Last updated on 2026-08-03 00:50:54 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 3.4.5 | 13.39 | 151.43 | 164.82 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 3.4.5 | 9.55 | 101.75 | 111.30 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 3.4.5 | 29.00 | 242.36 | 271.36 | NOTE | |
| r-devel-linux-x86_64-fedora-gcc | 3.4.5 | 11.00 | 96.50 | 107.50 | NOTE | |
| r-devel-windows-x86_64 | 3.4.5 | 18.00 | 156.00 | 174.00 | NOTE | |
| r-patched-linux-x86_64 | 3.4.5 | 18.53 | 143.23 | 161.76 | NOTE | |
| r-release-linux-x86_64 | 3.4.5 | 14.35 | 146.20 | 160.55 | NOTE | |
| r-release-macos-arm64 | 3.4.5 | 4.00 | 33.00 | 37.00 | NOTE | |
| r-release-macos-x86_64 | 3.4.5 | 11.00 | 152.00 | 163.00 | NOTE | |
| r-release-windows-x86_64 | 3.4.5 | 18.00 | 180.00 | 198.00 | NOTE | |
| r-oldrel-macos-arm64 | 3.4.5 | NOTE | ||||
| r-oldrel-macos-x86_64 | 3.4.5 | 10.00 | 149.00 | 159.00 | NOTE | |
| r-oldrel-windows-x86_64 | 3.4.5 | 24.00 | 212.00 | 236.00 | NOTE |
Version: 3.4.5
Check: CRAN incoming feasibility
Result: 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.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 3.4.5
Check: Rd files
Result: 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.}
| ^
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, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64
Version: 3.4.5
Check: for new files in some other directories
Result: NOTE
Found the following files/directories:
‘~/tmp/scratch/Rtmp0TQDds’ ‘~/tmp/scratch/Rtmp0i3Cvf’
‘~/tmp/scratch/Rtmp0x0D0J’ ‘~/tmp/scratch/Rtmp0zT0QC’
‘~/tmp/scratch/Rtmp1ga6X6’ ‘~/tmp/scratch/Rtmp3FtVjS’
‘~/tmp/scratch/Rtmp4EBECy’ ‘~/tmp/scratch/Rtmp4PlWfS’
‘~/tmp/scratch/Rtmp4hQY4N’ ‘~/tmp/scratch/Rtmp4nSgJt’
‘~/tmp/scratch/Rtmp58xbkR’ ‘~/tmp/scratch/Rtmp5awEuc’
‘~/tmp/scratch/Rtmp5mjfil’ ‘~/tmp/scratch/Rtmp6MsHWV’
‘~/tmp/scratch/Rtmp6nhXAi’ ‘~/tmp/scratch/Rtmp7QK1RG’
‘~/tmp/scratch/Rtmp84czMZ’ ‘~/tmp/scratch/Rtmp8R1ZlZ’
‘~/tmp/scratch/Rtmp8V2mPA’ ‘~/tmp/scratch/Rtmp8cMjsA’
‘~/tmp/scratch/Rtmp8l5vB9’ ‘~/tmp/scratch/Rtmp99p8zw’
‘~/tmp/scratch/RtmpAQn0dt’ ‘~/tmp/scratch/RtmpAnyKyp’
‘~/tmp/scratch/RtmpB1FrRj’ ‘~/tmp/scratch/RtmpB2Z01T’
