Last updated on 2026-08-02 13:49:08 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.4 | 2.35 | 28.66 | 31.01 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 1.4 | 2.14 | 22.81 | 24.95 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 1.4 | 46.55 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 1.4 | 21.61 | NOTE | |||
| r-devel-windows-x86_64 | 1.4 | 4.00 | 52.00 | 56.00 | NOTE | |
| r-patched-linux-x86_64 | 1.4 | 2.29 | 26.67 | 28.96 | NOTE | |
| r-release-linux-x86_64 | 1.4 | 2.45 | 26.80 | 29.25 | NOTE | |
| r-release-macos-arm64 | 1.4 | 1.00 | 10.00 | 11.00 | NOTE | |
| r-release-macos-x86_64 | 1.4 | 2.00 | 36.00 | 38.00 | NOTE | |
| r-release-windows-x86_64 | 1.4 | 3.00 | 54.00 | 57.00 | NOTE | |
| r-oldrel-macos-arm64 | 1.4 | NOTE | ||||
| r-oldrel-macos-x86_64 | 1.4 | 2.00 | 30.00 | 32.00 | NOTE | |
| r-oldrel-windows-x86_64 | 1.4 | 4.00 | 56.00 | 60.00 | NOTE |
Version: 1.4
Check: CRAN incoming feasibility
Result: NOTE
Maintainer: ‘Przemyslaw Biecek <przemyslaw.biecek@gmail.com>’
No Authors@R field in DESCRIPTION.
Please add one, modifying
Authors@R: c(person(given = "Przemyslaw",
family = "Biecek",
role = c("aut", "cre"),
email = "przemyslaw.biecek@gmail.com",
comment = "R code"),
person(given = "Teresa",
family = "Ledwina",
role = "aut",
comment = "support,\n descriptions"))
as necessary.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 1.4
Check: R code for possible problems
Result: NOTE
Found calls to structure() using deprecated special names:
ddst/R/ddst.exp.test.R (.Dim: 20)
ddst/R/ddst.extr.test.R (.Dim: 41)
ddst/R/ddst.norm.test.R (.Dim: 40)
ddst/R/zzz.R (.Dim: 1, .Dimnames: 1)
'.Dim' should be changed to 'dim'.
'.Dimnames' should be changed to 'dimnames'.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64
Version: 1.4
Check: Rd files
Result: NOTE
checkRd: (-1) ddst-package.Rd:29: Lost braces; missing escapes or markup?
29 | \emph{$W_k=[1/sqrt(n) sum_{i=1}^n l(Z_i)]I^{-1}[1/sqrt(n) sum_{i=1}^n l(Z_i)]'$},
| ^
checkRd: (-1) ddst-package.Rd:29: Lost braces; missing escapes or markup?
29 | \emph{$W_k=[1/sqrt(n) sum_{i=1}^n l(Z_i)]I^{-1}[1/sqrt(n) sum_{i=1}^n l(Z_i)]'$},
| ^
checkRd: (-1) ddst-package.Rd:29: Lost braces; missing escapes or markup?
29 | \emph{$W_k=[1/sqrt(n) sum_{i=1}^n l(Z_i)]I^{-1}[1/sqrt(n) sum_{i=1}^n l(Z_i)]'$},
| ^
checkRd: (-1) ddst-package.Rd:31: Lost braces; missing escapes or markup?
31 | where \emph{$l(Z_i)$}, i=1,...,n, is \emph{k}-dimensional (row) score vector, the symbol \emph{'} denotes transposition while \emph{$I=Cov_{theta_0}[l(Z_1)]'[l(Z_1)]$}. Following Neyman's idea of modelling underlying distributions one gets \emph{$l(Z_i)=(phi_1(F(Z_i)),...,phi_k(F(Z_i)))$} and \emph{I} being the identity matrix, where \emph{$phi_j$}'s, j >= 1, are zero mean orthonormal functions on [0,1], while \emph{F} is the completely specified null distribution function.
| ^
checkRd: (-1) ddst-package.Rd:35: Lost braces; missing escapes or markup?
