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.
       |                                                                                                                                            ^