Last updated on 2026-08-04 02:51:06 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 | 10.71 | 98.42 | 109.13 | 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 | 168.00 | 186.00 | 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 | 222.00 | 246.00 | 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/Rtmp1SMPM5’ ‘~/tmp/scratch/Rtmp2R6z3L’
‘~/tmp/scratch/Rtmp2frLKM’ ‘~/tmp/scratch/Rtmp31trkE’
‘~/tmp/scratch/Rtmp3xikYy’ ‘~/tmp/scratch/Rtmp574tki’
‘~/tmp/scratch/Rtmp5LXP8U’ ‘~/tmp/scratch/Rtmp76vZQ3’
‘~/tmp/scratch/Rtmp7qfehr’ ‘~/tmp/scratch/Rtmp7wqcKZ’
‘~/tmp/scratch/Rtmp80oTpr’ ‘~/tmp/scratch/Rtmp8gtNDe’
‘~/tmp/scratch/Rtmp8hRF2v’ ‘~/tmp/scratch/Rtmp8i1Ctj’
‘~/tmp/scratch/Rtmp8tDHA9’ ‘~/tmp/scratch/Rtmp8zRh8Z’
‘~/tmp/scratch/Rtmp9EEen5’ ‘~/tmp/scratch/Rtmp9FllOX’
‘~/tmp/scratch/RtmpB5KCMq’ ‘~/tmp/scratch/RtmpBJY2Bu’
‘~/tmp/scratch/RtmpBg7iuH’ ‘~/tmp/scratch/RtmpC0WVKg’
‘~/tmp/scratch/RtmpC7V8xt’ ‘~/tmp/scratch/RtmpCLgdp3’
‘~/tmp/scratch/RtmpCkaPN6’ ‘~/tmp/scratch/RtmpDHRYf6’
‘~/tmp/scratch/RtmpDIbLbj’ ‘~/tmp/scratch/RtmpDKRlcO’
‘~/tmp/scratch/RtmpDcHHNJ’ ‘~/tmp/scratch/RtmpDpoYi4’
‘~/tmp/scratch/RtmpEBa35a’ ‘~/tmp/scratch/RtmpEOkTxV’
‘~/tmp/scratch/RtmpESeu9d’ ‘~/tmp/scratch/RtmpFKxASO’
‘~/tmp/scratch/RtmpG3raOA’ ‘~/tmp/scratch/RtmpGTPNgN’
‘~/tmp/scratch/RtmpGpeZQv’ ‘~/tmp/scratch/RtmpHGzO0f’
‘~/tmp/scratch/RtmpHNmEfA’ ‘~/tmp/scratch/RtmpI1VpmV’
‘~/tmp/scratch/RtmpIDd9S5’ ‘~/tmp/scratch/RtmpJI5d0U’
‘~/tmp/scratch/RtmpJRfbZK’ ‘~/tmp/scratch/RtmpK4wV1h’
‘~/tmp/scratch/RtmpLIFpKN’ ‘~/tmp/scratch/RtmpMiPbTB’
‘~/tmp/scratch/RtmpN2C3xq’ ‘~/tmp/scratch/RtmpNLLna5’
‘~/tmp/scratch/RtmpOheXoE’ ‘~/tmp/scratch/RtmpPi90st’
‘~/tmp/scratch/RtmpPjS9kT’ ‘~/tmp/scratch/RtmpQGWcKk’
‘~/tmp/scratch/RtmpQSbjCj’ ‘~/tmp/scratch/RtmpRWwR5E’
‘~/tmp/scratch/RtmpRsczyr’ ‘~/tmp/scratch/RtmpRyU8mg’
‘~/tmp/scratch/RtmpSfRXi3’ ‘~/tmp/scratch/RtmpShs7xK’
‘~/tmp/scratch/RtmpSsHbUz’ ‘~/tmp/scratch/RtmpSun5Ur’