35 | \emph{$W_k^{*}(tilde gamma)=[1/sqrt(n) sum_{i=1}^n l^*(Z_i;tilde gamma)][I^*(tilde gamma)]^{-1}[1/sqrt(n) sum_{i=1}^n l^*(Z_i;tilde gamma)]'$},
| ^
checkRd: (-1) ddst-package.Rd:35: Lost braces; missing escapes or markup?
35 | \emph{$W_k^{*}(tilde gamma)=[1/sqrt(n) sum_{i=1}^n l^*(Z_i;tilde gamma)][I^*(tilde gamma)]^{-1}[1/sqrt(n) sum_{i=1}^n l^*(Z_i;tilde gamma)]'$},
| ^
checkRd: (-1) ddst-package.Rd:35: Lost braces; missing escapes or markup?
35 | \emph{$W_k^{*}(tilde gamma)=[1/sqrt(n) sum_{i=1}^n l^*(Z_i;tilde gamma)][I^*(tilde gamma)]^{-1}[1/sqrt(n) sum_{i=1}^n l^*(Z_i;tilde gamma)]'$},
| ^
checkRd: (-1) ddst-package.Rd:35: Lost braces; missing escapes or markup?
35 | \emph{$W_k^{*}(tilde gamma)=[1/sqrt(n) sum_{i=1}^n l^*(Z_i;tilde gamma)][I^*(tilde gamma)]^{-1}[1/sqrt(n) sum_{i=1}^n l^*(Z_i;tilde gamma)]'$},
| ^
checkRd: (-1) ddst-package.Rd:36: Lost braces; missing escapes or markup?
36 | where \emph{$tilde gamma$} is an appropriate estimator of \emph{$gamma$} while \emph{$I^*(gamma)=Cov_{theta_0}[l^*(Z_1;gamma)]'[l^*(Z_1;gamma)]$}. More details can be found in Janic and Ledwina (2008), Kallenberg and Ledwina (1997 a,b) as well as Inglot and Ledwina (2006 a,b).
| ^
checkRd: (-1) ddst-package.Rd:40: Lost braces
40 | \emph{$T = min{1 <= k <= d: W_k-pi(k,n,c) >= W_j-pi(j,n,c), j=1,...,d}$}
| ^
checkRd: (-1) ddst-package.Rd:45: Lost braces
45 | $T^* = min{1 <= k <= d: W_k^*(tilde gamma)-pi^*(k,n,c) >= W_j^*(tilde gamma)-pi^*(j,n,c), j=1,...,d}$}.
| ^
checkRd: (-1) ddst-package.Rd:49: Lost braces
49 | \emph{$pi(j,n,c)={jlog n, if max{1 <= k <= d}|Y_k| <= sqrt(c log(n)), 2j, if max{1 <= k <= d}|Y_k|>sqrt(c log(n)). }$}
| ^
checkRd: (-1) ddst-package.Rd:49: Lost braces
49 | \emph{$pi(j,n,c)={jlog n, if max{1 <= k <= d}|Y_k| <= sqrt(c log(n)), 2j, if max{1 <= k <= d}|Y_k|>sqrt(c log(n)). }$}
| ^
checkRd: (-1) ddst-package.Rd:49: Lost braces
49 | \emph{$pi(j,n,c)={jlog n, if max{1 <= k <= d}|Y_k| <= sqrt(c log(n)), 2j, if max{1 <= k <= d}|Y_k|>sqrt(c log(n)). }$}
| ^
checkRd: (-1) ddst-package.Rd:54: Lost braces
54 | $pi^*(j,n,c)={jlog n, if max{1 <= k <= d}|Y_k^*| <= sqrt(c log(n)),2j if max(1 <= k <= d)|Y_k^*| > sqrt(c log(n))}$}.
| ^
checkRd: (-1) ddst-package.Rd:54: Lost braces
54 | $pi^*(j,n,c)={jlog n, if max{1 <= k <= d}|Y_k^*| <= sqrt(c log(n)),2j if max(1 <= k <= d)|Y_k^*| > sqrt(c log(n))}$}.
| ^
checkRd: (-1) ddst-package.Rd:58: Lost braces; missing escapes or markup?
58 | \emph{$(Y_1,...,Y_k)=[1/sqrt(n) sum_{i=1}^n l(Z_i)]I^{-1/2}$}
| ^
checkRd: (-1) ddst-package.Rd:58: Lost braces; missing escapes or markup?
