a T by N numeric matrix representing an fMRI run. There should not be any missing data (NA or NaN).

Normalize the data? Default: TRUE. Normalization removes constant-zero voxels, scales by 100 / the median of the mean image, and then centers each voxel on its mean.

To replicate Afyouni and Nichols' procedure for the HCP MPP data, since the HCP scans are already normalized to 10,000, just divide the data by 100 and center the voxels on their means:

Y <- Y/100; DVARS(t(Y - apply(Y, 1, mean))) where Y is the V by T data matrix.

Note that while voxel centering doesn't affect DVARS, it does affect DPD and ZD.

Numeric outlier cutoffs. Timepoints exceeding these cutoffs will be flagged as outliers.

Should occasional updates be printed? Default is FALSE.

An N by 6 matrix in which the first three columns represent the translational RPs (trans_units), and the second three columns represent the rotational RPs (rot_units). If rot_units measures an angle, it will be converted to trans_units by measuring displacement on a sphere of radius brain_radius trans_units.

Alternatively, this can be the file path to an N by 6 matrix which can be read with read.table (fields separated by white-space; no header).

"mm" for millimeters (default), "cm" for centimeters, or "in" for inches.

"deg" for degrees (default), "rad" for radians, or one of the trans_units options.

If rot_units measures an angle, the rotational RPs are transformed to a spatial measurement representing the displacement on a sphere of radius brain_radius trans_units.

If brain_radius is NULL (default), it will be set to 50 mm.

Detrend each RP with the DCT before computing FD? Default: FALSE. Can be a number of DCT bases to use, or TRUE to use 4.

The difference of indices between which to calculate change in position. Default: 1 (the previous timepoint). Changing this argument sets \Delta x_i = x_{i-lag} - x_i (and similarly for the other RPs).

FD values higher than this will be flagged. Default: .3.

The data

The univariate outliers

The dimension reduced and selected data.

Indices of the included subset of data.

Compute the robust distances? Default: TRUE.

The data

Default: 4.

Transform the data? Default: "none". The other choice is "robust-YJ". The "SHASH" method has not been implemented yet.

The numeric vector in which to detect outliers.

The maximum number of iterations. Default: 10.

SD threshold for outlier flagging. Default: 4.

Initial weights. Default: NULL (no pre-determined outliers).

Numeric vector of data to transform.

Parameter that modulates the mean of x.

Parameter that modulates the variance of x. Must be greater than zero. This parameter is on the logarithm scale.

Parameter that modulates the skewness of x.

Parameter that modulates the tailweight of x. Must be greater than zero. This parameter is on the logarithm scale.

Transform normal data to SHASH instead? Default: FALSE.

A "scrub_projection" object containing projection scrubbing results.

The timepoints or column indexes for which to compute artifact images. If NULL (default), use the outlying timepoints.

If detrended components are available (the "U" matrix of PCA or "M" matrix of ICA), should they be used to compute the artifact images? Otherwise, use the non-detrended components. Default: TRUE.

Wide numeric data matrix (T observations by V variables, T << V). For example, if X represents an fMRI run, T should be the number of timepoints and V should be the number of brainordinate vertices/voxels.

Or, a 4D array or NIFTI or file path to a NIFTI (I by J by K by T observations), in which case ROI_data must be provided. (The vectorized data will be T timepoints by V_{in-mask} voxels)

Or, a ciftiTools "xifti" object or a file path to a CIFTI (The vectorized data will be T timepoints by V_{left+right+sub} grayordinates).

Indicates the data ROI. Allowed arguments depend on X:

If X is a matrix, this must be a length V logical vector, where the data ROI is indicated by TRUE values. If "infer" (default), all columns of X will be included in the data ROI (rep(TRUE, V)).

If X is an array or NIFTI, this must be either a vector of values to expect for out-of-mask voxels in X, or a (file path to a) 3D NIFTI. In the latter case, each of the volume dimensions should match the first three dimensions of X. Voxels in the data ROI should be indicated by TRUE and all other voxels by FALSE. If "infer" (default), will be set to c(0, NA, NaN) (include all voxels which are not constant 0, NA, or NaN).

If X is a "xifti" this must be the brainstructures argument to read_cifti. If "infer" (default), brainstructures will be set to "all" (use both left and right cortex vertices, and subcortical voxels).

If NULL, the data ROI will be empty. This is useful for obtaining just the noise ROI, if the data and noise are located in separate files.

