2020-09-28

- There are a few dozen new S3 methods for
`bg_GLM`

objects. See`methods(class='bg_GLM')`

for the full list.`coeff_determ`

calculates the coefficient of determination.`coeff_table`

returns the coefficients table (same as`summary.lm(x)$coefficients`

) for each region. - There are new GLM fitting functions (prefixed with
`fastLmBG`

) that are significantly faster and work with matrix/array inputs. You can see these with the code`apropos('fastLm')`

. - New functions
`randomise`

and`randomise_3d`

can be called directly (although not recommended), and the`partition`

function is now exported. Each of these is for permutation-based analysis of linear models. Furthermore, there are new permutation methods (`manly`

,`draperStoneman`

, and`stillWhite`

). - New object
`brainGraphList`

for a collection of all graphs at a single density/threshold. There are multiple S3 methods for this object, including the creation method`make_brainGraphList`

. `make_brainGraph`

is now a S3 method.- There are several new matrix utility functions.
`inv`

calculates the the “unscaled covariance” matrix used in linear models.`pinv`

calculates the*pseudoinverse*.`qr`

calculates the QR decomposition for each matrix in a 3D array.`colMax`

,`colMin`

, and`colMaxAbs`

calculate the max, min, and max of the absolute value across matrix columns.`is_binary`

determines if a matrix consists only of 0’s and 1’s.`symmetrize`

is now a S3 method.`symm_mean`

symmetrizes a matrix using the mean of off-diagonal elements. - There are 4 new atlases:
`hcp_mmp1.0`

(HCP multimodal atlas),`power264`

,`gordon333`

, and`brainnetome`

- New function
`sim.rand.graph.hqs`

generates random graphs from random covariance matrices for*structural covariance networks*using the HQS algorithm. - New plotting function
`slicer`

plots multiple graphs in a single figure. - Fewer package dependencies.
`mean_distance_wt`

calculates weighted shortest path lengths.

`count_interlobar`

is replaced by`count_inter`

`make_mediate_brainGraph`

is replaced by the`make_brainGraph`

S3 method.`plot_brainGraph`

is replaced by the`plot`

S3 method for`brainGraph`

objects.

2019-10-20

- The
`mediation`

package no longer exports`summary.mediate`

, so it has to be removed from`brainGraph`

- Move
`mediation`

to*Suggests*, as well

- Move

2019-06-29

- Fix bug in
`import_scn`

so the*Study.ID*column is always read as`character`

- Remove deprecated function
`brainGraph_init`

2018-12-15

`import_scn`

replaces`brainGraph_init`

, with a few changes in behavior:- It is no longer necessary to abbreviate region names yourself; the function does it automatically
- Expects files with the name
`${parcellation}_${hemi}_${modality}.csv`

in the`datadir`

- Here,
`${parcellation}`

could be`aparc`

, for example - Also,
`${modality}`

could be`thickness`

- Here,
- If the
*atlas*you are using includes`scgm`

, there should be a`asegstats.csv`

file

`aop`

and`loo`

now return*S3*objects, with class name`IC`

- These also have
`summary`

and`plot`

methods - Furthermore, these objects return some more information

- These also have

- The
`exclude`

argument to`get.resid`

is now`exclude.cov`

to highlight that it is for specifying*covariates*to exclude from the GLM

2018-12-07

- Fix bug in
`partition`

, changing`method`

to`part.method`

- Add some checks in
`robustness`

so it doesn’t throw an error

2018-09-04

- Fixed bug in
`count_homologous`

that affected a subset of atlases- Performance is modestly improved (approx. 2-3x faster)

`count_inter`

replaces`count_interlobar`

; the new function calculates inter- and intra-group edge counts, where a group of vertices can be a*lobe*,*hemisphere*,*network*(for the`dosenbach160`

atlas), or*class*(for the`destrieux`

atlases)- The return object is now more informative; the function returns a matrix of all inter- and intra-group edge counts, in addition to a data.table containing a summary (that matches the output of previous versions)

