• using R Under development (unstable) (2026-07-30 r90327)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc-16 (Debian 16.1.0-3) 16.1.0     GNU Fortran (Debian 16.1.0-3) 16.1.0
• running under: Debian GNU/Linux forky/sid
• using session charset: UTF-8 * current time: 2026-07-31 12:46:26 UTC
• checking for file ‘Renvlp/DESCRIPTION’ ... OK
• checking extension type ... Package
• this is package ‘Renvlp’ version ‘3.4.5’
• checking CRAN incoming feasibility ... [1s/2s] NOTE Maintainer: ‘Minji Lee <minjilee101@gmail.com>’ No Authors@R field in DESCRIPTION. Please add one, modifying   Authors@R: c(person(given = "Minji",                       family = "Lee",                       role = c("aut", "cre"),                       email = "minjilee101@gmail.com"),                person(given = "Zhihua",                       family = "Su",                       role = "aut")) as necessary.
• checking package namespace information ... OK
• checking package dependencies ... OK
• checking if this is a source package ... OK
• checking if there is a namespace ... OK
• checking for executable files ... OK
• checking for hidden files and directories ... OK
• checking for portable file names ... OK
• checking for sufficient/correct file permissions ... OK
• checking whether package ‘Renvlp’ can be installed ... OK See the install log for details.
• checking package directory ... OK
• checking for future file timestamps ... OK
• checking DESCRIPTION meta-information ... OK
• checking top-level files ... OK
• checking for left-over files ... OK
• checking index information ... OK
• checking package subdirectories ... OK
• checking code files for non-ASCII characters ... OK
• checking R files for syntax errors ... OK
• checking whether the package can be loaded ... [0s/0s] OK
• checking whether the package can be loaded with stated dependencies ... [0s/0s] OK
• checking whether the package can be unloaded cleanly ... [0s/0s] OK
• checking whether the namespace can be loaded with stated dependencies ... [0s/0s] OK
• checking whether the namespace can be unloaded cleanly ... [0s/0s] OK
• checking loading without being on the library search path ... [0s/0s] OK
• checking use of S3 registration ... OK
• checking dependencies in R code ... OK
• checking S3 generic/method consistency ... OK
• checking replacement functions ... OK
• checking foreign function calls ... OK
• checking R code for possible problems ... [29s/36s] OK
• checking Rd files ... [1s/1s] NOTE checkRd: (-1) testcoef.env.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.env.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.env.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.env.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.env.apweights.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with nonconstant errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.env.apweights.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with nonconstant errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.env.apweights.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with nonconstant errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.env.apweights.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with nonconstant errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.env.tcond.