58 | \emph{$(Y_1,...,Y_k)=[1/sqrt(n) sum_{i=1}^n l(Z_i)]I^{-1/2}$}
| ^
checkRd: (-1) ddst-package.Rd:62: Lost braces; missing escapes or markup?
62 | \emph{$(Y_1^*,...,Y_k^*)=[1/sqrt(n) sum_{i=1}^n l^*(Z_i; tilde gamma)][I^*(tilde gamma)]^{-1/2}$}.
| ^
checkRd: (-1) ddst-package.Rd:62: Lost braces; missing escapes or markup?
62 | \emph{$(Y_1^*,...,Y_k^*)=[1/sqrt(n) sum_{i=1}^n l^*(Z_i; tilde gamma)][I^*(tilde gamma)]^{-1/2}$}.
| ^
checkRd: (-1) ddst-package.Rd:65: Lost braces; missing escapes or markup?
65 | and \emph{$W_{T^*} = W_{T^*}(tilde gamma)$}, respectively. For details see Inglot and Ledwina (2006 a,b,c).
| ^
checkRd: (-1) ddst-package.Rd:65: Lost braces; missing escapes or markup?
65 | and \emph{$W_{T^*} = W_{T^*}(tilde gamma)$}, respectively. For details see Inglot and Ledwina (2006 a,b,c).
| ^
checkRd: (-1) ddst-package.Rd:67: Lost braces; missing escapes or markup?
67 | The choice of \emph{c} in \emph{T} and \emph{$T^*$} is decisive to finite sample behaviour of the selection rules and pertaining statistics \emph{$W_T$} and \emph{$W_{T^*}(tilde gamma)$}. In particular, under large \emph{c}'s the rules behave similarly as Schwarz's (1978) BIC while for \emph{c=0} they mimic Akaike's (1973) AIC. For moderate sample sizes, values \emph{c in (2,2.5)} guarantee, under `smooth' departures, only slightly smaller power as in case BIC were used and simultaneously give much higher power than BIC under multimodal alternatives. In genral, large \emph{c's} are recommended if changes in location, scale, skewness and kurtosis are in principle aimed to be detected. For evidence and discussion see Inglot and Ledwina (2006 c).
| ^
checkRd: (-1) ddst-package.Rd:69: Lost braces; missing escapes or markup?
69 | It \emph{c>0} then the limiting null distribution of \emph{$W_T$} and \emph{$W_{T^*}(tilde gamma)$} is central chi-squared with one degree of freedom. In our implementation, for given \emph{n}, both critical values and \emph{p}-values are computed by MC method.
| ^
checkRd: (-1) ddst-package.Rd:71: Lost braces; missing escapes or markup?
71 | Empirical distributions of \emph{T} and \emph{$T^*$} as well as \emph{$W_T$} and \emph{$W_{T^*}(tilde gamma)$} are not essentially influenced by the choice of reasonably large \emph{d}'s, provided that sample size is at least moderate.
| ^
checkRd: (-1) ddst.exp.test.Rd:27: Lost braces; missing escapes or markup?
27 | Modelling alternatives similarly as in Kallenberg and Ledwina (1997 a,b), e.g., and estimating \emph{$gamma$} by \emph{$tilde gamma= 1/n sum_{i=1}^n Z_i$} yields the efficient score
| ^
checkRd: (-1) ddst.exp.test.Rd:30: Lost braces; missing escapes or markup?
30 | The matrix \emph{$[I^*(tilde gamma)]^{-1}$} does not depend on \emph{$tilde gamma$} and is calculated for succeding dimensions \emph{k} using some recurrent relations for Legendre's polynomials and computed in a numerical way in case of cosine basis. In the implementation the default value of \emph{c} in \emph{$T^*$} is set to be 100.
| ^
checkRd: (-1) ddst.extr.test.Rd:29: Lost braces; missing escapes or markup?