`rich_core`

now calculates the rich core for weighted networks- In addition, the function runs
*considerably*faster:- for smaller sparse graphs, it is ~40-80x faster
- for larger dense graphs, it is more than 2,000x faster

- In addition, the function runs
`robustness`

now returns a data.table containing much more information (in addition to the max. connected component ratio)- This makes plotting outputs simpler; see Chapter 14 of the
*User Guide* - When
`type='edge'`

, the function is about 2-3x faster than previous versions

- This makes plotting outputs simpler; see Chapter 14 of the

`plot.mtpc`

: the stats displayed in the caption have been “transposed”, such that`S.crit`

and`A.crit`

are in the top row

2018-09-01

- Fixed regression bug in
`NBS`

(introduced by`v2.0.0`

) which only occurred if`alternative='less'`

when calculating the minimum statistic of permuted networks

- Updated code that symmetrizes matrices:
- Introduced new function,
`symm_mean`

, that more quickly symmetrizes a matrix about the diagonal by assigning`mean(c(A[i, j], A[j, i]))`

to the off-diagonal elements - Uses
`pmin`

and`pmax`

for symmetrizing matrices based on the off-diagonal minimum and maximum, respectively

- Introduced new function,
- Optimized code in a few functions for faster execution:
`sim.rand.graph.clust`

is about 2x faster due to improvement in the`choose.edges`

helper function`centr_lev`

and`edge_asymmetry`

are also faster

2018-07-21

`hubness`

: new function for determining which vertices are hubs`set_brainGraph_attr`

- New argument
`clust.method`

lets the user choose which clustering (community detection) method to use.- The default is still the
`louvain`

algorithm. - If you select
`spinglass`

, but the graph is unconnected, then`louvain`

is used instead. - If there are any negative edge weights, and you choose anything other than
`walktrap`

or`spinglass`

, the`walktrap`

method is used.

- The default is still the
- Now calculates
`num.hubs`

using the new`hubness`

function, and calculates separate values for weighted and unweighted networks

- New argument

2018-07-06

- Fixed bugs in
`rich_club_norm`

that would throw an error if certain graph attributes weren’t present

`rich_club_all`

- new function that is a wrapper for`rich_club_coeff`

, applying over all possible degree values

2018-06-25

- Fixed regression bug in
`plot.brainGraph`

, which occurred when choosing`plane='sagittal'`

2018-06-22

- Fixed regression bug when fitting GLM models with a
`F contrast`

- Fixed minor bug in
`make_nbs_brainGraph`

which did not properly assign the`p.nbs`

attribute to all vertices

- The elements of the
`NBS`

output object,`p.mat`

and`T.mat`

, are now 3-dimensional arrays (with extent equal to the # of contrasts) instead of lists of matrices

2018-06-20

- Fixed a bug in
`brainGraph_permute`

that I didn’t catch before

2018-06-20

`brainGraph_boot`

and`corr.matrix`

:- Incorrectly calculated
`E.global.wt`

before; now it transforms edge weights - To do so, includes argument
`xfm.type`

- Fixed bug when calling
`corr.matrix`

(added`rand=TRUE`

) - Also had to update the return object of
`corr.matrix`

for this purpose

- Incorrectly calculated
`mtpc`

- Previously gave some incorrect results when
`alt='less'`

; fixed - The
`plot`

method also now gives correct values when`alt='less'`

- Previously gave some incorrect results when
`brainGraph_GLM`

now returns the correct*null.thresh*when`alt != 'greater'`

`plot.brainGraph`

: fixed bug that occurred when`plane='sagittal'`

and a`hemi`

value was not supplied`plot_rich_norm`

: didn’t plot values for all degrees present in the networks under certain scenarios

`set_brainGraph_attr`

now calculates a graph-level`Lp.wt`

, which equals:

```
Lpv.wt <- distances(g)
Lpv.wt[is.infinite(Lpv.wt)] <- NA
g$Lp.wt <- mean(Lpv.wt[upper.tri(Lpv.wt)], na.rm=T)
```

`plot_rich_norm`

: new argument`smooth`

lets you plot with a smoother in the case of single-subject data, as opposed to the previous default of a line plot for all subjects

- GLM-related and other functions will now:
- Allow for the
`Study.ID`

column to be*numeric*; they will convert it to class*character* - Creates a
*character*vector of integers if`Study.ID`

is not present in the data

- Allow for the
- The
`summary.mtpc`

method now also prints the value of`clust.size`

2018-05-28

- Moved
`RGtk2`

and`cairoDevice`

to*Suggests*(from*Depends*) to allow installation on headless servers- Thanks to
`@michaelhallquist`

for the pull request!