Indicates the noise ROIs for aCompCor. Should be a list where each entry corresponds to a distinct noise ROI. The names of the list should be the ROI names, e.g. "white_matter" and "csf". The expected formats of the list entries depends on X:

For all types of X, ROI_noise entries can be a matrix of noise ROI data. The matrix should have T rows, with each column being a data location's timeseries.

If X is a matrix, entries can also indicate a noise ROI within X. These entries must be a length V logical vector with TRUE values indicating locations in X within that noise ROI. Since the ROIs must not overlap, the masks must be mutually exclusive with each other, and with ROI_data.

If X is an array or NIFTI, entries can also indicate a noise ROI within X. These entries must be a logical array or (file path to) a 3D NIFTI with the same spatial dimensions as X, and with TRUE values indicating voxels inside the noise ROI. Since the ROIs must not overlap, the masks must be mutually exclusive with each other, and with ROI_data.

(If X is a "xifti", entries must be data matrices, since no grayordinate locations in X are appropriate noise ROIs).

The data

MAD threshold

Numeric vector of integers indicating the indices of the flagged volumes. Or, a logical vector of length n_time where TRUE values indicate the flagged volumes.

The length of the vectors to obtain. For nuisance regression, this is the length of the BOLD data. The highest index in flags should not exceed n_time.

A matrix; each column is a component. For PCA, this is the U matrix. For ICA, this is the M matrix.

components with kurtosis of at least this quantile are kept.

The number of simulation data to use for estimating the sampling distribution of kurtosis. Only used if a new simulation is performed. (If n<1000 and the quantile is 90%, a pre-computed value is used instead. If n>1000, the theoretical asymptotic distribution is used instead.)

Require at least one component to be selected? In other words, if no components meet the quantile cutoff, should the component with the highest kurtosis be returned? Default: FALSE.

The n by Q PC score matrix/IC mixing matrix.

Assume the columns of Comps are orthogonal and have 2-norms equal to 1? Speeds up the computation.

The outlier cutoff, in multiples of the median leverage. Default: NULL (do not compute outliers).

The number of principal components to compute for each noise ROI. Alternatively, values between 0 and 1, in which case they will represent the minimum proportion of variance explained by the PCs used for each noise ROI. The smallest number of PCs will be used to achieve this proportion of variance explained.

Should be a list or numeric vector with the same length as ROI_noise. It will be matched to each ROI based on the name of each entry, or if the names are missing, the order of entries. If it is an unnamed vector, its elements will be recycled. Default: 5 (compute the top 5 PCs for each noise ROI).

The number of voxel layers to erode the noise ROIs by. Should be a list or numeric vector with the same length as ROI_noise. It will be matched to each ROI based on the name of each entry, or if the names are missing, the order of entries. If it is an unnamed vector, its elements will be recycled. Default: NULL, which will use a value of 0 (do not erode the noise ROIs). Note that noise erosion can only be performed if the noise ROIs are volumetric.

The "scrub_DVARS" object

(Optional) If provided, will add a title to the plot.

Additional arguments to ggplot, e.g. main, sub, xlab, ylab, legend.position

The "scrub_FD" object

(Optional) If provided, will add a title to the plot.

Additional arguments to ggplot, e.g. main, sub, xlab, ylab, legend.position

The "scrub_projection" object

(Optional) If provided, will add a title to the plot.

Additional arguments to ggplot, e.g. main, sub, xlab, ylab, legend.position

The "scrub_projection_multi" object.

(Optional) If provided, will add a title to the plot.

Additional arguments to ggplot, e.g. main, sub, xlab, ylab, legend.position

The "scrub_*" object.

(Optional) If provided, will add a title to the plot.

Additional arguments to ggplot, e.g. main, sub, xlab, ylab, legend.position

See pscrub.

Wide numeric data matrix (T observations by V variables, T << V). If X represents an fMRI run, T should be the number of timepoints and V should be the number of vertices/voxels. Projection scrubbing will measure the outlyingness of each row in X.

One of the following: "ICA" (default) or "PCA".

Nuisance signals to regress from each column of X. Should be specified as a design matrix: a T by N numeric matrix where N represents the number of nuisance signals. Or can be "DCT4" (default), which will create a matrix with a constant column (the intercept term) and four DCT bases. This default nuisance regression will have the effect of demeaning and detrending the data by removing low-frequency components. To not perform any nuisance regression set this argument to NULL, 0, or FALSE.