‘~/tmp/scratch/RtmpT15Emf’ ‘~/tmp/scratch/RtmpTB4tjf’
‘~/tmp/scratch/RtmpTmaVWx’ ‘~/tmp/scratch/RtmpTx5Sld’
‘~/tmp/scratch/RtmpUBiKu4’ ‘~/tmp/scratch/RtmpUSUo8N’
‘~/tmp/scratch/RtmpUTnE3b’ ‘~/tmp/scratch/RtmpUfZj75’
‘~/tmp/scratch/RtmpVD5PUu’ ‘~/tmp/scratch/RtmpVGwaqf’
‘~/tmp/scratch/RtmpVI75AB’ ‘~/tmp/scratch/RtmpVIBOs9’
‘~/tmp/scratch/RtmpVuZJ4g’ ‘~/tmp/scratch/RtmpXPgWke’
‘~/tmp/scratch/RtmpXWyxdE’ ‘~/tmp/scratch/RtmpXdICZK’
‘~/tmp/scratch/RtmpY14jgS’ ‘~/tmp/scratch/RtmpYHSSTd’
‘~/tmp/scratch/RtmpYQrZ6A’ ‘~/tmp/scratch/RtmpYlNv3E’
‘~/tmp/scratch/RtmpZs5dss’ ‘~/tmp/scratch/RtmpaisswK’
‘~/tmp/scratch/RtmpbDfni9’ ‘~/tmp/scratch/RtmpbaiWNj’
‘~/tmp/scratch/Rtmpbdjb9Y’ ‘~/tmp/scratch/RtmpbioZla’
‘~/tmp/scratch/RtmpcHmhwu’ ‘~/tmp/scratch/Rtmpcwg3KK’
‘~/tmp/scratch/Rtmpe4h5id’ ‘~/tmp/scratch/RtmpegXy27’
‘~/tmp/scratch/RtmpffXyC1’ ‘~/tmp/scratch/RtmpfrnMF8’
‘~/tmp/scratch/Rtmpfs1J19’ ‘~/tmp/scratch/RtmpglORKe’
‘~/tmp/scratch/RtmpgvaTY4’ ‘~/tmp/scratch/Rtmph1hBxF’
‘~/tmp/scratch/RtmphdidKx’ ‘~/tmp/scratch/Rtmpi0MqeG’
‘~/tmp/scratch/RtmpiWeeWw’ ‘~/tmp/scratch/RtmpijKBf1’
‘~/tmp/scratch/RtmpinuICt’ ‘~/tmp/scratch/RtmpjwT36V’
‘~/tmp/scratch/RtmplXOXQ3’ ‘~/tmp/scratch/RtmplzsSrI’
‘~/tmp/scratch/RtmpmBIgul’ ‘~/tmp/scratch/RtmpmOM5TU’
‘~/tmp/scratch/RtmpmQd8Qu’ ‘~/tmp/scratch/Rtmpmc5pi1’
‘~/tmp/scratch/Rtmpmkv6Kh’ ‘~/tmp/scratch/RtmpnCIKqx’
‘~/tmp/scratch/RtmpnILAWu’ ‘~/tmp/scratch/RtmpnjbjY6’
‘~/tmp/scratch/Rtmpo0Jyyl’ ‘~/tmp/scratch/RtmpoEgLB2’
‘~/tmp/scratch/RtmpoLu9Nq’ ‘~/tmp/scratch/RtmpoaYYl3’
‘~/tmp/scratch/RtmpohbJQR’ ‘~/tmp/scratch/Rtmpp43OAw’
‘~/tmp/scratch/Rtmpp86Yn2’ ‘~/tmp/scratch/Rtmppjd96B’
‘~/tmp/scratch/RtmpptTADS’ ‘~/tmp/scratch/RtmpqA0Hfc’
‘~/tmp/scratch/Rtmpr62zoQ’ ‘~/tmp/scratch/RtmpsB2zhF’
‘~/tmp/scratch/RtmpsXojO6’ ‘~/tmp/scratch/Rtmpt3soou’
‘~/tmp/scratch/RtmptO3Vse’ ‘~/tmp/scratch/RtmptOHPat’
‘~/tmp/scratch/Rtmpu9dnwj’ ‘~/tmp/scratch/RtmpvOI7tt’