- Thanks to
- Functions
`boot_global`

,`permute.group`

, and`plot_group_means`

are no longer accessible (deprecated since*v2.0.0*)

2018-05-03 (mostly changes to *structural covariance network* functionality)

- Fixed a bug in
`mtpc`

that was introduced in`v2.0.1`

`brainGraph_GLM_design`

has a new argument`factorize`

which specifies whether or not to convert all character columns (excluding*Study.ID*) to factor variables. The default is`TRUE`

. Previously, character columns were ignored.`get.resid`

- In the function call, you can choose whether or not to test a linear model for all groups together or separately, via the
`method`

argument - The
`plot`

method now returns a*list*of*ggplot*objects, and is similar to the`plot`

methods for`bg_GLM`

and`mtpc`

- In the function call, you can choose whether or not to test a linear model for all groups together or separately, via the
`corr.matrix`

- The
`resids`

argument must be the output of`get.resid`

(not a*data.table*as before) - Correlations will be calculated separately for all subject groups (as this information is stored in the output of
`get.resid`

); you no longer need to loop (or`lapply`

) across groups - In the function call, you can choose whether to correlate the residuals or raw structural values, via the
`what`

argument - The
`exclusions`

argument was renamed to`exclude.reg`

to highlight that you should specify*region names*to be excluded (if any) - You can explicitly choose whether to calculate Pearson or Spearman correlations, via the
`type`

argument (previously, this behavior was “hidden”)

- The

`brainGraph_init`

: the`modality`

argument now will accept*any*character string; the default is still*thickness*. The files with the string you supply still must be present on your system.- Due to
`corr.matrix`

expecting different input, the following functions also require, for their`resids`

argument, the output of`get.resid`

(instead of a*data.table*):`aop`

`brainGraph_boot`

`brainGraph_permute`

`loo`

2018-04-28

`gateway_coeff`

: no longer throws an error for very sparse graphs; instead, it returns a vector with`NaN`

values for unconnected vertices`make_mediate_brainGraph`

: did not return correct values (for the treatment condition) when`INT=TRUE`

(it recycled the values for the control condition)`make_intersection_brainGraph`

- Previously exited with error if any of the input graphs did not contain vertices meeting the desired
`subgraph`

condition - Now returns an empty graph if none of the input graphs meet the
`subgraph`

condition

- Previously exited with error if any of the input graphs did not contain vertices meeting the desired
`NBS`

:- When getting the indices for which matrix elements to transpose (so that result is symmetric), the result was slightly wrong for
`alt='greater'`

- Calculation of edge counts in
`summary`

method contained an error

- When getting the indices for which matrix elements to transpose (so that result is symmetric), the result was slightly wrong for

- All
`summary`

methods now provide a`DT.sum`

element in the returned list; previously it was inconsistent

2018-04-26

- In
`mtpc`

, the stats table that is returned previously was not always unique `mtpc`

did not return a list with a named element`clust.size`

(it was unnamed)- In
`plot.mtpc`

, if the user selected a contrast other than the first, it would not plot the correct null statistics (green dots)

2018-02-23

Release on CRAN; bugfix release.

- Fixed a bug in
`create_mats`

in which the ordering (along the 3rd dimension) of the arrays in`A.norm.sub`

did not match the ordering of the input matrix files (and therefore the ordering along the 3rd dimension of the arrays`A`

and`A.norm`

).- In the case that the input matrix files were already ordered by
*Group*and*Study.ID*, then this is not a “bug”, in that the ordering was already correct. So, if your subject groups are`groups <- c('Control', 'Patient')`

, and the matrix files are separated on the filesystem by group, there is no change in behavior. - This bug only appeared when
`threshold.by='consistency'`

or`threshold.by='consensus'`

(the default option).

