CRAN Package Check Results for Package sampleSelection

Last updated on 2021-09-23 04:56:45 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.2-12 18.66 409.05 427.71 OK
r-devel-linux-x86_64-debian-gcc 1.2-12 14.27 294.07 308.34 OK
r-devel-linux-x86_64-fedora-clang 1.2-12 487.72 OK
r-devel-linux-x86_64-fedora-gcc 1.2-12 481.89 OK
r-devel-windows-x86_64 1.2-12 20.00 363.00 383.00 OK
r-devel-windows-x86_64-gcc10-UCRT 1.2-12 ERROR
r-patched-linux-x86_64 1.2-12 17.27 387.00 404.27 OK
r-patched-solaris-x86 1.2-12 628.50 OK
r-release-linux-x86_64 1.2-12 15.49 388.06 403.55 OK
r-release-macos-arm64 1.2-12 OK
r-release-macos-x86_64 1.2-12 OK
r-release-windows-ix86+x86_64 1.2-12 35.00 362.00 397.00 OK
r-oldrel-macos-x86_64 1.2-12 OK
r-oldrel-windows-ix86+x86_64 1.2-12 23.00 353.00 376.00 OK

Check Details

Version: 1.2-12
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: 'Ecdat'
Flavor: r-devel-windows-x86_64-gcc10-UCRT

Version: 1.2-12
Check: examples
Result: ERROR
    Running examples in 'sampleSelection-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: selection
    > ### Title: Heckman-style selection and treatment effect models
    > ### Aliases: selection heckit treatReg
    > ### Keywords: models regression
    >
    > ### ** Examples
    >
    > ## Greene( 2003 ): example 22.8, page 786
    > data( Mroz87 )
    > Mroz87$kids <- ( Mroz87$kids5 + Mroz87$kids618 > 0 )
    > # Two-step estimation
    > summary( heckit( lfp ~ age + I( age^2 ) + faminc + kids + educ,
    + wage ~ exper + I( exper^2 ) + educ + city, Mroz87 ) )
    --------------------------------------------
    Tobit 2 model (sample selection model)
    2-step Heckman / heckit estimation
    753 observations (325 censored and 428 observed)
    14 free parameters (df = 740)
    Probit selection equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -4.157e+00 1.402e+00 -2.965 0.003127 **
    age 1.854e-01 6.597e-02 2.810 0.005078 **
    I(age^2) -2.426e-03 7.735e-04 -3.136 0.001780 **
    faminc 4.580e-06 4.206e-06 1.089 0.276544
    kidsTRUE -4.490e-01 1.309e-01 -3.430 0.000638 ***
    educ 9.818e-02 2.298e-02 4.272 2.19e-05 ***
    Outcome equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -0.9712003 2.0593505 -0.472 0.637
    exper 0.0210610 0.0624646 0.337 0.736
    I(exper^2) 0.0001371 0.0018782 0.073 0.942
    educ 0.4170174 0.1002497 4.160 3.56e-05 ***
    city 0.4438379 0.3158984 1.405 0.160
    Multiple R-Squared:0.1264, Adjusted R-Squared:0.116
     Error terms:
     Estimate Std. Error t value Pr(>|t|)
    invMillsRatio -1.098 1.266 -0.867 0.386
    sigma 3.200 NA NA NA
    rho -0.343 NA NA NA
    --------------------------------------------
    > # ML estimation
    > summary( selection( lfp ~ age + I( age^2 ) + faminc + kids + educ,
    + wage ~ exper + I( exper^2 ) + educ + city, Mroz87 ) )
    --------------------------------------------
    Tobit 2 model (sample selection model)
    Maximum Likelihood estimation
    Newton-Raphson maximisation, 5 iterations
    Return code 8: successive function values within relative tolerance limit (reltol)
    Log-Likelihood: -1581.258
    753 observations (325 censored and 428 observed)
    13 free parameters (df = 740)
    Probit selection equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -4.120e+00 1.401e+00 -2.942 0.003368 **
    age 1.840e-01 6.587e-02 2.794 0.005345 **
    I(age^2) -2.409e-03 7.723e-04 -3.119 0.001886 **
    faminc 5.680e-06 4.416e-06 1.286 0.198782
    kidsTRUE -4.506e-01 1.302e-01 -3.461 0.000568 ***
    educ 9.528e-02 2.315e-02 4.115 4.3e-05 ***
    Outcome equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -1.9630242 1.1982209 -1.638 0.102
    exper 0.0278683 0.0615514 0.453 0.651
    I(exper^2) -0.0001039 0.0018388 -0.056 0.955
    educ 0.4570051 0.0732299 6.241 7.33e-10 ***
    city 0.4465290 0.3159209 1.413 0.158
     Error terms:
     Estimate Std. Error t value Pr(>|t|)
    sigma 3.1084 0.1138 27.307 <2e-16 ***
    rho -0.1320 0.1651 -0.799 0.424
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    --------------------------------------------
    >
    > ## Example using binary outcome for selection model.
    > ## We estimate the probability of womens' education on their
    > ## chances to get high wage (> $5/hr in 1975 USD), using PSID data
    > ## We use education as explanatory variable
    > ## and add age, kids, and non-work income as exclusion restrictions.
    > data(Mroz87)
    > m <- selection(lfp ~ educ + age + kids5 + kids618 + nwifeinc,
    + wage >= 5 ~ educ, data = Mroz87 )
    > summary(m)
    --------------------------------------------
    Tobit 2 model with binary outcome (sample selection model)
    Maximum Likelihood estimation
    BHHH maximisation, 8 iterations
    Return code 8: successive function values within relative tolerance limit (reltol)
    Log-Likelihood: -653.2037
    753 observations (325 censored and 428 observed)
    9 free parameters (df = 744)
    Probit selection equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) 0.430362 0.475966 0.904 0.366
    educ 0.156223 0.023811 6.561 1.00e-10 ***
    age -0.034713 0.007649 -4.538 6.61e-06 ***
    kids5 -0.890560 0.112663 -7.905 9.69e-15 ***
    kids618 -0.038167 0.039320 -0.971 0.332
    nwifeinc -0.020948 0.004318 -4.851 1.49e-06 ***
    Outcome equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -4.5213 0.5611 -8.058 3.08e-15 ***
    educ 0.2879 0.0369 7.800 2.09e-14 ***
     Error terms:
     Estimate Std. Error t value Pr(>|t|)
    rho 0.1164 0.2706 0.43 0.667
    --------------------------------------------
    >
    >
    > ## example using random numbers
    > library( "mvtnorm" )
    > nObs <- 1000
    > sigma <- matrix( c( 1, -0.7, -0.7, 1 ), ncol = 2 )
    > errorTerms <- rmvnorm( nObs, c( 0, 0 ), sigma )
    > myData <- data.frame( no = c( 1:nObs ), x1 = rnorm( nObs ), x2 = rnorm( nObs ),
    + u1 = errorTerms[ , 1 ], u2 = errorTerms[ , 2 ] )
    > myData$y <- 2 + myData$x1 + myData$u1
    > myData$s <- ( 2 * myData$x1 + myData$x2 + myData$u2 - 0.2 ) > 0
    > myData$y[ !myData$s ] <- NA
    > myOls <- lm( y ~ x1, data = myData)
    > summary( myOls )
    