29 | \emph{$gamma=(gamma_1,gamma_2)$} is estimated by \emph{$tilde gamma=(tilde gamma_1,tilde gamma_2)$}, where \emph{$tilde gamma_1=-1/n sum_{i=1}^n Z_i + varepsilon G$}, where \emph{$varepsilon approx 0.577216 $} is the Euler constant and \emph{$ G = tilde gamma_2 = [n(n-1) ln2]^{-1}sum_{1<= j < i <= n}(Z_{n:i}^o - Z_{n:j}^o) $} while \emph{$Z_{n:1}^o <= ... <= Z_{n:n}^o$}
| ^
checkRd: (-1) ddst.extr.test.Rd:29: Lost braces; missing escapes or markup?
29 | \emph{$gamma=(gamma_1,gamma_2)$} is estimated by \emph{$tilde gamma=(tilde gamma_1,tilde gamma_2)$}, where \emph{$tilde gamma_1=-1/n sum_{i=1}^n Z_i + varepsilon G$}, where \emph{$varepsilon approx 0.577216 $} is the Euler constant and \emph{$ G = tilde gamma_2 = [n(n-1) ln2]^{-1}sum_{1<= j < i <= n}(Z_{n:i}^o - Z_{n:j}^o) $} while \emph{$Z_{n:1}^o <= ... <= Z_{n:n}^o$}
| ^
checkRd: (-1) ddst.extr.test.Rd:29: Lost braces; missing escapes or markup?
29 | \emph{$gamma=(gamma_1,gamma_2)$} is estimated by \emph{$tilde gamma=(tilde gamma_1,tilde gamma_2)$}, where \emph{$tilde gamma_1=-1/n sum_{i=1}^n Z_i + varepsilon G$}, where \emph{$varepsilon approx 0.577216 $} is the Euler constant and \emph{$ G = tilde gamma_2 = [n(n-1) ln2]^{-1}sum_{1<= j < i <= n}(Z_{n:i}^o - Z_{n:j}^o) $} while \emph{$Z_{n:1}^o <= ... <= Z_{n:n}^o$}
| ^
checkRd: (-1) ddst.extr.test.Rd:29: Lost braces; missing escapes or markup?
29 | \emph{$gamma=(gamma_1,gamma_2)$} is estimated by \emph{$tilde gamma=(tilde gamma_1,tilde gamma_2)$}, where \emph{$tilde gamma_1=-1/n sum_{i=1}^n Z_i + varepsilon G$}, where \emph{$varepsilon approx 0.577216 $} is the Euler constant and \emph{$ G = tilde gamma_2 = [n(n-1) ln2]^{-1}sum_{1<= j < i <= n}(Z_{n:i}^o - Z_{n:j}^o) $} while \emph{$Z_{n:1}^o <= ... <= Z_{n:n}^o$}
| ^
checkRd: (-1) ddst.extr.test.Rd:29: Lost braces; missing escapes or markup?
29 | \emph{$gamma=(gamma_1,gamma_2)$} is estimated by \emph{$tilde gamma=(tilde gamma_1,tilde gamma_2)$}, where \emph{$tilde gamma_1=-1/n sum_{i=1}^n Z_i + varepsilon G$}, where \emph{$varepsilon approx 0.577216 $} is the Euler constant and \emph{$ G = tilde gamma_2 = [n(n-1) ln2]^{-1}sum_{1<= j < i <= n}(Z_{n:i}^o - Z_{n:j}^o) $} while \emph{$Z_{n:1}^o <= ... <= Z_{n:n}^o$}
| ^
checkRd: (-1) ddst.extr.test.Rd:29: Lost braces; missing escapes or markup?
29 | \emph{$gamma=(gamma_1,gamma_2)$} is estimated by \emph{$tilde gamma=(tilde gamma_1,tilde gamma_2)$}, where \emph{$tilde gamma_1=-1/n sum_{i=1}^n Z_i + varepsilon G$}, where \emph{$varepsilon approx 0.577216 $} is the Euler constant and \emph{$ G = tilde gamma_2 = [n(n-1) ln2]^{-1}sum_{1<= j < i <= n}(Z_{n:i}^o - Z_{n:j}^o) $} while \emph{$Z_{n:1}^o <= ... <= Z_{n:n}^o$}
| ^
checkRd: (-1) ddst.extr.test.Rd:29: Lost braces; missing escapes or markup?