‘~/tmp/scratch/RtmpBXQVba’ ‘~/tmp/scratch/RtmpC0nxfZ’
‘~/tmp/scratch/RtmpDMMeun’ ‘~/tmp/scratch/RtmpDknJVx’
‘~/tmp/scratch/RtmpEfTU3e’ ‘~/tmp/scratch/RtmpEkW0ry’
‘~/tmp/scratch/RtmpEoBoPZ’ ‘~/tmp/scratch/RtmpEp7Xsd’
‘~/tmp/scratch/RtmpF43bhP’ ‘~/tmp/scratch/RtmpFKbyiL’
‘~/tmp/scratch/RtmpFWFczq’ ‘~/tmp/scratch/RtmpFXyf5e’
‘~/tmp/scratch/RtmpFw8VZG’ ‘~/tmp/scratch/RtmpG8B7yw’
‘~/tmp/scratch/RtmpG8mfZ5’ ‘~/tmp/scratch/RtmpGIqSrI’
‘~/tmp/scratch/RtmpH3WE2Y’ ‘~/tmp/scratch/RtmpHpsEQr’
‘~/tmp/scratch/RtmpJ2LnQ8’ ‘~/tmp/scratch/RtmpJVYWyP’
‘~/tmp/scratch/RtmpJnsg2k’ ‘~/tmp/scratch/RtmpJpOAdY’
‘~/tmp/scratch/RtmpKsCCol’ ‘~/tmp/scratch/RtmpLJ9RPt’
‘~/tmp/scratch/RtmpLppj48’ ‘~/tmp/scratch/RtmpLqtCxY’
‘~/tmp/scratch/RtmpM8Xcn1’ ‘~/tmp/scratch/RtmpMEdLaj’
‘~/tmp/scratch/RtmpMwgiYh’ ‘~/tmp/scratch/RtmpN9Mm4C’
‘~/tmp/scratch/RtmpNVIlyg’ ‘~/tmp/scratch/RtmpNhWtOB’
‘~/tmp/scratch/RtmpNpcUyU’ ‘~/tmp/scratch/RtmpOWAJVl’
‘~/tmp/scratch/RtmpOXwtDo’ ‘~/tmp/scratch/RtmpOZfRud’
‘~/tmp/scratch/RtmpOoJfY4’ ‘~/tmp/scratch/RtmpPFLGSz’
‘~/tmp/scratch/RtmpQ9uDyV’ ‘~/tmp/scratch/RtmpRJSb9w’
‘~/tmp/scratch/RtmpSrFTpE’ ‘~/tmp/scratch/RtmpTSXLoC’
‘~/tmp/scratch/RtmpTy1WQy’ ‘~/tmp/scratch/RtmpU1L4bi’
‘~/tmp/scratch/RtmpU3Jqbe’ ‘~/tmp/scratch/RtmpU7p4Vk’
‘~/tmp/scratch/RtmpUDA29E’ ‘~/tmp/scratch/RtmpUkEkq9’
‘~/tmp/scratch/RtmpV5BZ3u’ ‘~/tmp/scratch/RtmpVQ8rBT’
‘~/tmp/scratch/RtmpWSy2qy’ ‘~/tmp/scratch/RtmpX7GxpW’
‘~/tmp/scratch/RtmpXs1A6I’ ‘~/tmp/scratch/RtmpYCbdkA’
‘~/tmp/scratch/RtmpYZggSS’ ‘~/tmp/scratch/RtmpYe9wVL’
‘~/tmp/scratch/RtmpYxe9S8’ ‘~/tmp/scratch/RtmpZ671QO’
‘~/tmp/scratch/RtmpZCRAB1’ ‘~/tmp/scratch/RtmpZLTA9K’
‘~/tmp/scratch/RtmpZlEbv8’ ‘~/tmp/scratch/Rtmpakg7Ke’
‘~/tmp/scratch/RtmpbFdU20’ ‘~/tmp/scratch/Rtmpbjjaxz’
‘~/tmp/scratch/RtmpcP2LgL’ ‘~/tmp/scratch/Rtmpd4a9bp’
‘~/tmp/scratch/RtmpdE2hHv’ ‘~/tmp/scratch/Rtmpdt4JYt’
‘~/tmp/scratch/RtmpeiODho’ ‘~/tmp/scratch/RtmpfiJztg’
‘~/tmp/scratch/Rtmpfw94aL’ ‘~/tmp/scratch/RtmpgKeFWb’
‘~/tmp/scratch/RtmpgSYqV0’ ‘~/tmp/scratch/RtmphmRi6A’
‘~/tmp/scratch/RtmpiBBJdm’ ‘~/tmp/scratch/RtmpiIMj4R’
‘~/tmp/scratch/RtmpiN3bzF’ ‘~/tmp/scratch/RtmpiZ2jqO’
‘~/tmp/scratch/RtmpirN9VI’ ‘~/tmp/scratch/RtmpixmHqz’