    Call:
    lm(formula = y ~ x1, data = myData)
    
    Residuals:
     Min 1Q Median 3Q Max
    -3.1670 -0.6422 -0.0176 0.6851 3.1186
    
    Coefficients:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) 1.53676 0.06236 24.64 <2e-16 ***
    x1 1.26069 0.05891 21.40 <2e-16 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    Residual standard error: 0.9922 on 472 degrees of freedom
     (526 observations deleted due to missingness)
    Multiple R-squared: 0.4925, Adjusted R-squared: 0.4914
    F-statistic: 458 on 1 and 472 DF, p-value: < 2.2e-16
    
    > myHeckit <- heckit( s ~ x1 + x2, y ~ x1, myData, print.level = 1 )
    Tobit 2 model
    > summary( myHeckit )
    --------------------------------------------
    Tobit 2 model (sample selection model)
    2-step Heckman / heckit estimation
    1000 observations (526 censored and 474 observed)
    8 free parameters (df = 993)
    Probit selection equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -0.22904 0.06106 -3.751 0.000186 ***
    x1 1.97384 0.12229 16.141 < 2e-16 ***
    x2 1.01003 0.07930 12.737 < 2e-16 ***
    Outcome equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) 2.12355 0.10883 19.51 <2e-16 ***
    x1 0.89186 0.08257 10.80 <2e-16 ***
    Multiple R-Squared:0.5404, Adjusted R-Squared:0.5385
     Error terms:
     Estimate Std. Error t value Pr(>|t|)
    invMillsRatio -0.9622 0.1318 -7.298 5.99e-13 ***
    sigma 1.0750 NA NA NA
    rho -0.8950 NA NA NA
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    --------------------------------------------
    >
    > ## example using random numbers with IV/2SLS estimation
    > library( "mvtnorm" )
    > nObs <- 1000
    > sigma <- matrix( c( 1, 0.5, 0.1, 0.5, 1, -0.3, 0.1, -0.3, 1 ), ncol = 3 )
    > errorTerms <- rmvnorm( nObs, c( 0, 0, 0 ), sigma )
    > myData <- data.frame( no = c( 1:nObs ), x1 = rnorm( nObs ), x2 = rnorm( nObs ),
    + u1 = errorTerms[ , 1 ], u2 = errorTerms[ , 2 ], u3 = errorTerms[ , 3 ] )
    > myData$w <- 1 + myData$x1 + myData$u1
    > myData$y <- 2 + myData$w + myData$u2
    > myData$s <- ( 2 * myData$x1 + myData$x2 + myData$u3 - 0.2 ) > 0
    > myData$y[ !myData$s ] <- NA
    > myHeckit <- heckit( s ~ x1 + x2, y ~ w, data = myData )
    > summary( myHeckit ) # biased!
    --------------------------------------------
    Tobit 2 model (sample selection model)
    2-step Heckman / heckit estimation
    1000 observations (527 censored and 473 observed)
    8 free parameters (df = 993)
    Probit selection equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -0.15979 0.05860 -2.727 0.00651 **
    x1 1.86891 0.11381 16.421 < 2e-16 ***
    x2 0.98799 0.07971 12.394 < 2e-16 ***
    Outcome equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) 1.08092 0.09163 11.80 <2e-16 ***
    w 1.45660 0.03713 39.23 <2e-16 ***
    Multiple R-Squared:0.7841, Adjusted R-Squared:0.7832
     Error terms:
     Estimate Std. Error t value Pr(>|t|)
    invMillsRatio 0.1829 0.1013 1.806 0.0713 .
    sigma 0.9215 NA NA NA
    rho 0.1984 NA NA NA
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    --------------------------------------------
    > myHeckitIv <- heckit( s ~ x1 + x2, y ~ w, data = myData, inst = ~ x1 )
    > summary( myHeckitIv ) # unbiased
    --------------------------------------------
    Tobit 2 model (sample selection model)
    2-step Heckman / heckit estimation
    1000 observations (527 censored and 473 observed)
    8 free parameters (df = 993)
    Probit selection equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -0.15979 0.05860 -2.727 0.00651 **
    x1 1.86891 0.11381 16.421 < 2e-16 ***
    x2 0.98799 0.07971 12.394 < 2e-16 ***
    Outcome equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) 2.0345 0.1067 19.07 <2e-16 ***
    w 0.9785 0.0432 22.65 <2e-16 ***