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with t-distributed errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.env.tcond.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with t-distributed errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.env.tcond.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with t-distributed errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.env.tcond.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model with t-distributed errors. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.genv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta[[i]] R = A, versus Ha: L beta[[i]] R != A. The beta is estimated by the groupwise envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta[[i]] = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.genv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta[[i]] R = A, versus Ha: L beta[[i]] R != A. The beta is estimated by the groupwise envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta[[i]] = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.genv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta[[i]] R = A, versus Ha: L beta[[i]] R != A. The beta is estimated by the groupwise envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta[[i]] = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.genv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta[[i]] R = A, versus Ha: L beta[[i]] R != A. The beta is estimated by the groupwise envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta[[i]] = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.henv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the heteroscedastic envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.henv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the heteroscedastic envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.henv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the heteroscedastic envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.henv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the heteroscedastic envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.logit.env.Rd:18: Lost braces     18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.        | ^ checkRd: (-1) testcoef.logit.env.Rd:18: Lost braces; missing escapes or markup?     18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.        | ^ checkRd: (-1) testcoef.logit.env.Rd:18: Lost braces; missing escapes or markup?     18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.        | ^ checkRd: (-1) testcoef.logit.env.Rd:18: Lost braces     18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.        | ^ checkRd: (-1) testcoef.penv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta1 R = A, versus Ha: L beta1 R != A. The beta is estimated by the partial envelope model. If L = Ir, R = Ip1 and A = 0, then the test is equivalent to the standard F test on if beta1 = 0. The test statistics used is vec(L beta1 R - A) hat{Sigma}^{-1} vec(L beta1 R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta1 R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.penv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta1 R = A, versus Ha: L beta1 R != A. The beta is estimated by the partial envelope model. If L = Ir, R = Ip1 and A = 0, then the test is equivalent to the standard F test on if beta1 = 0. The test statistics used is vec(L beta1 R - A) hat{Sigma}^{-1} vec(L