checkRd: (-1) inspect.Rd:35: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) inspect.Rd:36: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) single.change.Rd:25: Lost braces; missing escapes or markup?
    25 | \item{noise}{Noise structure of the multivarite time series. For noise = 0, 0.5, 1, columns of W have independent multivariate normal distribution with covariance matrix Sigma. When noise = 0, Sigma = sigma^2 * I_p; when noise = 0.5, noise has local dependence structure given by Sigma_{i,j} = sigma*corr^|i-j|; when noise = 1, noise has global dependence structure given by matrix(corr,p,p)+diag(p)*(1-corr))) * sigma. When noise = 2, rows of the W are independent and each having an AR(1) structure given by W_{j,t} = W_{j,t-1} * sqrt(corr) + rnorm(sd = sigma) * sqrt(1-corr). For noise = 3, 4, entries of W have i.i.d. uniform distribution and exponential distribution respectively, each centred and rescaled to have zero mean and variance sigma^2.}
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checkRd: (-1) single.change.Rd:25: Lost braces; missing escapes or markup?
    25 | \item{noise}{Noise structure of the multivarite time series. For noise = 0, 0.5, 1, columns of W have independent multivariate normal distribution with covariance matrix Sigma. When noise = 0, Sigma = sigma^2 * I_p; when noise = 0.5, noise has local dependence structure given by Sigma_{i,j} = sigma*corr^|i-j|; when noise = 1, noise has global dependence structure given by matrix(corr,p,p)+diag(p)*(1-corr))) * sigma. When noise = 2, rows of the W are independent and each having an AR(1) structure given by W_{j,t} = W_{j,t-1} * sqrt(corr) + rnorm(sd = sigma) * sqrt(1-corr). For noise = 3, 4, entries of W have i.i.d. uniform distribution and exponential distribution respectively, each centred and rescaled to have zero mean and variance sigma^2.}
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checkRd: (-1) single.change.Rd:25: Lost braces; missing escapes or markup?
    25 | \item{noise}{Noise structure of the multivarite time series. For noise = 0, 0.5, 1, columns of W have independent multivariate normal distribution with covariance matrix Sigma. When noise = 0, Sigma = sigma^2 * I_p; when noise = 0.5, noise has local dependence structure given by Sigma_{i,j} = sigma*corr^|i-j|; when noise = 1, noise has global dependence structure given by matrix(corr,p,p)+diag(p)*(1-corr))) * sigma. When noise = 2, rows of the W are independent and each having an AR(1) structure given by W_{j,t} = W_{j,t-1} * sqrt(corr) + rnorm(sd = sigma) * sqrt(1-corr). For noise = 3, 4, entries of W have i.i.d. uniform distribution and exponential distribution respectively, each centred and rescaled to have zero mean and variance sigma^2.}
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