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233 | 233 | """
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234 | 234 | crosscov!(r, x, y, lags; demean=true)
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235 | 235 |
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236 |
| -Compute the cross covariance function (CCF) between real-valued vectors or matrices |
| 236 | +Compute the cross covariance function (CCF) between vectors or matrices |
237 | 237 | `x` and `y` at `lags` and store the result in `r`. `demean` specifies whether the
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238 | 238 | respective means of `x` and `y` should be subtracted from them before computing their
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239 | 239 | CCF.
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343 | 343 | """
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344 | 344 | crosscov(x, y, [lags]; demean=true)
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345 | 345 |
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346 |
| -Compute the cross covariance function (CCF) between real-valued vectors or |
| 346 | +Compute the cross covariance function (CCF) between vectors or |
347 | 347 | matrices `x` and `y`, optionally specifying the `lags`. `demean` specifies
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348 | 348 | whether the respective means of `x` and `y` should be subtracted from them
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349 | 349 | before computing their CCF.
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@@ -384,7 +384,7 @@ crosscov(x::AbstractVecOrMat, y::AbstractVecOrMat; demean::Bool=true) =
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384 | 384 | """
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385 | 385 | crosscor!(r, x, y, lags; demean=true)
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386 | 386 |
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387 |
| -Compute the cross correlation between real-valued vectors or matrices `x` and `y` at |
| 387 | +Compute the cross correlation between vectors or matrices `x` and `y` at |
388 | 388 | `lags` and store the result in `r`. `demean` specifies whether the respective means of
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389 | 389 | `x` and `y` should be subtracted from them before computing their cross correlation.
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390 | 390 |
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504 | 504 | """
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505 | 505 | crosscor(x, y, [lags]; demean=true)
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506 | 506 |
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507 |
| -Compute the cross correlation between real-valued vectors or matrices `x` and `y`, |
| 507 | +Compute the cross correlation between vectors or matrices `x` and `y`, |
508 | 508 | optionally specifying the `lags`. `demean` specifies whether the respective means of
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509 | 509 | `x` and `y` should be subtracted from them before computing their cross correlation.
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510 | 510 |
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613 | 613 | """
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614 | 614 | pacf(X, lags; method=:regression)
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615 | 615 |
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616 |
| -Compute the partial autocorrelation function (PACF) of a real-valued vector |
| 616 | +Compute the partial autocorrelation function (PACF) of a vector |
617 | 617 | or matrix `X` at `lags`. `method` designates the estimation method. Recognized
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618 | 618 | values are `:regression`, which computes the partial autocorrelations via successive
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619 | 619 | regression models, and `:yulewalker`, which computes the partial autocorrelations
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