Last updated on 2024-12-23 12:49:24 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.2-12 | 12.55 | 300.78 | 313.33 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 1.2-12 | 9.81 | 184.09 | 193.90 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.2-12 | 552.51 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 1.2-12 | 498.86 | NOTE | |||
| r-devel-windows-x86_64 | 1.2-12 | 15.00 | 327.00 | 342.00 | NOTE | |
| r-patched-linux-x86_64 | 1.2-12 | 14.77 | 286.21 | 300.98 | NOTE | |
| r-release-linux-x86_64 | 1.2-12 | 13.87 | 289.22 | 303.09 | NOTE | |
| r-release-macos-arm64 | 1.2-12 | 137.00 | WARN | |||
| r-release-macos-x86_64 | 1.2-12 | 209.00 | NOTE | |||
| r-release-windows-x86_64 | 1.2-12 | 14.00 | 344.00 | 358.00 | NOTE | |
| r-oldrel-macos-arm64 | 1.2-12 | 182.00 | NOTE | |||
| r-oldrel-macos-x86_64 | 1.2-12 | 316.00 | NOTE | |||
| r-oldrel-windows-x86_64 | 1.2-12 | 18.00 | 394.00 | 412.00 | OK |
Version: 1.2-12
Check: Rd files
Result: NOTE
checkRd: (-1) binaryChoice.Rd:105: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) binaryChoice.Rd:106: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) binaryChoice.Rd:111: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) binaryChoice.Rd:112: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) binaryChoice.Rd:113: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) binaryChoice.Rd:114: Lost braces in \itemize; \value handles \item{}{} directly
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-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64
Version: 1.2-12
Check: tests
Result: NOTE
Running ‘Mroz87SelectionTest.R’ [5s/6s]
Comparing ‘Mroz87SelectionTest.Rout’ to ‘Mroz87SelectionTest.Rout.save’ ... OK
Running ‘binarySelectionOutcome.R’ [15s/19s]
Comparing ‘binarySelectionOutcome.Rout’ to ‘binarySelectionOutcome.Rout.save’ ... OK
Running ‘fail.R’ [2s/3s]
Comparing ‘fail.Rout’ to ‘fail.Rout.save’ ... OK
Running ‘heckitIvTest.R’ [3s/3s]
Comparing ‘heckitIvTest.Rout’ to ‘heckitIvTest.Rout.save’ ... OK
Running ‘intervalTest.R’ [18s/25s]
Comparing ‘intervalTest.Rout’ to ‘intervalTest.Rout.save’ ... OK
Running ‘invMillsRatioTest.R’ [20s/25s]
Comparing ‘invMillsRatioTest.Rout’ to ‘invMillsRatioTest.Rout.save’ ...60a61
>
Running ‘probit.R’ [4s/4s]
Comparing ‘probit.Rout’ to ‘probit.Rout.save’ ... OK
Running ‘selection.R’ [14s/15s]
Comparing ‘selection.Rout’ to ‘selection.Rout.save’ ... OK
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.2-12
Check: examples
Result: ERROR
Running examples in ‘sampleSelection-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### 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-linux-x86_64-debian-gcc
Version: 1.2-12
Check: tests
Result: NOTE
Running ‘Mroz87SelectionTest.R’ [3s/4s]
Comparing ‘Mroz87SelectionTest.Rout’ to ‘Mroz87SelectionTest.Rout.save’ ... OK
Running ‘binarySelectionOutcome.R’ [8s/10s]
Comparing ‘binarySelectionOutcome.Rout’ to ‘binarySelectionOutcome.Rout.save’ ... OK
Running ‘fail.R’ [2s/2s]
Comparing ‘fail.Rout’ to ‘fail.Rout.save’ ... OK
Running ‘heckitIvTest.R’ [2s/2s]
Comparing ‘heckitIvTest.Rout’ to ‘heckitIvTest.Rout.save’ ... OK
Running ‘intervalTest.R’ [9s/11s]
Comparing ‘intervalTest.Rout’ to ‘intervalTest.Rout.save’ ... OK
Running ‘invMillsRatioTest.R’ [12s/16s]
Comparing ‘invMillsRatioTest.Rout’ to ‘invMillsRatioTest.Rout.save’ ...60a61
>
Running ‘probit.R’ [3s/5s]
Comparing ‘probit.Rout’ to ‘probit.Rout.save’ ... OK
Running ‘selection.R’ [10s/11s]
Comparing ‘selection.Rout’ to ‘selection.Rout.save’ ... OK
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.2-12
Check: re-building of vignette outputs
Result: ERROR
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
--- finished re-building ‘intReg.Rnw’
--- re-building ‘selection.Rnw’ using Sweave
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/
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
--- finished re-building ‘selection.Rnw’
--- re-building ‘treatReg.Rnw’ using Sweave
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/
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-linux-x86_64-debian-gcc
Version: 1.2-12
Check: tests
Result: NOTE
Running ‘Mroz87SelectionTest.R’ [8s/22s]
Comparing ‘Mroz87SelectionTest.Rout’ to ‘Mroz87SelectionTest.Rout.save’ ... OK
Running ‘binarySelectionOutcome.R’ [25s/66s]
Comparing ‘binarySelectionOutcome.Rout’ to ‘binarySelectionOutcome.Rout.save’ ... OK
Running ‘fail.R’ [4s/14s]
Comparing ‘fail.Rout’ to ‘fail.Rout.save’ ... OK
Running ‘heckitIvTest.R’ [4s/11s]
Comparing ‘heckitIvTest.Rout’ to ‘heckitIvTest.Rout.save’ ... OK
Running ‘intervalTest.R’ [32s/82s]
Comparing ‘intervalTest.Rout’ to ‘intervalTest.Rout.save’ ... OK