29 | \emph{$gamma=(gamma_1,gamma_2)$} is estimated by \emph{$tilde gamma=(tilde gamma_1,tilde gamma_2)$}, where \emph{$tilde gamma_1=-1/n sum_{i=1}^n Z_i + varepsilon G$}, where \emph{$varepsilon approx 0.577216 $} is the Euler constant and \emph{$ G = tilde gamma_2 = [n(n-1) ln2]^{-1}sum_{1<= j < i <= n}(Z_{n:i}^o - Z_{n:j}^o) $} while \emph{$Z_{n:1}^o <= ... <= Z_{n:n}^o$}
| ^
checkRd: (-1) ddst.extr.test.Rd:33: Lost braces; missing escapes or markup?
33 | The related matrix \emph{$[I^*(tilde gamma)]^{-1}$} does not depend on \emph{$tilde gamma$} and is calculated for succeding dimensions \emph{k} using some recurrent relations for Legendre's polynomials and numerical methods for cosine functions. In the implementation the default value of \emph{c} in \emph{$T^*$} was fixed to be 100. Hence, \emph{$T^*$} is Schwarz-type model selection rule. The resulting data driven test statistic for extreme value distribution is \emph{$W_{T^*}=W_{T^*}(tilde gamma)$}.
| ^
checkRd: (-1) ddst.extr.test.Rd:33: Lost braces; missing escapes or markup?
33 | The related matrix \emph{$[I^*(tilde gamma)]^{-1}$} does not depend on \emph{$tilde gamma$} and is calculated for succeding dimensions \emph{k} using some recurrent relations for Legendre's polynomials and numerical methods for cosine functions. In the implementation the default value of \emph{c} in \emph{$T^*$} was fixed to be 100. Hence, \emph{$T^*$} is Schwarz-type model selection rule. The resulting data driven test statistic for extreme value distribution is \emph{$W_{T^*}=W_{T^*}(tilde gamma)$}.
| ^
checkRd: (-1) ddst.extr.test.Rd:33: Lost braces; missing escapes or markup?
33 | The related matrix \emph{$[I^*(tilde gamma)]^{-1}$} does not depend on \emph{$tilde gamma$} and is calculated for succeding dimensions \emph{k} using some recurrent relations for Legendre's polynomials and numerical methods for cosine functions. In the implementation the default value of \emph{c} in \emph{$T^*$} was fixed to be 100. Hence, \emph{$T^*$} is Schwarz-type model selection rule. The resulting data driven test statistic for extreme value distribution is \emph{$W_{T^*}=W_{T^*}(tilde gamma)$}.
| ^
checkRd: (-1) ddst.norm.test.Rd:30: Lost braces; missing escapes or markup?
30 | \emph{$gamma=(gamma_1,gamma_2)$} is estimated by \emph{$tilde gamma=(tilde gamma_1,tilde gamma_2)$}, where \emph{$tilde gamma_1=1/n sum_{i=1}^n Z_i$} and
| ^
checkRd: (-1) ddst.norm.test.Rd:31: Lost braces; missing escapes or markup?
31 | \emph{$tilde gamma_2 = 1/(n-1) sum_{i=1}^{n-1}(Z_{n:i+1}-Z_{n:i})(H_{i+1}-H_i)$},
| ^
checkRd: (-1) ddst.norm.test.Rd:31: Lost braces; missing escapes or markup?
31 | \emph{$tilde gamma_2 = 1/(n-1) sum_{i=1}^{n-1}(Z_{n:i+1}-Z_{n:i})(H_{i+1}-H_i)$},
| ^
checkRd: (-1) ddst.norm.test.Rd:31: Lost braces; missing escapes or markup?
31 | \emph{$tilde gamma_2 = 1/(n-1) sum_{i=1}^{n-1}(Z_{n:i+1}-Z_{n:i})(H_{i+1}-H_i)$},
| ^
checkRd: (-1) ddst.norm.test.Rd:31: Lost braces; missing escapes or markup?
31 | \emph{$tilde gamma_2 = 1/(n-1) sum_{i=1}^{n-1}(Z_{n:i+1}-Z_{n:i})(H_{i+1}-H_i)$},
| ^
checkRd: (-1) ddst.norm.test.Rd:31: Lost braces; missing escapes or markup?
31 | \emph{$tilde gamma_2 = 1/(n-1) sum_{i=1}^{n-1}(Z_{n:i+1}-Z_{n:i})(H_{i+1}-H_i)$},
| ^
checkRd: (-1) ddst.norm.test.Rd:32: Lost braces; missing escapes or markup?
