CRAN Package Check Results for Package asht

Last updated on 2024-04-15 05:55:09 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0.1 15.24 125.02 140.26 NOTE
r-devel-linux-x86_64-debian-gcc 1.0.1 13.05 90.61 103.66 NOTE
r-devel-linux-x86_64-fedora-clang 1.0.1 175.35 NOTE
r-devel-linux-x86_64-fedora-gcc 1.0.1 171.14 NOTE
r-devel-windows-x86_64 1.0.1 13.00 107.00 120.00 NOTE
r-patched-linux-x86_64 1.0.1 20.18 119.31 139.49 NOTE
r-release-linux-x86_64 1.0.1 10.39 122.89 133.28 OK
r-release-macos-arm64 1.0.1 53.00 OK
r-release-macos-x86_64 1.0.1 112.00 OK
r-release-windows-x86_64 1.0.1 20.00 132.00 152.00 OK
r-oldrel-macos-arm64 1.0.1 55.00 OK
r-oldrel-windows-x86_64 1.0.1 19.00 136.00 155.00 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