Detrending is highly recommended for time-series data, especially if there are many time points or evolving circumstances affecting the data. Additionally, if kurtosis is being used to select the projection directions, trends can induce positive or negative kurtosis, contaminating the connection between high kurtosis and outlier presence. Detrending should not be used with non-time-series data because the observations are not temporally related.

Additional nuisance regressors can be specified like so: cbind(1, fMRItools::dct_bases(nrow(x), 4), more_nuisance).

Center the columns of the data by their medians, and scale the columns of the data by their median absolute deviations (MADs)? Default: TRUE. Centering is necessary for computing the projections, so if center is FALSE, the data must already be centered.

Note that centering and scaling occur after nuisance regression, so even if center is FALSE, the data will be centered on the means if the nuisance regression included an intercept term, as it does by default.

Stabilize the mean and variance of each projection component's timecourse prior to computing kurtosis and leverage? These arguments should be TRUE, FALSE (default), or the number of DCT bases to use for detrending (TRUE will use 4). Note that these arguments affect the projection components and not the data itself. Also, if variance-stabilizing but not mean-stabilizing, the components must already be expected to be mean-stabilized, for example if the data was rigorously detrended; otherwise, the results will be invalid.

Slow-moving mean and variance patterns in the components will interfere with the roles of kurtosis and leverage in identifying outliers. While nuisance can be used to detrend the data, this nuisance regression is estimated non-robustly, since a robust model takes too long to estimate at each data location. On the other hand, comps_mean_dt and comps_var_dt can be used to apply a robust nuisance regression at each component, since there are much fewer components than original data locations. Thus, even if the data has been detrended with nuisance it may be helpful to detrend the components with comps_mean_dt; furthermore, the data nuisance regression does not address the potential existence of variance patterns in the components.

Overall, for fMRI we recommend enabling comps_mean_dt and comps_var_dt unless the data has been cleaned not only with a low-pass filter like DCT nuisance regression, but also with anatomical CompCor, ICA-FIX, or a similar data-driven strategy that takes into account common sources of artifactual mean and variance trends such as motion and physiological cycles.

Use pesel to select the number of components? Default: TRUE. Otherwise, use the number of principal components with above-average variance.

What quantile cutoff should be used to select the components? Default: 0.99. Use 0 to select all high-variance components regardless of kurtosis value.

We model each component as a length T vector of Normal iid random variables, for which the distribution of kurtosis values can be approximated. The quantile is estimated based on this distribution.

Should the projection directions be returned? This is the V matrix in PCA and S matrix in ICA. The default is FALSE to save memory. However, get_dirs==TRUE is required for artifact_images.

Only applies to the PCA projection. Return the full SVD? Default: FALSE (return only the high-variance components).

Should outliers be flagged based on cutoff? Default: TRUE.

Median leverage cutoff value. Default: 4.

Set a seed right before the call to pesel::pesel or ica::icaimax? If NULL, do not set a seed. If numeric (default: 0), will use as the seed.

The method argument to fastICA: "C" to use C code with BLAS (default), or "R" to use R code.

Should occasional updates be printed? Default: FALSE.

Wide numeric data matrix (T observations by V variables, T << V). If X represents an fMRI run, T should be the number of timepoints and V should be the number of vertices/voxels. Projection scrubbing will measure the outlyingness of each row in X.

Nuisance signals to regress from each column of X. Should be specified as a design matrix: a T by N numeric matrix where N represents the number of nuisance signals. Or can be "DCT4" (default), which will create a matrix with a constant column (the intercept term) and four DCT bases. This default nuisance regression will have the effect of demeaning and detrending the data by removing low-frequency components. To not perform any nuisance regression set this argument to NULL, 0, or FALSE.

Detrending is highly recommended for time-series data, especially if there are many time points or evolving circumstances affecting the data. Additionally, if kurtosis is being used to select the projection directions, trends can induce positive or negative kurtosis, contaminating the connection between high kurtosis and outlier presence. Detrending should not be used with non-time-series data because the observations are not temporally related.

Additional nuisance regressors can be specified like so: cbind(1, fMRItools::dct_bases(nrow(x), 4), more_nuisance).

Center the columns of the data by their medians, and scale the columns of the data by their median absolute deviations (MADs)? Default: TRUE. Centering is necessary for computing the projections, so if center is FALSE, the data must already be centered.