32 | while \emph{$Z_{n:1}<= ... <= Z_{n:n}$} are ordered values of \emph{$Z_1, ..., Z_n$} and \emph{$H_i= phi^{-1}((i-3/8)(n+1/4))$}, cf. Chen and Shapiro (1995).
| ^
checkRd: (-1) ddst.norm.test.Rd:32: Lost braces; missing escapes or markup?
32 | while \emph{$Z_{n:1}<= ... <= Z_{n:n}$} are ordered values of \emph{$Z_1, ..., Z_n$} and \emph{$H_i= phi^{-1}((i-3/8)(n+1/4))$}, cf. Chen and Shapiro (1995).
| ^
checkRd: (-1) ddst.norm.test.Rd:32: Lost braces; missing escapes or markup?
32 | while \emph{$Z_{n:1}<= ... <= Z_{n:n}$} are ordered values of \emph{$Z_1, ..., Z_n$} and \emph{$H_i= phi^{-1}((i-3/8)(n+1/4))$}, cf. Chen and Shapiro (1995).
| ^
checkRd: (-1) ddst.norm.test.Rd:35: Lost braces; missing escapes or markup?
35 | The pertaining matrix \emph{$[I^*(tilde gamma)]^{-1}$} does not depend on \emph{$tilde gamma$} and is calculated for succeding dimensions \emph{k} using some recurrent relations for Legendre's polynomials and is computed in a numerical way in case of cosine basis. In the implementation of \emph{$T^*$} the default value of \emph{c} is set to be 100. Therefore, in practice, \emph{$T^*$} is Schwarz-type criterion. See Inglot and Ledwina (2006) as well as Janic and Ledwina (2008) for comments. The resulting data driven test statistic for normality is \emph{$W_{T^*}=W_{T^*}(tilde gamma)$}.
| ^
checkRd: (-1) ddst.norm.test.Rd:35: Lost braces; missing escapes or markup?
35 | The pertaining matrix \emph{$[I^*(tilde gamma)]^{-1}$} does not depend on \emph{$tilde gamma$} and is calculated for succeding dimensions \emph{k} using some recurrent relations for Legendre's polynomials and is computed in a numerical way in case of cosine basis. In the implementation of \emph{$T^*$} the default value of \emph{c} is set to be 100. Therefore, in practice, \emph{$T^*$} is Schwarz-type criterion. See Inglot and Ledwina (2006) as well as Janic and Ledwina (2008) for comments. The resulting data driven test statistic for normality is \emph{$W_{T^*}=W_{T^*}(tilde gamma)$}.
| ^
checkRd: (-1) ddst.norm.test.Rd:35: Lost braces; missing escapes or markup?
35 | The pertaining matrix \emph{$[I^*(tilde gamma)]^{-1}$} does not depend on \emph{$tilde gamma$} and is calculated for succeding dimensions \emph{k} using some recurrent relations for Legendre's polynomials and is computed in a numerical way in case of cosine basis. In the implementation of \emph{$T^*$} the default value of \emph{c} is set to be 100. Therefore, in practice, \emph{$T^*$} is Schwarz-type criterion. See Inglot and Ledwina (2006) as well as Janic and Ledwina (2008) for comments. The resulting data driven test statistic for normality is \emph{$W_{T^*}=W_{T^*}(tilde gamma)$}.
| ^
checkRd: (-1) ddst.uniform.test.Rd:25: Lost braces; missing escapes or markup?
25 | $W_k=[1/sqrt(n) sum_{j=1}^k sum_{i=1}^n phi_j(Z_i)]^2$},
| ^
checkRd: (-1) ddst.uniform.test.Rd:25: Lost braces; missing escapes or markup?
