CRAN Package Check Results for Package Renvlp

Last updated on 2024-05-23 19:54:57 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 3.4.5 20.78 197.62 218.40 NOTE
r-devel-linux-x86_64-debian-gcc 3.4.5 16.45 136.68 153.13 NOTE
r-devel-linux-x86_64-fedora-clang 3.4.5 250.87 NOTE
r-devel-linux-x86_64-fedora-gcc 3.4.5 232.83 NOTE
r-devel-windows-x86_64 3.4.5 17.00 157.00 174.00 NOTE
r-patched-linux-x86_64 3.4.5 19.83 183.92 203.75 NOTE
r-release-linux-x86_64 3.4.5 18.89 186.75 205.64 NOTE
r-release-macos-arm64 3.4.5 57.00 NOTE
r-release-windows-x86_64 3.4.5 16.00 156.00 172.00 NOTE
r-oldrel-macos-arm64 3.4.5 63.00 OK
r-oldrel-macos-x86_64 3.4.5 176.00 OK
r-oldrel-windows-x86_64 3.4.5 23.00 188.00 211.00 OK

Check Details

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-windows-x86_64