- In the case that the input matrix files were already ordered by

2018-02-07

- Fixed error in
`mtpc`

when creating the MTPC statistics`data.table`

2018-02-05

*2nd major release; 6th CRAN release*. (The previous CRAN release was at v1.0.0)

For other updates and bug fixes, see the minor release notes below.

- Mediation analysis is now possible through
`brainGraph_mediate`

. - I have introduced some simple
*S3 classes*and*methods*. All of the classes have`plot`

(except`NBS`

) and`summary`

methods. The classes and corresponding “creation functions” are:

Class | Creation func. | Description |
---|---|---|

brainGraph | make_brainGraph | Any graph with certain attributes |

bg_GLM | brainGraph_GLM | Results of GLM analysis |

NBS | NBS | Results of NBS analysis |

mtpc | mtpc | Results of MTPC analysis |

brainGraph_GLM | make_glm_brainGraph | Graphs from GLM analysis |

brainGraph_NBS | make_nbs_brainGraph | Graphs from NBS analysis |

brainGraph_mtpc | make_glm_brainGraph | Graphs from MTPC analysis |

brainGraph_mediate | make_mediate_brainGraph | Graphs from mediation analysis |

brainGraph_boot | brainGraph_boot | Results of bootstrap analysis |

brainGraph_permute | brainGraph_permute | Results of permutation tests |

brainGraph_resids | get.resid | Residuals for covariance networks |

- Multiple contrasts (in the same function call), as well as F-contrasts, are now allowed in the GLM-based functions:
`brainGraph_GLM`

,`mtpc`

,`NBS`

, and`get.resid`

.- There is a new function argument,
`con.type`

, for this purpose. - Since both contrast types are now specified in the form of a
*contrast matrix*, the argument`con.vec`

has been replaced by`con.mat`

.

- There is a new function argument,
- Designs with 3-way interactions (e.g.,
`2 x 2 x 2`

) are now allowed for GLM-based analyses. - Permutations for GLM-based analyses are now done using the
*Freedman-Lane*method (the same as in FSL’s*randomise*and in*PALM*). - Plot the “diagnostics” from GLM analyses through the
`plot.bg_GLM`

method to the output of`brainGraph_GLM`

. - Plot the statistics from MTPC analyses through the
`plot.mtpc`

method for`mtpc`

results. `aop`

has a new argument`control.value`

allowing you to specify the control group; all comparisons will be to that group.- Removes the need to loop through patient groups in the console (if you have more than 1).

- Most of the GLM-based functions have a new argument,
`long`

, which will not return all of the permutation results if`long=FALSE`

.

`boot_global`

was renamed to`brainGraph_boot`

.`check.resid`

was removed; you now just call the`plot`

method to outputs of`get.resid`

.`permute.group`

:- Function was renamed to
`brainGraph_permute`

. - The arguments are slightly re-ordered
- Argument
`permSet`

was renamed to`perms`

. - New argument
`auc`

lets you explicitly define whether or not you want statistics for the*area under the curve (AUC)*.

- Function was renamed to
`plot_boot`

was removed; you now just call the`plot`

method to outputs of`brainGraph_boot`

.`plot_brainGraph_mni`

has been removed; this functionality can be changed by the`mni`

argument to`plot.brainGraph`

(i.e., the*plot method*for objects of class`brainGraph`

)`plot_group_means`

was renamed to`plot_volumetric`

, as it works specifically for structural covariance networks.`plot_perm_diffs`

was removed; you now just call the`plot`

method to outputs of`brainGraph_permute`

.

`NBS`

now automatically symmetrizes the input matrices. This is partly for speed and partly because`igraph`

symmetrizes the matrices anyway.- There is a new function argument,
`symm.by`

(which is the same as that for`create_mats`

) for this purpose.

- There is a new function argument,
`corr.matrix`

:- Now expects as its first input the residuals from
`get.resid`

. - You may specify multiple
`densities`

(or`thresholds`

), - Returns a
*list*including the binarized, thresholded matrices as an*array*(still named`r.thresh`

).