25 | $W_k=[1/sqrt(n) sum_{j=1}^k sum_{i=1}^n phi_j(Z_i)]^2$},
| ^
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: 1.4
Check: for new files in some other directories
Result: NOTE
Found the following files/directories:
‘~/tmp/scratch/Rtmp0Bjdeh’ ‘~/tmp/scratch/Rtmp32w9uj’
‘~/tmp/scratch/Rtmp3Qhebx’ ‘~/tmp/scratch/Rtmp48yRVR’
‘~/tmp/scratch/Rtmp6ggJyB’ ‘~/tmp/scratch/Rtmp6nlsRm’
‘~/tmp/scratch/Rtmp6sFKD9’ ‘~/tmp/scratch/Rtmp6zwB8V’
‘~/tmp/scratch/Rtmp7alnw9’ ‘~/tmp/scratch/Rtmp86v15x’
‘~/tmp/scratch/Rtmp86wbr1’ ‘~/tmp/scratch/Rtmp8Ldd1x’
‘~/tmp/scratch/Rtmp8NriCC’ ‘~/tmp/scratch/Rtmp8cIpVC’
‘~/tmp/scratch/Rtmp9NaDqp’ ‘~/tmp/scratch/Rtmp9faUjG’
‘~/tmp/scratch/RtmpAsWrlu’ ‘~/tmp/scratch/RtmpCo5ueW’
‘~/tmp/scratch/RtmpEc4BtG’ ‘~/tmp/scratch/RtmpEhj2gZ’
‘~/tmp/scratch/RtmpExTbWv’ ‘~/tmp/scratch/RtmpFFSRxE’
‘~/tmp/scratch/RtmpGJQgok’ ‘~/tmp/scratch/RtmpGeZ5Ap’
‘~/tmp/scratch/RtmpI7OiYy’ ‘~/tmp/scratch/RtmpKIZot9’
‘~/tmp/scratch/RtmpKLltDe’ ‘~/tmp/scratch/RtmpKsi55i’
‘~/tmp/scratch/RtmpL6nqBI’ ‘~/tmp/scratch/RtmpLNBEJ7’
‘~/tmp/scratch/RtmpLPa5RV’ ‘~/tmp/scratch/RtmpLz0dSS’
‘~/tmp/scratch/RtmpMVetbh’ ‘~/tmp/scratch/RtmpNPnuqT’
‘~/tmp/scratch/RtmpNsbEkA’ ‘~/tmp/scratch/RtmpO8qA6E’
‘~/tmp/scratch/RtmpO8qtGa’ ‘~/tmp/scratch/RtmpOhNvp8’
‘~/tmp/scratch/RtmpOpk9Qr’ ‘~/tmp/scratch/RtmpPADEhc’
‘~/tmp/scratch/RtmpQUZNWV’ ‘~/tmp/scratch/RtmpQVyoJM’
‘~/tmp/scratch/RtmpQbqOOL’ ‘~/tmp/scratch/RtmpREG5nI’
‘~/tmp/scratch/RtmpRMQDtZ’ ‘~/tmp/scratch/RtmpT0Vf27’
‘~/tmp/scratch/RtmpT1JWRv’ ‘~/tmp/scratch/RtmpTMqbaW’
‘~/tmp/scratch/RtmpTllE55’ ‘~/tmp/scratch/RtmpTnVGjI’
‘~/tmp/scratch/RtmpTnpzFR’ ‘~/tmp/scratch/RtmpUwHTVv’
‘~/tmp/scratch/RtmpV8vEbu’ ‘~/tmp/scratch/RtmpVtuvrf’
‘~/tmp/scratch/RtmpVuJEzK’ ‘~/tmp/scratch/RtmpWAwkT3’
‘~/tmp/scratch/RtmpWiLapc’ ‘~/tmp/scratch/RtmpWvQbZV’
‘~/tmp/scratch/RtmpXKwYqd’ ‘~/tmp/scratch/RtmpXo9Bg3’
‘~/tmp/scratch/RtmpYCQm3L’ ‘~/tmp/scratch/RtmpYDNbJs’
‘~/tmp/scratch/RtmpYiUhBE’ ‘~/tmp/scratch/RtmpYkBiB6’
‘~/tmp/scratch/RtmpYx39Uw’ ‘~/tmp/scratch/Rtmpa6vGpW’
‘~/tmp/scratch/Rtmpaw0p9r’ ‘~/tmp/scratch/Rtmpayk2u3’