    Multiple R-Squared:0.7083, Adjusted R-Squared:0.707
     Error terms:
     Estimate Std. Error t value Pr(>|t|)
    invMillsRatio -0.2891 0.1175 -2.46 0.0141 *
    sigma 1.0767 NA NA NA
    rho -0.2685 NA NA NA
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    --------------------------------------------
    >
    > ## tobit-5 example
    > N <- 500
    > library(mvtnorm)
    > vc <- diag(3)
    > vc[lower.tri(vc)] <- c(0.9, 0.5, 0.6)
    > vc[upper.tri(vc)] <- vc[lower.tri(vc)]
    > eps <- rmvnorm(N, rep(0, 3), vc)
    > xs <- runif(N)
    > ys <- xs + eps[,1] > 0
    > xo1 <- runif(N)
    > yo1 <- xo1 + eps[,2]
    > xo2 <- runif(N)
    > yo2 <- xo2 + eps[,3]
    > a <- selection(ys~xs, list(yo1 ~ xo1, yo2 ~ xo2))
    > summary(a)
    --------------------------------------------
    Tobit 5 model (switching regression model)
    Maximum Likelihood estimation
    Newton-Raphson maximisation, 5 iterations
    Return code 1: gradient close to zero (gradtol)
    Log-Likelihood: -916.9684
    500 observations: 157 selection 1 (FALSE) and 343 selection 2 (TRUE)
    10 free parameters (df = 490)
    Probit selection equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -0.0534 0.1065 -0.501 0.616
    xs 1.1250 0.1842 6.106 2.08e-09 ***
    Outcome equation 1:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) 0.07615 0.18201 0.418 0.676
    xo1 0.92591 0.17272 5.361 1.28e-07 ***
    Outcome equation 2:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -0.01079 0.17850 -0.060 0.952
    xo2 1.09098 0.17267 6.318 5.95e-10 ***
     Error terms:
     Estimate Std. Error t value Pr(>|t|)
    sigma1 0.97959 0.10528 9.305 <2e-16 ***
    sigma2 0.97087 0.06317 15.370 <2e-16 ***
    rho1 0.88349 0.05759 15.341 <2e-16 ***
    rho2 0.34270 0.30009 1.142 0.254
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    --------------------------------------------
    >
    > ## tobit2 example
    > vc <- diag(2)
    > vc[2,1] <- vc[1,2] <- -0.7
    > eps <- rmvnorm(N, rep(0, 2), vc)
    > xs <- runif(N)
    > ys <- xs + eps[,1] > 0
    > xo <- runif(N)
    > yo <- (xo + eps[,2])*(ys > 0)
    > a <- selection(ys~xs, yo ~xo)
    > summary(a)
    --------------------------------------------
    Tobit 2 model (sample selection model)
    Maximum Likelihood estimation
    Newton-Raphson maximisation, 3 iterations
    Return code 8: successive function values within relative tolerance limit (reltol)
    Log-Likelihood: -725.3648
    500 observations (160 censored and 340 observed)
    6 free parameters (df = 494)
    Probit selection equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) 0.1222 0.1095 1.115 0.265198
    xs 0.7149 0.1955 3.657 0.000283 ***
    Outcome equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -0.01516 0.15424 -0.098 0.922
    xo 0.87447 0.15769 5.545 4.78e-08 ***
     Error terms:
     Estimate Std. Error t value Pr(>|t|)
    sigma 0.91843 0.08754 10.492 < 2e-16 ***
    rho -0.60686 0.22383 -2.711 0.00694 **
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    --------------------------------------------
    >
    > ## Example for treatment regressions
    > ## Estimate the effect of treatment on income
    > ## selection outcome: treatment participation, logical (treatment)
    > ## selection explanatory variables: age, education (years)
    > ## unemployment in 1974, 1975, race
    > ## outcome: log real income 1978
    > ## outcome explanatory variables: treatment, age, education, race.
    > ## unemployment variables are treated as exclusion restriction
    > data(Treatment, package="Ecdat")
    Error in find.package(package, lib.loc, verbose = verbose) :
     there is no package called 'Ecdat'
    Calls: data -> find.package
    Execution halted
Flavor: r-devel-windows-x86_64-gcc10-UCRT