Note that centering and scaling occur after nuisance regression, so even if center is FALSE, the data will be centered on the means if the nuisance regression included an intercept term, as it does by default.

Stabilize the mean and variance of each projection component's timecourse prior to computing kurtosis and leverage? These arguments should be TRUE, FALSE (default), or the number of DCT bases to use for detrending (TRUE will use 4). Note that these arguments affect the projection components and not the data itself. Also, if variance-stabilizing but not mean-stabilizing, the components must already be expected to be mean-stabilized, for example if the data was rigorously detrended; otherwise, the results will be invalid.

Slow-moving mean and variance patterns in the components will interfere with the roles of kurtosis and leverage in identifying outliers. While nuisance can be used to detrend the data, this nuisance regression is estimated non-robustly, since a robust model takes too long to estimate at each data location. On the other hand, comps_mean_dt and comps_var_dt can be used to apply a robust nuisance regression at each component, since there are much fewer components than original data locations. Thus, even if the data has been detrended with nuisance it may be helpful to detrend the components with comps_mean_dt; furthermore, the data nuisance regression does not address the potential existence of variance patterns in the components.

Overall, for fMRI we recommend enabling comps_mean_dt and comps_var_dt unless the data has been cleaned not only with a low-pass filter like DCT nuisance regression, but also with anatomical CompCor, ICA-FIX, or a similar data-driven strategy that takes into account common sources of artifactual mean and variance trends such as motion and physiological cycles.

What quantile cutoff should be used to select the components? Default: 0.99. Use 0 to select all high-variance components regardless of kurtosis value.

We model each component as a length T vector of Normal iid random variables, for which the distribution of kurtosis values can be approximated. The quantile is estimated based on this distribution.

Should the projection directions be returned? This is the V matrix in PCA and S matrix in ICA. The default is FALSE to save memory. However, get_dirs==TRUE is required for artifact_images.

Only applies to the PCA projection. Return the full SVD? Default: FALSE (return only the high-variance components).

Should outliers be flagged based on cutoff? Default: TRUE.

Median leverage cutoff value. Default: 4.

Should occasional updates be printed? Default: FALSE.

Set a seed right before the call to pesel::pesel or ica::icaimax? If NULL, do not set a seed. If numeric (default: 0), will use as the seed.

The method argument to fastICA: "C" to use C code with BLAS (default), or "R" to use R code.

The "pscrub_multi" object to convert

Wide numeric data matrix (T observations by V variables, T << V). If X represents an fMRI run, T should be the number of timepoints and V should be the number of vertices/voxels. Projection scrubbing will measure the outlyingness of each row in X.

Projection scrubbing projects the data onto directions likely to contain outlier information. Choose at least one of the following:

"PCA"

PCA using the top k PCs.

"PCA_kurt"

PCA using the high-kurtosis PCs among the top k.

"PCA2"

PCA using the top k2 PCs.

"PCA2_kurt"

PCA using the high-kurtosis PCs among the top k2.

"ICA"

ICA using the top k ICs.

"ICA_kurt"

ICA using the high-kurtosis ICs among the top k.

"ICA2"

ICA using the top k2 ICs.

"ICA2_kurt"

ICA using the high-kurtosis ICs among the top k2.

where k is the number of components determined by PESEL, and k2 is the number of principal components with above-average variance.

Use "all" to use all projection methods. Default: "ICA_kurt".

Nuisance signals to regress from each column of X. Should be specified as a design matrix: a T by N numeric matrix where N represents the number of nuisance signals. Or can be "DCT4" (default), which will create a matrix with a constant column (the intercept term) and four DCT bases. This default nuisance regression will have the effect of demeaning and detrending the data by removing low-frequency components. To not perform any nuisance regression set this argument to NULL, 0, or FALSE.

Detrending is highly recommended for time-series data, especially if there are many time points or evolving circumstances affecting the data. Additionally, if kurtosis is being used to select the projection directions, trends can induce positive or negative kurtosis, contaminating the connection between high kurtosis and outlier presence. Detrending should not be used with non-time-series data because the observations are not temporally related.

Additional nuisance regressors can be specified like so: cbind(1, fMRItools::dct_bases(nrow(x), 4), more_nuisance).

Center the columns of the data by their medians, and scale the columns of the data by their median absolute deviations (MADs)? Default: TRUE. Centering is necessary for computing the projections, so if center is FALSE, the data must already be centered.