Running ‘invMillsRatioTest.R’ [38s/141s]
Comparing ‘invMillsRatioTest.Rout’ to ‘invMillsRatioTest.Rout.save’ ...60a61
>
Running ‘probit.R’ [6s/26s]
Comparing ‘probit.Rout’ to ‘probit.Rout.save’ ... OK
Running ‘selection.R’ [27s/98s]
Comparing ‘selection.Rout’ to ‘selection.Rout.save’ ... OK
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.2-12
Check: tests
Result: NOTE
Running ‘Mroz87SelectionTest.R’
Comparing ‘Mroz87SelectionTest.Rout’ to ‘Mroz87SelectionTest.Rout.save’ ... OK
Running ‘binarySelectionOutcome.R’ [22s/30s]
Comparing ‘binarySelectionOutcome.Rout’ to ‘binarySelectionOutcome.Rout.save’ ... OK
Running ‘fail.R’
Comparing ‘fail.Rout’ to ‘fail.Rout.save’ ... OK
Running ‘heckitIvTest.R’
Comparing ‘heckitIvTest.Rout’ to ‘heckitIvTest.Rout.save’ ... OK
Running ‘intervalTest.R’ [28s/36s]
Comparing ‘intervalTest.Rout’ to ‘intervalTest.Rout.save’ ... OK
Running ‘invMillsRatioTest.R’ [33s/40s]
Comparing ‘invMillsRatioTest.Rout’ to ‘invMillsRatioTest.Rout.save’ ...60a61
>
Running ‘probit.R’
Comparing ‘probit.Rout’ to ‘probit.Rout.save’ ... OK
Running ‘selection.R’ [25s/33s]
Comparing ‘selection.Rout’ to ‘selection.Rout.save’ ... OK
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 1.2-12
Check: tests
Result: NOTE
Running 'Mroz87SelectionTest.R' [5s]
Comparing 'Mroz87SelectionTest.Rout' to 'Mroz87SelectionTest.Rout.save' ... OK
Running 'binarySelectionOutcome.R' [11s]
Comparing 'binarySelectionOutcome.Rout' to 'binarySelectionOutcome.Rout.save' ... OK
Running 'fail.R' [2s]
Comparing 'fail.Rout' to 'fail.Rout.save' ... OK
Running 'heckitIvTest.R' [2s]
Comparing 'heckitIvTest.Rout' to 'heckitIvTest.Rout.save' ... OK
Running 'intervalTest.R' [13s]
Comparing 'intervalTest.Rout' to 'intervalTest.Rout.save' ... OK
Running 'invMillsRatioTest.R' [17s]
Comparing 'invMillsRatioTest.Rout' to 'invMillsRatioTest.Rout.save' ...60a61
>
Running 'probit.R' [3s]
Comparing 'probit.Rout' to 'probit.Rout.save' ... OK
Running 'selection.R' [15s]
Comparing 'selection.Rout' to 'selection.Rout.save' ... OK
Flavor: r-devel-windows-x86_64
Version: 1.2-12
Check: re-building of vignette outputs
Result: WARN
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
--- finished re-building ‘intReg.Rnw’
--- re-building ‘selection.Rnw’ using Sweave
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/
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
--- finished re-building ‘selection.Rnw’
--- re-building ‘treatReg.Rnw’ using Sweave
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/
Error: processing vignette 'treatReg.Rnw' failed with diagnostics:
Running 'texi2dvi' on 'treatReg.tex' failed.
LaTeX errors:
!pdfTeX error: pdflatex (file bbm10): Font bbm10 at 600 not found
==> Fatal error occurred, no output PDF file produced!
--- failed re-building ‘treatReg.Rnw’
SUMMARY: processing the following file failed:
‘treatReg.Rnw’
Error: Vignette re-building failed.
Execution halted
Flavor: r-release-macos-arm64
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
--- finished re-building ‘intReg.Rnw’
--- re-building ‘selection.Rnw’ using Sweave
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/
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
--- finished re-building ‘selection.Rnw’
--- re-building ‘treatReg.Rnw’ using Sweave
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/
Error: processing vignette 'treatReg.Rnw' failed with diagnostics:
Running 'texi2dvi' on 'treatReg.tex' failed.
LaTeX errors:
!pdfTeX error: pdflatex (file bbm10): Font bbm10 at 600 not found
==> Fatal error occurred, no output PDF file produced!
--- failed re-building ‘treatReg.Rnw’
SUMMARY: processing the following file failed:
‘treatReg.Rnw’
Error: Vignette re-building failed.
Execution halted
Flavor: r-oldrel-macos-arm64
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
--- finished re-building ‘intReg.Rnw’
--- re-building ‘selection.Rnw’ using Sweave
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/
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
--- finished re-building ‘selection.Rnw’
--- re-building ‘treatReg.Rnw’ using Sweave
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/
Error: processing vignette 'treatReg.Rnw' failed with diagnostics:
Running 'texi2dvi' on 'treatReg.tex' failed.
LaTeX errors:
! LaTeX Error: File `icomma.sty' not found.
Type X to quit or <RETURN> to proceed,
or enter new name. (Default extension: sty)
! Emergency stop.
<read *>
l.19 \usepackage
{natbib}^^M
! ==> Fatal error occurred, no output PDF file produced!
--- failed re-building ‘treatReg.Rnw’
SUMMARY: processing the following file failed:
‘treatReg.Rnw’
Error: Vignette re-building failed.
Execution halted
Flavor: r-oldrel-macos-x86_64