beta1 R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta1 R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.penv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta1 R = A, versus Ha: L beta1 R != A. The beta is estimated by the partial envelope model. If L = Ir, R = Ip1 and A = 0, then the test is equivalent to the standard F test on if beta1 = 0. The test statistics used is vec(L beta1 R - A) hat{Sigma}^{-1} vec(L beta1 R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta1 R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.penv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta1 R = A, versus Ha: L beta1 R != A. The beta is estimated by the partial envelope model. If L = Ir, R = Ip1 and A = 0, then the test is equivalent to the standard F test on if beta1 = 0. The test statistics used is vec(L beta1 R - A) hat{Sigma}^{-1} vec(L beta1 R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta1 R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.pois.env.Rd:18: Lost braces     18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.        | ^ checkRd: (-1) testcoef.pois.env.Rd:18: Lost braces; missing escapes or markup?     18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.        | ^ checkRd: (-1) testcoef.pois.env.Rd:18: Lost braces; missing escapes or markup?     18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.        | ^ checkRd: (-1) testcoef.pois.env.Rd:18: Lost braces     18 | This function tests for hypothesis H0: L beta = A, versus Ha: L beta != A. The beta is estimated by the envelope model in predictor space. If L = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta - A) hat{Sigma}^{-1} vec(L beta - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta - A). The reference distribution is chi-squared distribution with degrees of freedom d1.        | ^ checkRd: (-1) testcoef.rrenv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.rrenv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.rrenv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.rrenv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.rrenv.apweights.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model that accommodates nonconstant error variance. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.rrenv.apweights.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model that accommodates nonconstant error variance. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.rrenv.apweights.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model that accommodates nonconstant error variance. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.rrenv.apweights.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the reduced rank envelope model that accommodates nonconstant error variance. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.senv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.senv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.senv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.senv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.stenv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the simultaneous envelope model. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.stenv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the simultaneous envelope model. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.stenv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the simultaneous envelope model. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.stenv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the simultaneous envelope model. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.sxenv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model in the predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.sxenv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model in the predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.sxenv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model in the predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.sxenv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model in the predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.xenv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model in predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.xenv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model in predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.xenv.Rd:19: Lost braces; missing escapes or markup?     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model in predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) testcoef.xenv.Rd:19: Lost braces     19 | This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the envelope model in predictor space. If L = Ip, R = Ir and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hat{Sigma}^{-1} vec(L beta R - A)^{T}, where beta is the envelope estimator and hat{Sigma} is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.        | ^ checkRd: (-1) xenv.Rd:28: Lost braces; missing escapes or markup?     28 | \item{eta}{The estimated eta. According to the envelope parameterization, beta = Gamma * Omega^{-1} * eta.}        | ^
• checking Rd metadata ... OK
• checking Rd line widths ... OK
• checking Rd cross-references ... OK
• checking for missing documentation entries ... OK
• checking for code/documentation mismatches ... OK
• checking Rd \usage sections ... OK
• checking Rd contents ... OK
• checking for unstated dependencies in examples ... OK
• checking contents of ‘data’ directory ... OK
• checking data for non-ASCII characters ... [0s/0s] OK
• checking data for ASCII and uncompressed saves ... OK
• checking examples ... [34s/46s] OK
• checking PDF version of manual ... [7s/10s] OK
• checking HTML version of manual ... [3s/4s] OK
• checking for non-standard things in the check directory ... OK
• checking for new files in some other directories ... NOTE Found the following files/directories:   ‘~/tmp/scratch/Rtmp0XdQbZ’ ‘~/tmp/scratch/Rtmp1kPBha’   ‘~/tmp/scratch/Rtmp1uqyGD’ ‘~/tmp/scratch/Rtmp28KSFZ’   ‘~/tmp/scratch/Rtmp2kwgG6’ ‘~/tmp/scratch/Rtmp2to0Qa’   ‘~/tmp/scratch/Rtmp31vE5V’ ‘~/tmp/scratch/Rtmp3WiZhR’   ‘~/tmp/scratch/Rtmp3XW7ao’ ‘~/tmp/scratch/Rtmp3YKDT4’   ‘~/tmp/scratch/Rtmp3c730q’ ‘~/tmp/scratch/Rtmp4EDSWU’   ‘~/tmp/scratch/Rtmp4GGdpR’ ‘~/tmp/scratch/Rtmp4XGjXv’   ‘~/tmp/scratch/Rtmp4foTIT’ ‘~/tmp/scratch/Rtmp5gAtU7’   ‘~/tmp/scratch/Rtmp5yHO4K’ ‘~/tmp/scratch/Rtmp6Wp8pQ’   ‘~/tmp/scratch/Rtmp7JKNzD’ ‘~/tmp/scratch/Rtmp8ZLuYX’   ‘~/tmp/scratch/Rtmp8vrWzQ’ ‘~/tmp/scratch/Rtmp92gdWS’   ‘~/tmp/scratch/Rtmp93gnNr’ ‘~/tmp/scratch/Rtmp9BPRFS’   ‘~/tmp/scratch/Rtmp9Yo19t’ ‘~/tmp/scratch/Rtmp9ZWSrI’   ‘~/tmp/scratch/RtmpA55EWO’ ‘~/tmp/scratch/RtmpB1hO4M’   ‘~/tmp/scratch/RtmpBPmm0D’ ‘~/tmp/scratch/RtmpBhtLym’   ‘~/tmp/scratch/RtmpBnZLXF’ ‘~/tmp/scratch/RtmpBqciRz’   ‘~/tmp/scratch/RtmpCkZIXt’ ‘~/tmp/scratch/RtmpCm6AXR’   ‘~/tmp/scratch/RtmpCx25sv’ ‘~/tmp/scratch/RtmpDCnSqK’   ‘~/tmp/scratch/RtmpDGtU2v’ ‘~/tmp/scratch/RtmpDnGJIB’   ‘~/tmp/scratch/RtmpEHrapP’ ‘~/tmp/scratch/RtmpFf8SxG’   ‘~/tmp/scratch/RtmpFjfjiP’ ‘~/tmp/scratch/RtmpGK3vmm’   ‘~/tmp/scratch/RtmpGQSxBV’ ‘~/tmp/scratch/RtmpGu1xqO’   ‘~/tmp/scratch/RtmpH8ATbU’ ‘~/tmp/scratch/RtmpHHn3uq’   ‘~/tmp/scratch/RtmpHOrHp7’ ‘~/tmp/scratch/RtmpHpo9js’   ‘~/tmp/scratch/RtmpHxFy4r’ ‘~/tmp/scratch/RtmpI4G83L’   ‘~/tmp/scratch/RtmpIngnZv’ ‘~/tmp/scratch/RtmpIpiKnY’   ‘~/tmp/scratch/RtmpJlIj8E’ ‘~/tmp/scratch/RtmpKawXpU’   ‘~/tmp/scratch/RtmpKfL5XQ’ ‘~/tmp/scratch/RtmpLF7nFs’   ‘~/tmp/scratch/RtmpLPoCsk’ ‘~/tmp/scratch/RtmpMn9t3t’   ‘~/tmp/scratch/RtmpMxwJe6’ ‘~/tmp/scratch/RtmpN2a1SQ’   ‘~/tmp/scratch/RtmpNL6P4B’ ‘~/tmp/scratch/RtmpO44YWy’   ‘~/tmp/scratch/RtmpODmHNL’ ‘~/tmp/scratch/RtmpORpCjl’   ‘~/tmp/scratch/RtmpPf58kA’ ‘~/tmp/scratch/RtmpQopISu’   ‘~/tmp/scratch/RtmpS5GyWZ’ ‘~/tmp/scratch/RtmpSbiTQC’   ‘~/tmp/scratch/RtmpTbNUmc’ ‘~/tmp/scratch/RtmpUxEsny’   ‘~/tmp/scratch/RtmpVu2Y5I’ ‘~/tmp/scratch/RtmpVy2eRa’   ‘~/tmp/scratch/RtmpXN5w7s’ ‘~/tmp/scratch/RtmpYT5b1z’   ‘~/tmp/scratch/RtmpZAndlq’ ‘~/tmp/scratch/RtmpZmpnVw’   ‘~/tmp/scratch/RtmpaP7Mj5’ ‘~/tmp/scratch/Rtmpanakml’   ‘~/tmp/scratch/RtmpbHRuSv’ ‘~/tmp/scratch/RtmpbTRE2Y’   ‘~/tmp/scratch/RtmpcBpOi1’ ‘~/tmp/scratch/RtmpcQmjhQ’   ‘~/tmp/scratch/RtmpciVr0j’ ‘~/tmp/scratch/RtmpfI1QEz’   ‘~/tmp/scratch/RtmpfdzrN2’ ‘~/tmp/scratch/Rtmpff3Z4o’   ‘~/tmp/scratch/RtmpfnXDAi’ ‘~/tmp/scratch/Rtmpfwz9EE’   ‘~/tmp/scratch/Rtmpfx9bUl’ ‘~/tmp/scratch/Rtmpg25EGp’   ‘~/tmp/scratch/RtmpgHc3l1’ ‘~/tmp/scratch/RtmpgPLghQ’   ‘~/tmp/scratch/Rtmpgieoo9’ ‘~/tmp/scratch/Rtmph05gme’   ‘~/tmp/scratch/RtmphUurs9’ ‘~/tmp/scratch/RtmphscmOU’   ‘~/tmp/scratch/Rtmpi23l2S’ ‘~/tmp/scratch/RtmpiUeyLz’   ‘~/tmp/scratch/Rtmpj9C0d0’ ‘~/tmp/scratch/Rtmpj9PXce’   ‘~/tmp/scratch/RtmpjZymas’ ‘~/tmp/scratch/RtmpjgB74Q’   ‘~/tmp/scratch/RtmpjmjGIx’ ‘~/tmp/scratch/Rtmpk55BQy’   ‘~/tmp/scratch/Rtmpk7qZTE’ ‘~/tmp/scratch/RtmpkKSsbu’   ‘~/tmp/scratch/RtmpkrMN66’ ‘~/tmp/scratch/Rtmpl7flsS’   ‘~/tmp/scratch/RtmplFuRYS’ ‘~/tmp/scratch/RtmplFwS1r’   ‘~/tmp/scratch/RtmplGCoJF’ ‘~/tmp/scratch/Rtmpm5GLTk’   ‘~/tmp/scratch/Rtmpm6VplB’ ‘~/tmp/scratch/Rtmpmtp1Yu’   ‘~/tmp/scratch/Rtmpn0QmTQ’ ‘~/tmp/scratch/RtmpnCLTtB’   ‘~/tmp/scratch/RtmpnOjSm4’ ‘~/tmp/scratch/Rtmpnb0DF7’   ‘~/tmp/scratch/Rtmpnpxwc8’ ‘~/tmp/scratch/Rtmpo5xnka’   ‘~/tmp/scratch/RtmpopAxcy’ ‘~/tmp/scratch/Rtmpoz6OiW’   ‘~/tmp/scratch/Rtmppbb5Wv’ ‘~/tmp/scratch/RtmppraAsd’   ‘~/tmp/scratch/RtmppuKRWU’ ‘~/tmp/scratch/RtmpqAJqZn’   ‘~/tmp/scratch/RtmpqFIhjY’ ‘~/tmp/scratch/RtmpqGr3DS’   ‘~/tmp/scratch/RtmprUIOCr’ ‘~/tmp/scratch/RtmprbwBM8’   ‘~/tmp/scratch/Rtmpt5tTtY’ ‘~/tmp/scratch/RtmptZfDxe’   ‘~/tmp/scratch/RtmpujTFzy’ ‘~/tmp/scratch/Rtmpv206nf’   ‘~/tmp/scratch/Rtmpv2KU42’ ‘~/tmp/scratch/Rtmpwmiew5’   ‘~/tmp/scratch/RtmpxeVjHr’ ‘~/tmp/scratch/Rtmpxj8dlO’   ‘~/tmp/scratch/RtmpxuPtIX’ ‘~/tmp/scratch/Rtmpy6ezYU’   ‘~/tmp/scratch/RtmpyimK0Z’ ‘~/tmp/scratch/Rtmpz5PDNT’   ‘~/tmp/scratch/RtmpzEIKDT’ ‘~/tmp/scratch/RtmpznQa8F’   ‘~/tmp/scratch/Rtmpzv5Ofj’ ‘~/tmp/scratch/RtmpzzbBrZ’   ‘~/tmp/scratch/ccGkni6O.s’ ‘~/tmp/scratch/xvfb-run.0FKnmx’   ‘~/tmp/scratch/xvfb-run.1heDvx’ ‘~/tmp/scratch/xvfb-run.4WdFBt’   ‘~/tmp/scratch/xvfb-run.5J6usF’ ‘~/tmp/scratch/xvfb-run.5t4UxN’   ‘~/tmp/scratch/xvfb-run.7OCSVY’ ‘~/tmp/scratch/xvfb-run.9QREby’   ‘~/tmp/scratch/xvfb-run.BNjJ6J’ ‘~/tmp/scratch/xvfb-run.BbXu1i’   ‘~/tmp/scratch/xvfb-run.D7k0nS’ ‘~/tmp/scratch/xvfb-run.DGAEWO’   ‘~/tmp/scratch/xvfb-run.DIDvnF’ ‘~/tmp/scratch/xvfb-run.DZry2g’   ‘~/tmp/scratch/xvfb-run.Es5u5y’ ‘~/tmp/scratch/xvfb-run.HqyvvT’   ‘~/tmp/scratch/xvfb-run.I99lAN’ ‘~/tmp/scratch/xvfb-run.IB3RQe’   ‘~/tmp/scratch/xvfb-run.IBcqWf’ ‘~/tmp/scratch/xvfb-run.JDTJHh’   ‘~/tmp/scratch/xvfb-run.JRiA5y’ ‘~/tmp/scratch/xvfb-run.JzOfwX’   ‘~/tmp/scratch/xvfb-run.L4Ijte’ ‘~/tmp/scratch/xvfb-run.LNphCA’   ‘~/tmp/scratch/xvfb-run.LVVWt6’ ‘~/tmp/scratch/xvfb-run.MZy0Mj’   ‘~/tmp/scratch/xvfb-run.NkKE6w’ ‘~/tmp/scratch/xvfb-run.PAVJWI’   ‘~/tmp/scratch/xvfb-run.PiyB3Y’ ‘~/tmp/scratch/xvfb-run.PkZTTh’   ‘~/tmp/scratch/xvfb-run.S4sWbv’ ‘~/tmp/scratch/xvfb-run.SXnBJH’   ‘~/tmp/scratch/xvfb-run.SdvEEg’ ‘~/tmp/scratch/xvfb-run.VP9LFN’   ‘~/tmp/scratch/xvfb-run.Xnm3J9’ ‘~/tmp/scratch/xvfb-run.Yk766f’   ‘~/tmp/scratch/xvfb-run.YnSI8e’ ‘~/tmp/scratch/xvfb-run.aEpNC4’   ‘~/tmp/scratch/xvfb-run.aejLtA’ ‘~/tmp/scratch/xvfb-run.bVAmnJ’   ‘~/tmp/scratch/xvfb-run.bhYyf7’ ‘~/tmp/scratch/xvfb-run.bpD8Gk’   ‘~/tmp/scratch/xvfb-run.cfbZ3i’ ‘~/tmp/scratch/xvfb-run.dIh1uR’   ‘~/tmp/scratch/xvfb-run.dWvi5n’ ‘~/tmp/scratch/xvfb-run.fYzF5S’   ‘~/tmp/scratch/xvfb-run.gI4KOL’ ‘~/tmp/scratch/xvfb-run.gMkD0k’   ‘~/tmp/scratch/xvfb-run.hiGUez’ ‘~/tmp/scratch/xvfb-run.inj7uV’   ‘~/tmp/scratch/xvfb-run.jNIyqE’ ‘~/tmp/scratch/xvfb-run.jyw5gG’   ‘~/tmp/scratch/xvfb-run.kTYoWl’ ‘~/tmp/scratch/xvfb-run.mPfnqe’   ‘~/tmp/scratch/xvfb-run.mZdHmP’ ‘~/tmp/scratch/xvfb-run.olpdmm’   ‘~/tmp/scratch/xvfb-run.orHbWN’ ‘~/tmp/scratch/xvfb-run.rP1PdR’   ‘~/tmp/scratch/xvfb-run.ugEUq0’ ‘~/tmp/scratch/xvfb-run.w34OIP’   ‘~/tmp/scratch/xvfb-run.wShmvi’ ‘~/tmp/scratch/xvfb-run.wuHNcH’   ‘~/tmp/scratch/xvfb-run.xXEvmJ’ ‘~/tmp/scratch/xvfb-run.xu69nG’   ‘~/tmp/scratch/xvfb-run.z427Ru’ ‘~/tmp/scratch/xvfb-run.zEv34s’   ‘/dev/shm/sm_segment.gimli1.1001.433c0000.0’   ‘/dev/shm/sm_segment.gimli1.1001.441d0000.0’   ‘/dev/shm/sm_segment.gimli1.1001.4c440000.0’   ‘/dev/shm/sm_segment.gimli1.1001.530d0000.0’   ‘/dev/shm/sm_segment.gimli1.1001.625c0000.0’   ‘/dev/shm/sm_segment.gimli1.1001.783f0000.0’   ‘/dev/shm/sm_segment.gimli1.1001.851f0000.0’   ‘/dev/shm/sm_segment.gimli1.1001.8f420000.0’   ‘/dev/shm/sm_segment.gimli1.1001.92a20000.0’   ‘/dev/shm/sm_segment.gimli1.1001.92e00000.0’   ‘/dev/shm/sm_segment.gimli1.1001.9e7b0000.0’   ‘/dev/shm/sm_segment.gimli1.1001.baeb0000.0’   ‘/dev/shm/sm_segment.gimli1.1001.de9a0000.0’   ‘/dev/shm/sm_segment.gimli1.1001.eac70000.0’   ‘~/.cache/pocl/uncached/tempfile_5XzKIS’   ‘~/.cache/pocl/uncached/tempfile_6fuTxQ’   ‘~/.cache/pocl/uncached/tempfile_7CGBNp’   ‘~/.cache/pocl/uncached/tempfile_DHUwpg’   ‘~/.cache/pocl/uncached/tempfile_GFtxiP’   ‘~/.cache/pocl/uncached/tempfile_LInNcs’   ‘~/.cache/pocl/uncached/tempfile_LnCKkl’   ‘~/.cache/pocl/uncached/tempfile_O3SodK’   ‘~/.cache/pocl/uncached/tempfile_Py2lWk’   ‘~/.cache/pocl/uncached/tempfile_ZUcDNk’   ‘~/.cache/pocl/uncached/tempfile_ihM1MJ’   ‘~/.cache/pocl/uncached/tempfile_tHGLUd’   ‘~/.cache/pocl/uncached/tempfile_y76oLu’   ‘~/.cache/pocl/uncached/tempfile_zGuouL’
• DONE

Status: 3 NOTEs