‘~/tmp/scratch/RtmpbpBJdP’ ‘~/tmp/scratch/RtmpdFQau0’
‘~/tmp/scratch/RtmpeL2Rr7’ ‘~/tmp/scratch/RtmpgCLiho’
‘~/tmp/scratch/RtmpghofzT’ ‘~/tmp/scratch/RtmphVPBid’
‘~/tmp/scratch/Rtmpi5x8L9’ ‘~/tmp/scratch/Rtmpib45ku’
‘~/tmp/scratch/RtmpihSHxc’ ‘~/tmp/scratch/RtmpjQbFaR’
‘~/tmp/scratch/RtmpkbBIvR’ ‘~/tmp/scratch/RtmpkstY4A’
‘~/tmp/scratch/RtmplMPo0s’ ‘~/tmp/scratch/RtmplNE0ky’
‘~/tmp/scratch/RtmplhHEY0’ ‘~/tmp/scratch/RtmpmKn0Df’
‘~/tmp/scratch/RtmpmZPU3h’ ‘~/tmp/scratch/RtmpmoXGHk’
‘~/tmp/scratch/RtmpmuSBDf’ ‘~/tmp/scratch/RtmpnG6ok9’
‘~/tmp/scratch/RtmpnOciHe’ ‘~/tmp/scratch/Rtmpnka5Fp’
‘~/tmp/scratch/RtmpocYcdL’ ‘~/tmp/scratch/RtmpqCEax2’
‘~/tmp/scratch/RtmprJeVSV’ ‘~/tmp/scratch/RtmptUCSEb’
‘~/tmp/scratch/RtmptxHbP7’ ‘~/tmp/scratch/RtmpwYmDGE’
‘~/tmp/scratch/RtmpxAynqy’ ‘~/tmp/scratch/RtmpxNg5Gq’
‘~/tmp/scratch/RtmpyDmdyB’ ‘~/tmp/scratch/RtmpyKFaav’
‘~/tmp/scratch/RtmpyUWmd9’ ‘~/tmp/scratch/RtmpyXGvfQ’
‘~/tmp/scratch/Rtmpz4hTvG’ ‘~/tmp/scratch/RtmpzP3IK6’
‘~/tmp/scratch/RtmpzxrcXF’ ‘~/tmp/scratch/ccdRrvcm.s’
‘~/tmp/scratch/xvfb-run.4BpU35’ ‘~/tmp/scratch/xvfb-run.4G2vuU’
‘~/tmp/scratch/xvfb-run.5RWoJ6’ ‘~/tmp/scratch/xvfb-run.DoVgel’
‘~/tmp/scratch/xvfb-run.ET7S64’ ‘~/tmp/scratch/xvfb-run.EwtRRh’
‘~/tmp/scratch/xvfb-run.HDs8YZ’ ‘~/tmp/scratch/xvfb-run.JJZTTt’
‘~/tmp/scratch/xvfb-run.OJKFg6’ ‘~/tmp/scratch/xvfb-run.OlpB5e’
‘~/tmp/scratch/xvfb-run.PqiTsr’ ‘~/tmp/scratch/xvfb-run.QPVvl2’
‘~/tmp/scratch/xvfb-run.Qb9DP8’ ‘~/tmp/scratch/xvfb-run.TrHJVi’
‘~/tmp/scratch/xvfb-run.V2hvI2’ ‘~/tmp/scratch/xvfb-run.XXymh3’
‘~/tmp/scratch/xvfb-run.bT5Epm’ ‘~/tmp/scratch/xvfb-run.blK35r’
‘~/tmp/scratch/xvfb-run.czlovt’ ‘~/tmp/scratch/xvfb-run.dMrWEJ’
‘~/tmp/scratch/xvfb-run.doRvyh’ ‘~/tmp/scratch/xvfb-run.fw4QLe’
‘~/tmp/scratch/xvfb-run.ix8jYz’ ‘~/tmp/scratch/xvfb-run.juk8i7’
‘~/tmp/scratch/xvfb-run.kHljGJ’ ‘~/tmp/scratch/xvfb-run.nyJh26’
‘~/tmp/scratch/xvfb-run.ooP43h’ ‘~/tmp/scratch/xvfb-run.qwkFIZ’
‘~/tmp/scratch/xvfb-run.wIV6iQ’ ‘~/tmp/scratch/xvfb-run.zttJjc’
Flavor: r-devel-linux-x86_64-debian-gcc