‘~/tmp/scratch/RtmpvOItJ8’ ‘~/tmp/scratch/RtmpvYPp8J’
‘~/tmp/scratch/RtmpvavWJJ’ ‘~/tmp/scratch/RtmpwLfxvD’
‘~/tmp/scratch/RtmpwuGqA0’ ‘~/tmp/scratch/RtmpxG7Dh4’
‘~/tmp/scratch/Rtmpxa6BdE’ ‘~/tmp/scratch/RtmpyLxeZT’
‘~/tmp/scratch/RtmpyYB7ZZ’ ‘~/tmp/scratch/Rtmpyc0MiQ’
‘~/tmp/scratch/RtmpzFfZCy’ ‘~/tmp/scratch/RtmpzPWShD’
‘~/tmp/scratch/RtmpzwpKUI’ ‘~/tmp/scratch/xvfb-run.0YdQTe’
‘~/tmp/scratch/xvfb-run.3dkJD2’ ‘~/tmp/scratch/xvfb-run.3wj7pA’
‘~/tmp/scratch/xvfb-run.4O5DNm’ ‘~/tmp/scratch/xvfb-run.60dSuG’
‘~/tmp/scratch/xvfb-run.7TY0tP’ ‘~/tmp/scratch/xvfb-run.8nIVhD’
‘~/tmp/scratch/xvfb-run.9xi2nO’ ‘~/tmp/scratch/xvfb-run.9ygXuL’
‘~/tmp/scratch/xvfb-run.Arusj2’ ‘~/tmp/scratch/xvfb-run.C6Ytal’
‘~/tmp/scratch/xvfb-run.DFwdO3’ ‘~/tmp/scratch/xvfb-run.DfdkaD’
‘~/tmp/scratch/xvfb-run.E33U5Z’ ‘~/tmp/scratch/xvfb-run.E8vgwx’
‘~/tmp/scratch/xvfb-run.Ew93A0’ ‘~/tmp/scratch/xvfb-run.FHHfC2’
‘~/tmp/scratch/xvfb-run.Fa2eGG’ ‘~/tmp/scratch/xvfb-run.G2dd5x’
‘~/tmp/scratch/xvfb-run.Hc57Gy’ ‘~/tmp/scratch/xvfb-run.JGlO5z’
‘~/tmp/scratch/xvfb-run.KG0Sxz’ ‘~/tmp/scratch/xvfb-run.KMJd4W’
‘~/tmp/scratch/xvfb-run.O9Cjey’ ‘~/tmp/scratch/xvfb-run.OUeJvo’
‘~/tmp/scratch/xvfb-run.QfcTmk’ ‘~/tmp/scratch/xvfb-run.QxeQhJ’
‘~/tmp/scratch/xvfb-run.TgyXYa’ ‘~/tmp/scratch/xvfb-run.WcPgto’
‘~/tmp/scratch/xvfb-run.Y5pHE5’ ‘~/tmp/scratch/xvfb-run.Y8tHa7’
‘~/tmp/scratch/xvfb-run.Zb1RYt’ ‘~/tmp/scratch/xvfb-run.bZrJ5l’
‘~/tmp/scratch/xvfb-run.cRc8Mv’ ‘~/tmp/scratch/xvfb-run.dRfgMf’
‘~/tmp/scratch/xvfb-run.dk1FC5’ ‘~/tmp/scratch/xvfb-run.dxJuSn’
‘~/tmp/scratch/xvfb-run.fFiwLA’ ‘~/tmp/scratch/xvfb-run.fzPYMu’
‘~/tmp/scratch/xvfb-run.g3iCnJ’ ‘~/tmp/scratch/xvfb-run.gvhmHo’
‘~/tmp/scratch/xvfb-run.imm8s9’ ‘~/tmp/scratch/xvfb-run.jfoEEG’
‘~/tmp/scratch/xvfb-run.kNq71b’ ‘~/tmp/scratch/xvfb-run.kx87Dm’
‘~/tmp/scratch/xvfb-run.mgsCMi’ ‘~/tmp/scratch/xvfb-run.nd2kLI’
‘~/tmp/scratch/xvfb-run.pNLkG5’ ‘~/tmp/scratch/xvfb-run.rGe1tj’