Version: 1.2-12
Check: running R code from vignettes
Result: WARN
    Errors in running code in vignettes:
    when running code in 'treatReg.Rnw'
    
    > library(sampleSelection)
    Loading required package: maxLik
    Loading required package: miscTools
    
    Please cite the 'maxLik' package as:
    Henningsen, Arne and Toomet, Ott (2011). maxLik: A package for maximum likelihood estimation in R. Computational Statistics 26(3), 443-458. DOI 10.1007/s00180-010-0217-1.
    
    If you have questions, suggestions, or comments regarding the 'maxLik' package, please use a forum or 'tracker' at maxLik's R-Forge site:
    https://r-forge.r-project.org/projects/maxlik/
    
    > options(width = 70)
    
    > set.seed(0)
    
    > N <- 2000
    
    > sigma <- 1
    
    > rho <- 0.8
    
    > Sigma <- matrix(c(1, rho * sigma, rho * sigma, sigma^2),
    + 2, 2)
    
    > uv <- mvtnorm::rmvnorm(N, mean = c(0, 0), sigma = Sigma)
    
    > u <- uv[, 1]
    
    > v <- uv[, 2]
    
    > x <- rnorm(N)
    
    > z <- rnorm(N)
    
    > ySX <- -1 + x + z + u
    
    > yS <- ySX > 0
    
    > yO <- x + yS + v
    
    > dat <- data.frame(yO, yS, x, z)
    
    > m <- lm(yO ~ x + yS, data = dat)
    
    > print(summary(m))
    
    Call:
    lm(formula = yO ~ x + yS, data = dat)
    
    Residuals:
     Min 1Q Median 3Q Max
    -3.5146 -0.6649 0.0365 0.6754 2.6004
    
    Coefficients:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -0.25460 0.02557 -9.956 <2e-16 ***
    x 0.81027 0.02289 35.394 <2e-16 ***
    ySTRUE 1.92569 0.05129 37.546 <2e-16 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    Residual standard error: 0.9332 on 1997 degrees of freedom
    Multiple R-squared: 0.7054, Adjusted R-squared: 0.7052
    F-statistic: 2391 on 2 and 1997 DF, p-value: < 2.2e-16
    
