameters: 2
    
    Maximum log-Likelihood value: -119.50
    AIC : 243
    
    Estimated parameters:
    scale sill
    1.161 1.112
    
    Standard errors:
     scale sill
    0.3679 0.2511
    
    Variance-covariance matrix of the estimates:
     scale sill
    scale 0.13536 0.07201
    sill 0.07201 0.06305
    
    ##################################################################
    >
    > ################################################################
    > ###
    > ### Example 3. Maximum likelihood fitting of
    > ### Gaussian random fields with exponetial correlation.
    > ### One spatial replication.
    > ### Likelihood setting: tapered full likelihood.
    > ###
    > ###############################################################
    >
    > # Maximum tapered likelihood fitting of the random field:
    > fit <- FitComposite(data, coordx=coords, corrmodel=corrmodel,likelihood="Full",
    + type="Tapering",taper="Wendland1",maxdist=1.5,
    + varest=TRUE,start=start,fixed=fixed)
    Error in UseMethod("determinant") :
     no applicable method for 'determinant' applied to an object of class "spam.chol.NgPeyton"
    Calls: FitComposite ... optim -> <Anonymous> -> fn -> apply -> FUN -> determinant
    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