CRAN Package Check Results for Package asht

Last updated on 2024-05-24 07:00:13 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0.1 18.24 128.27 146.51 NOTE
r-devel-linux-x86_64-debian-gcc 1.0.1 11.72 91.73 103.45 NOTE
r-devel-linux-x86_64-fedora-clang 1.0.1 185.47 NOTE
r-devel-linux-x86_64-fedora-gcc 1.0.1 167.59 NOTE
r-devel-windows-x86_64 1.0.1 13.00 106.00 119.00 NOTE
r-patched-linux-x86_64 1.0.1 21.45 119.48 140.93 NOTE
r-release-linux-x86_64 1.0.1 10.17 117.65 127.82 NOTE
r-release-macos-arm64 1.0.1 46.00 NOTE
r-release-windows-x86_64 1.0.1 13.00 108.00 121.00 NOTE
r-oldrel-macos-arm64 1.0.1 53.00 OK
r-oldrel-macos-x86_64 1.0.1 112.00 OK
r-oldrel-windows-x86_64 1.0.1 19.00 127.00 146.00 OK

Check Details

Version: 1.0.1
Check: Rd files
Result: NOTE checkRd: (-1) signTest.Rd:29: Lost braces 29 | code{stat="ud"} gives 0, and \code{stat="cpp"} gives 0.5). | ^ checkRd: (-1) simulateSS.Rd:46: Lost braces; missing escapes or markup? 46 | and the total number of batches, $b_{tot}$. | ^ checkRd: (-1) simulateSS.Rd:48: Lost braces; missing escapes or markup? 48 | Then we use a normal approximation to estimate the target sample size, say $N_{norm}$. In step 2, we replicate $m$ data sets with sample size $N_2 = N_{norm}$ | ^ checkRd: (-1) simulateSS.Rd:48: Lost braces; missing escapes or markup? 48 | Then we use a normal approximation to estimate the target sample size, say $N_{norm}$. In step 2, we replicate $m$ data sets with sample size $N_2 = N_{norm}$ | ^ checkRd: (-1) simulateSS.Rd:49: Lost braces; missing escapes or markup? 49 | to get the associated proportion of rejections, say $P_2$. We repeat 2 more batches with $N_3=N_{norm}/2$ and $N_4=2 N_{norm}$, | ^ checkRd: (-1) simulateSS.Rd:49: Lost braces; missing escapes or markup? 49 | to get the associated proportion of rejections, say $P_2$. We repeat 2 more batches with $N_3=N_{norm}/2$ and $N_4=2 N_{norm}$, | ^ checkRd: (-1) simulateSS.Rd:51: Lost braces; missing escapes or markup? 51 | the sample size at the target power, $N_{target}$. We use that estimate of $N_{target}$ for our sample size for the next | ^ checkRd: (-1) simulateSS.Rd:51: Lost braces; missing escapes or markup? 51 | the sample size at the target power, $N_{target}$. We use that estimate of $N_{target}$ for our sample size for the next | ^ checkRd: (-1) simulateSS.Rd:53: Lost braces; missing escapes or markup? 53 | Wu (1985, see Section 3). Step 4 is iterative, for the $i$th batch we repeat the isotonic regression, except now with $N_i$ estimated from the first $(i-1)$ observation pairs. We repeat step 4 until either the number of batches is $b_{tot}$, | ^ checkRd: (-1) simulateSS.Rd:56: Lost braces; missing escapes or markup? 56 | current sample size estimate, otherwise add 1. Continue with that up-and-down-like method until we reach the number of batches equal to $b_{tot}$. The up-and-down-like method was added because sometimes the algorithm would get stuck in too large of a sample size estimate. | ^ 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