    
    > tm <- treatReg(yS ~ x + z, yO ~ x + yS, data = dat)
    
    > print(summary(tm))
    --------------------------------------------
    Tobit treatment model (switching regression model)
    Maximum Likelihood estimation
    Newton-Raphson maximisation, 3 iterations
    Return code 1: gradient close to zero (gradtol)
    Log-Likelihood: -3254.356
    2000 observations: 1419 non-participants (selection FALSE) and 581
     participants (selection TRUE)
    
    8 free parameters (df = 1992)
    Probit selection equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -1.02120 0.04376 -23.34 <2e-16 ***
    x 1.01973 0.04555 22.39 <2e-16 ***
    z 1.05186 0.04651 22.62 <2e-16 ***
    Outcome equation:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) 0.01565 0.02943 0.532 0.595
    x 0.99599 0.02569 38.769 <2e-16 ***
    ySTRUE 0.98779 0.06571 15.033 <2e-16 ***
     Error terms:
     Estimate Std. Error t value Pr(>|t|)
    sigma 1.00761 0.01888 53.38 <2e-16 ***
    rho 0.81050 0.02385 33.98 <2e-16 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    --------------------------------------------
    
    > data(Treatment, package = "Ecdat")
    
     When sourcing 'treatReg.R':
    Error: there is no package called 'Ecdat'
    Execution halted
    
     'intReg.Rnw' using 'UTF-8'... OK
     'selection.Rnw' using 'UTF-8'... OK
     'treatReg.Rnw' using 'UTF-8'... failed
Flavor: r-devel-windows-x86_64-gcc10-UCRT

Version: 1.2-12
Check: re-building of vignette outputs
Result: NOTE
    Error(s) in re-building vignettes:
    --- re-building 'intReg.Rnw' using Sweave
    Loading required package: miscTools
    
    Please cite the 'maxLik' package as:
    Henningsen, Arne and Toomet, Ott (2011). maxLik: A package for maximum likelihood estimation in R. Computational Statistics 26(3), 443-458. DOI 10.1007/s00180-010-0217-1.
    
    If you have questions, suggestions, or comments regarding the 'maxLik' package, please use a forum or 'tracker' at maxLik's R-Forge site:
    https://r-forge.r-project.org/projects/maxlik/
    Loading required package: zoo
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
    texify: C:\jenkins\workspace\miktex\windows\build\source\Libraries\MiKTeX\Core\Utils\Utils.cpp:490: internal error
    
    Warning in system(paste(shQuote(texi2dvi), "--version"), intern = TRUE) :
     running command '"C:\PROGRA~1\MiKTeX\miktex\bin\x64\texify.exe" --version' had status 5
    --- finished re-building 'intReg.Rnw'
    
    --- re-building 'selection.Rnw' using Sweave
    Warning in sqrt(diag(vc)) : NaNs produced
    Warning in sqrt(diag(vc)) : NaNs produced
    Warning in sqrt(diag(vcov(object, part = "full"))) : NaNs produced
    Warning in sqrt(diag(vc)) : NaNs produced
    Warning in sqrt(diag(vc)) : NaNs produced
    Warning in sqrt(diag(vcov(object, part = "full"))) : NaNs produced
    texify: C:\jenkins\workspace\miktex\windows\build\source\Libraries\MiKTeX\Core\Utils\Utils.cpp:490: internal error
    
    Warning in system(paste(shQuote(texi2dvi), "--version"), intern = TRUE) :
     running command '"C:\PROGRA~1\MiKTeX\miktex\bin\x64\texify.exe" --version' had status 5
    --- finished re-building 'selection.Rnw'
    
    --- re-building 'treatReg.Rnw' using Sweave
    
    Error: processing vignette 'treatReg.Rnw' failed with diagnostics:
     chunk 5 (label = EcdatExample)
    Error in find.package(package, lib.loc, verbose = verbose) :
     there is no package called 'Ecdat'
    
    --- failed re-building 'treatReg.Rnw'
    
    SUMMARY: processing the following file failed:
     'treatReg.Rnw'
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-devel-windows-x86_64-gcc10-UCRT

Version: 1.2-12
Check: PDF version of manual
Result: WARN
    LaTeX errors when creating PDF version.
    This typically indicates Rd problems.
Flavor: r-devel-windows-x86_64-gcc10-UCRT

Version: 1.2-12
Check: PDF version of manual without hyperrefs or index
Result: ERROR
    Re-running with no redirection of stdout/stderr.
Flavor: r-devel-windows-x86_64-gcc10-UCRT