‘~/tmp/scratch/RtmpjG1IrI’ ‘~/tmp/scratch/RtmpjieZFo’
‘~/tmp/scratch/RtmpkMJabv’ ‘~/tmp/scratch/RtmpkPCPV1’
‘~/tmp/scratch/Rtmpknme8E’ ‘~/tmp/scratch/Rtmpl1QOKB’
‘~/tmp/scratch/RtmplSmSSO’ ‘~/tmp/scratch/RtmplWf99M’
‘~/tmp/scratch/RtmplXCpGh’ ‘~/tmp/scratch/Rtmpm8PLk3’
‘~/tmp/scratch/RtmpmIKTZc’ ‘~/tmp/scratch/RtmpmWyYCB’
‘~/tmp/scratch/Rtmpn1pWx5’ ‘~/tmp/scratch/RtmpnANzLZ’
‘~/tmp/scratch/RtmpnJZMhU’ ‘~/tmp/scratch/RtmpoEZIHj’
‘~/tmp/scratch/RtmponsiFd’ ‘~/tmp/scratch/RtmppEMyfd’
‘~/tmp/scratch/RtmppsBr5V’ ‘~/tmp/scratch/RtmpqSqCKx’
‘~/tmp/scratch/RtmprLVTVb’ ‘~/tmp/scratch/RtmprRBJrp’
‘~/tmp/scratch/Rtmpt42WIV’ ‘~/tmp/scratch/RtmptVBTGn’
‘~/tmp/scratch/Rtmpth3Zjq’ ‘~/tmp/scratch/RtmpuLReCY’
‘~/tmp/scratch/RtmpungTez’ ‘~/tmp/scratch/Rtmpv7u1WT’
‘~/tmp/scratch/RtmpvgYeaS’ ‘~/tmp/scratch/RtmpvrZ43q’
‘~/tmp/scratch/RtmpwlImkt’ ‘~/tmp/scratch/RtmpxMPfPh’
‘~/tmp/scratch/Rtmpxq42Kz’ ‘~/tmp/scratch/RtmpyKHwxP’
‘~/tmp/scratch/RtmpyM2DVx’ ‘~/tmp/scratch/RtmpyouGcQ’
‘~/tmp/scratch/RtmpyuJFYc’ ‘~/tmp/scratch/RtmpzHDFtv’
‘~/tmp/scratch/RtmpzMmzmn’ ‘~/tmp/scratch/Rtmpzku6FV’
‘~/tmp/scratch/xvfb-run.0LISgg’ ‘~/tmp/scratch/xvfb-run.1eSy3f’
‘~/tmp/scratch/xvfb-run.2UxLmy’ ‘~/tmp/scratch/xvfb-run.5yAwKS’
‘~/tmp/scratch/xvfb-run.6DrR1q’ ‘~/tmp/scratch/xvfb-run.6WocQ5’
‘~/tmp/scratch/xvfb-run.7CXhNp’ ‘~/tmp/scratch/xvfb-run.7bUZhh’
‘~/tmp/scratch/xvfb-run.A3iA5y’ ‘~/tmp/scratch/xvfb-run.A6KuvU’
‘~/tmp/scratch/xvfb-run.C6TaIf’ ‘~/tmp/scratch/xvfb-run.EqxJgI’
‘~/tmp/scratch/xvfb-run.HBKeMS’ ‘~/tmp/scratch/xvfb-run.JDidc1’
‘~/tmp/scratch/xvfb-run.Ks5oPr’ ‘~/tmp/scratch/xvfb-run.KzpF6E’
‘~/tmp/scratch/xvfb-run.L8qiL3’ ‘~/tmp/scratch/xvfb-run.M1oZ2w’
‘~/tmp/scratch/xvfb-run.NQ0HUS’ ‘~/tmp/scratch/xvfb-run.PMbn4O’
‘~/tmp/scratch/xvfb-run.PkICTU’ ‘~/tmp/scratch/xvfb-run.PlgyGi’
‘~/tmp/scratch/xvfb-run.Q8yY5h’ ‘~/tmp/scratch/xvfb-run.QPzCI9’
‘~/tmp/scratch/xvfb-run.QcACUW’ ‘~/tmp/scratch/xvfb-run.RKhuxJ’
‘~/tmp/scratch/xvfb-run.RZB9yh’ ‘~/tmp/scratch/xvfb-run.SJ9nlM’
‘~/tmp/scratch/xvfb-run.SrvoSS’ ‘~/tmp/scratch/xvfb-run.THxyl8’
‘~/tmp/scratch/xvfb-run.TwJecf’ ‘~/tmp/scratch/xvfb-run.VC2mcG’