Note that centering and scaling occur after nuisance regression, so even if center is FALSE, the data will be centered on the means if the nuisance regression included an intercept term, as it does by default.

Stabilize the mean and variance of each projection component's timecourse prior to computing kurtosis and leverage? These arguments should be TRUE, FALSE (default), or the number of DCT bases to use for detrending (TRUE will use 4). Note that these arguments affect the projection components and not the data itself. Also, if variance-stabilizing but not mean-stabilizing, the components must already be expected to be mean-stabilized, for example if the data was rigorously detrended; otherwise, the results will be invalid.

Slow-moving mean and variance patterns in the components will interfere with the roles of kurtosis and leverage in identifying outliers. While nuisance can be used to detrend the data, this nuisance regression is estimated non-robustly, since a robust model takes too long to estimate at each data location. On the other hand, comps_mean_dt and comps_var_dt can be used to apply a robust nuisance regression at each component, since there are much fewer components than original data locations. Thus, even if the data has been detrended with nuisance it may be helpful to detrend the components with comps_mean_dt; furthermore, the data nuisance regression does not address the potential existence of variance patterns in the components.

Overall, for fMRI we recommend enabling comps_mean_dt and comps_var_dt unless the data has been cleaned not only with a low-pass filter like DCT nuisance regression, but also with anatomical CompCor, ICA-FIX, or a similar data-driven strategy that takes into account common sources of artifactual mean and variance trends such as motion and physiological cycles.

What quantile cutoff should be used to select the components? Default: 0.99. Use 0 to select all high-variance components regardless of kurtosis value.

We model each component as a length T vector of Normal iid random variables, for which the distribution of kurtosis values can be approximated. The quantile is estimated based on this distribution.

Should the projection directions be returned? This is the V matrix in PCA and S matrix in ICA. The default is FALSE to save memory. However, get_dirs==TRUE is required for artifact_images.

Only applies to the PCA projection. Return the full SVD? Default: FALSE (return only the high-variance components).

Should outliers be flagged based on cutoff? Default: TRUE.

Median leverage cutoff value. Default: 4.

Set a seed right before the call to pesel::pesel or ica::icaimax? If NULL, do not set a seed. If numeric (default: 0), will use as the seed.

The method argument to fastICA: "C" to use C code with BLAS (default), or "R" to use R code.

Should occasional updates be printed? Default: FALSE.

The timeseries to stabilize.

Center and scale? Default: TRUE for both. If scaling but not centering, the data must already be centered; otherwise, the results will be invalid. Can also be the number of DCT bases to use for robust stabilization of center/scale; TRUE will use 4.

The lmrob_method argument to robustbase::lmrob.

After stabilizing x, re-center and re-scale to the original mean and variance? Default: TRUE.

The input vector to regress on DCT bases

The number of DCT bases to use. Default: 4

The lmrob_method argument to robustbase::lmrob.

Set a seed right before the call to robustbase::lmrob? If NULL, do not set a seed. If numeric (default: 0), will use as the seed.

Wide numeric data matrix (T observations by V variables, T << V). If X represents an fMRI run, T should be the number of timepoints and V should be the number of vertices/voxels. Projection scrubbing will measure the outlyingness of each row in X.

Default: 4.

Quantile cutoff...?

Apply a transformation prior to univariate outlier detection? Three options: "none" (default), "robust-YJ", and "SHASH".

Use bootstrapping to estimate the robust distance null distribution? If so, set this to the number of bootstraps. Default: 100. Use 0 (or FALSE), to use an empirical quantile instead.

If using bootstrap (bootstrap > 0), this is the level of the bootstrap CI. Default: 0.99.

One of the following: "ICA" (default) or "PCA".

Nuisance signals to regress from each column of X. Should be specified as a design matrix: a T by N numeric matrix where N represents the number of nuisance signals. Or can be "DCT4" (default), which will create a matrix with a constant column (the intercept term) and four DCT bases. This default nuisance regression will have the effect of demeaning and detrending the data by removing low-frequency components. To not perform any nuisance regression set this argument to NULL, 0, or FALSE.

Detrending is highly recommended for time-series data, especially if there are many time points or evolving circumstances affecting the data. Additionally, if kurtosis is being used to select the projection directions, trends can induce positive or negative kurtosis, contaminating the connection between high kurtosis and outlier presence. Detrending should not be used with non-time-series data because the observations are not temporally related.