‘~/tmp/scratch/xvfb-run.rLRi13’ ‘~/tmp/scratch/xvfb-run.rRM2IJ’
‘~/tmp/scratch/xvfb-run.rhWRK1’ ‘~/tmp/scratch/xvfb-run.s90i9z’
‘~/tmp/scratch/xvfb-run.sO4QQU’ ‘~/tmp/scratch/xvfb-run.tIyxXq’
‘~/tmp/scratch/xvfb-run.tzQvIO’ ‘~/tmp/scratch/xvfb-run.usXYI3’
‘~/tmp/scratch/xvfb-run.vzAWFz’ ‘~/tmp/scratch/xvfb-run.w6SO5J’
‘~/tmp/scratch/xvfb-run.x6Juuv’ ‘~/tmp/scratch/xvfb-run.xU78Zp’
‘~/tmp/scratch/xvfb-run.yZbmpW’ ‘~/tmp/scratch/xvfb-run.yy6aCO’
‘~/tmp/scratch/xvfb-run.zEIzQM’ ‘~/tmp/scratch/xvfb-run.zUiWkt’
‘/dev/shm/sm_segment.gimli1.1001.14000000.0’
‘/dev/shm/sm_segment.gimli1.1001.25660000.0’
‘/dev/shm/sm_segment.gimli1.1001.30300000.0’
‘/dev/shm/sm_segment.gimli1.1001.46550000.0’
‘/dev/shm/sm_segment.gimli1.1001.4cfa0000.0’
‘/dev/shm/sm_segment.gimli1.1001.53050000.0’
‘/dev/shm/sm_segment.gimli1.1001.8aab0000.0’
‘/dev/shm/sm_segment.gimli1.1001.92110000.0’
‘/dev/shm/sm_segment.gimli1.1001.96d70000.0’
‘/dev/shm/sm_segment.gimli1.1001.9e630000.0’
‘/dev/shm/sm_segment.gimli1.1001.aeb10000.0’
‘/dev/shm/sm_segment.gimli1.1001.d4280000.0’
‘/dev/shm/sm_segment.gimli1.1001.db150000.0’
‘/dev/shm/sm_segment.gimli1.1001.e8d40000.0’
‘/dev/shm/sm_segment.gimli1.1001.ebad0000.0’
‘/dev/shm/sm_segment.gimli1.1001.ff240000.0’
‘~/.cache/pocl/uncached/tempfile_7xsFRy’
‘~/.cache/pocl/uncached/tempfile_AKHWFq’
‘~/.cache/pocl/uncached/tempfile_CagScd’
‘~/.cache/pocl/uncached/tempfile_MEioBg’
‘~/.cache/pocl/uncached/tempfile_OEkUJN’
‘~/.cache/pocl/uncached/tempfile_Ou6TKt’
‘~/.cache/pocl/uncached/tempfile_S5a2F3’
‘~/.cache/pocl/uncached/tempfile_TmmzWb’
‘~/.cache/pocl/uncached/tempfile_X983LK’
‘~/.cache/pocl/uncached/tempfile_b4LO2L’
‘~/.cache/pocl/uncached/tempfile_dSe4oC’
‘~/.cache/pocl/uncached/tempfile_lD4hjv’
‘~/.cache/pocl/uncached/tempfile_op4Kls’
‘~/.cache/pocl/uncached/tempfile_sRAsBf’
‘~/.cache/pocl/uncached/tempfile_uSXGZI’
‘~/.cache/pocl/uncached/tempfile_vWKIA1’
Flavor: r-devel-linux-x86_64-debian-gcc