‘~/tmp/scratch/xvfb-run.VChaXx’ ‘~/tmp/scratch/xvfb-run.XW0P8c’
‘~/tmp/scratch/xvfb-run.Yw7cfz’ ‘~/tmp/scratch/xvfb-run.bet1uJ’
‘~/tmp/scratch/xvfb-run.cV76IQ’ ‘~/tmp/scratch/xvfb-run.ctznlm’
‘~/tmp/scratch/xvfb-run.dZmIOt’ ‘~/tmp/scratch/xvfb-run.eIWb2y’
‘~/tmp/scratch/xvfb-run.euLGNx’ ‘~/tmp/scratch/xvfb-run.fo8gTr’
‘~/tmp/scratch/xvfb-run.gsl6XX’ ‘~/tmp/scratch/xvfb-run.h8A5wn’
‘~/tmp/scratch/xvfb-run.hCHKsZ’ ‘~/tmp/scratch/xvfb-run.iasdjr’
‘~/tmp/scratch/xvfb-run.ijlLE3’ ‘~/tmp/scratch/xvfb-run.keqdgC’
‘~/tmp/scratch/xvfb-run.nGQHyw’ ‘~/tmp/scratch/xvfb-run.ojJWFR’
‘~/tmp/scratch/xvfb-run.or7ef4’ ‘~/tmp/scratch/xvfb-run.pE1PkQ’
‘~/tmp/scratch/xvfb-run.pP7RtM’ ‘~/tmp/scratch/xvfb-run.qTxlcG’
‘~/tmp/scratch/xvfb-run.ro5PIA’ ‘~/tmp/scratch/xvfb-run.s3PSvY’
‘~/tmp/scratch/xvfb-run.sZoquH’ ‘~/tmp/scratch/xvfb-run.t3f2iN’
‘~/tmp/scratch/xvfb-run.uYzuVY’ ‘~/tmp/scratch/xvfb-run.vMgfzA’
‘~/tmp/scratch/xvfb-run.vV9pv4’ ‘~/tmp/scratch/xvfb-run.wkCKB2’
‘~/tmp/scratch/xvfb-run.zD1vMo’ ‘~/tmp/scratch/xvfb-run.zdkPl0’
‘/dev/shm/sm_segment.gimli1.1001.1f0a0000.0’
‘/dev/shm/sm_segment.gimli1.1001.2d5c0000.0’
‘/dev/shm/sm_segment.gimli1.1001.31960000.0’
‘/dev/shm/sm_segment.gimli1.1001.57e20000.0’
‘/dev/shm/sm_segment.gimli1.1001.57f40000.0’
‘/dev/shm/sm_segment.gimli1.1001.705f0000.0’
‘/dev/shm/sm_segment.gimli1.1001.70f00000.0’
‘/dev/shm/sm_segment.gimli1.1001.a5f0000.0’
‘/dev/shm/sm_segment.gimli1.1001.b9970000.0’
‘/dev/shm/sm_segment.gimli1.1001.d8070000.0’
‘/dev/shm/sm_segment.gimli1.1001.dc240000.0’
‘/dev/shm/sm_segment.gimli1.1001.dddb0000.0’
‘/dev/shm/sm_segment.gimli1.1001.df5f0000.0’
‘~/.cache/pocl/uncached/tempfile_30QgqU’
‘~/.cache/pocl/uncached/tempfile_5FH7kI’
‘~/.cache/pocl/uncached/tempfile_6qmxM1’
‘~/.cache/pocl/uncached/tempfile_91dRY5’
‘~/.cache/pocl/uncached/tempfile_BQLQKj’
‘~/.cache/pocl/uncached/tempfile_FnYFsW’
‘~/.cache/pocl/uncached/tempfile_H2Tqtp’
‘~/.cache/pocl/uncached/tempfile_Pvu7NH’
‘~/.cache/pocl/uncached/tempfile_W2O3dc’
‘~/.cache/pocl/uncached/tempfile_WFzK8O’
‘~/.cache/pocl/uncached/tempfile_Y42FW3’
‘~/.cache/pocl/uncached/tempfile_ifNVCF’
‘~/.cache/pocl/uncached/tempfile_oYUzMr’
Flavor: r-devel-linux-x86_64-debian-gcc