Additional nuisance regressors can be specified like so: cbind(1, fMRItools::dct_bases(nrow(x), 4), more_nuisance).

Center the columns of the data by their medians, and scale the columns of the data by their median absolute deviations (MADs)? Default: TRUE. Centering is necessary for computing the projections, so if center is FALSE, the data must already be centered.

Note that centering and scaling occur after nuisance regression, so even if center is FALSE, the data will be centered on the means if the nuisance regression included an intercept term, as it does by default.

Stabilize the mean and variance of each projection component's timecourse prior to computing kurtosis and leverage? These arguments should be TRUE, FALSE (default), or the number of DCT bases to use for detrending (TRUE will use 4). Note that these arguments affect the projection components and not the data itself. Also, if variance-stabilizing but not mean-stabilizing, the components must already be expected to be mean-stabilized, for example if the data was rigorously detrended; otherwise, the results will be invalid.

Slow-moving mean and variance patterns in the components will interfere with the roles of kurtosis and leverage in identifying outliers. While nuisance can be used to detrend the data, this nuisance regression is estimated non-robustly, since a robust model takes too long to estimate at each data location. On the other hand, comps_mean_dt and comps_var_dt can be used to apply a robust nuisance regression at each component, since there are much fewer components than original data locations. Thus, even if the data has been detrended with nuisance it may be helpful to detrend the components with comps_mean_dt; furthermore, the data nuisance regression does not address the potential existence of variance patterns in the components.

Overall, for fMRI we recommend enabling comps_mean_dt and comps_var_dt unless the data has been cleaned not only with a low-pass filter like DCT nuisance regression, but also with anatomical CompCor, ICA-FIX, or a similar data-driven strategy that takes into account common sources of artifactual mean and variance trends such as motion and physiological cycles.

Use pesel to select the number of components? Default: TRUE. Otherwise, use the number of principal components with above-average variance.

What quantile cutoff should be used to select the components? Default: 0.99. Use 0 to select all high-variance components regardless of kurtosis value.

We model each component as a length T vector of Normal iid random variables, for which the distribution of kurtosis values can be approximated. The quantile is estimated based on this distribution.

Should the projection directions be returned? This is the V matrix in PCA and S matrix in ICA. The default is FALSE to save memory. However, get_dirs==TRUE is required for artifact_images.

Only applies to the PCA projection. Return the full SVD? Default: FALSE (return only the high-variance components).

Should outliers be flagged based on cutoff? Default: TRUE.

Median leverage cutoff value. Default: 4.

Set a seed right before the call to pesel::pesel or ica::icaimax? If NULL, do not set a seed. If numeric (default: 0), will use as the seed.

The method argument to fastICA: "C" to use C code with BLAS (default), or "R" to use R code.

Skip dimension reduction? Default: FALSE.

Should occasional updates be printed? Default: FALSE.

A T by V numeric matrix representing an fMRI run. There should not be any missing data (NA or NaN).

"projection" (default) or "DVARS"

Additional arguments to the specific scrubbing function: see pscrub or DVARS.

A T by m numeric data.frame with each column being the timecourse for an outlyingness measure. The names of the columns will be used to label the plot.

A length m numeric vector with each value being the cutoff for an outlyingness measure (each column in meas).

Only flag timepoints at which all measures are outliers? Default: FALSE.

A length m character vector giving the colors of each measure (each column in meas)

Use log scale for y-axis? Default: FALSE

"point" (default) or "line"

The range of the y-axis.

Additional arguments to ggplot: main, sub, xlab, ...

Path to a CIFTI file, or a "xifti" object.

"projection" or "DVARS"

Character vector indicating which brain structure(s) to use: "left" (left cortical surface), "right" (right cortical surface) and/or "subcortical" (subcortical and cerebellar gray matter). Can also be "all" (obtain all three brain structures). Default: c("left", "right") (excludes the subcortex).

Additional arguments to each specific scrubbing function: pscrub or DVARS.

Numeric vector of data to estimate standard deviation for.

The scalar power transformation parameter. w = x^{1/d} is computed to obtain w \sim N(\mu_w, \sigma_w^2).

Object of class "scrub_DVARS".

further arguments passed to or from other methods.

Object of class "scrub_DVARS".

Object of class "scrub_FD".

further arguments passed to or from other methods.

Object of class "scrub_FD".

Object of class "scrub_projection".

further arguments passed to or from other methods.

Object of class "scrub_projection".