- Now expects as its first input the residuals from
`get.resid`

now allows for any design matrix for getting LM residuals (similar to`brainGraph_GLM`

).- Must supply a
`data.table`

of covariates. - You may pass on arguments to
`brainGraph_GLM_design`

for creating the correct design matrix.

- Must supply a
`mtpc`

accepts 2 new arguments (in addition to explicitly naming required arguments that pass on to`brainGraph_GLM`

):`clust.size`

lets you change the “cluster size”, the number of consecutive thresholds needed to deem a result significant (default:`3`

)`res.glm`

lets you input the`res.glm`

list element from a previous`mtpc`

run. This is only useful if you would like to compare results with different values for`clust.size`

.

`permute.group`

(see above section for changes)`rich_club_norm`

now returns a`data.table`

, which simplifies working with the data (and plotting).`set_brainGraph_attr`

: multiple (explicit) arguments were removed; these are now passed on to`make_brainGraph`

and can still be specified in the function call.- I now use the
`ggrepel`

package for any`ggplot`

objects with text labels.

2017-09-14

`brainGraph_init`

: fixed bug regarding the use of a custom atlas

- Some function arguments have been modified to reflect the object type (e.g., changing
`g`

to`g.list`

if the function requires a*list*object). `brainGraph_init`

:- New argument
`custom.atlas`

allows you to use an atlas that is not in the package (you must also specify`atlas="custom"`

). - This requires that the atlas you specify already be loaded into the R environment and meet the specifications of the package’s atlases
- It should be a
`data.table`

, and have columns*name*,*x.mni*,*y.mni*,*z.mni*,*lobe*,*hemi*(at a minimum).

- It should be a

- New argument
`permute.group`

: can now calculate`ev.cent`

2017-08-31

`boot_global`

: fixed bug in*modularity*calculation

`boot_global`

:- can omit display of the progress bar (by setting
`.progress=FALSE`

) - can now create weighted networks; to do so, you must choose a weighted metric in the function argument
`measure`

- added some weighted metrics as options for
`measure`

(*strength*,*mod.wt*,*E.global.wt*) - can specify the confidence level (for calculating confidence intervals) via the
`conf`

argument (default: 0.95)

- can omit display of the progress bar (by setting
`set_brainGraph_attr`

:- New argument
`xfm.type`

, which allows you to choose how edge weights should be transformed for calculating distance-based metrics. - The default is the
*reciprocal*(which is what was hard-coded in previous versions). - Other options are:
`1-w`

(subtract weights from 1); and`-log(w)`

(take the negative natural logarithm of weights).

- New argument

`symmetrize_array`

: a convenience function that applies`symmetrize_mats`

along the third dimension of an array`xfm.weights`

: utility function to transform edge weights (necessary when calculating distance-based metrics).

`graph_attr_dt`

and`vertex_attr_dt`

will now include`weighting`

, if present`set_brainGraph_attr`

has 2 new arguments:`weighting`

will create a graph-level attribute indicating how the edges are weighted (e.g., ‘fa’ for FA-weighted tractography networks)`threshold`

will create a graph-level attribute indicating the (numeric) threshold used to create the network (if applicable)

2017-06-10

`mtpc`

: fixed a bug that would incorrectly calculate`A.crit`

`apply_thresholds`

: threshold an additional set of matrices (e.g., FA-weighted matrices in DTI tractography) based on a set of matrices that have already been thresholded (e.g., streamline-weighted matrices in DTI tractography)

`analysis_random_graphs`

: no longer requires a*covars*argument

2017-04-30

`create_mats`

- fixed bug for deterministic tractography when the user would like to normalize the matrices by
*ROI size*. - Fixed bug for when
`threshold.by='density'`

. Previously, it would keep the top*X*% for*each*subject

- fixed bug for deterministic tractography when the user would like to normalize the matrices by

`create_mats`

`threshold.by='consensus'`

is the name of the new default, as this is what is called “consensus-based” thresholding in the literature.`threshold.by='consistency'`

is a new option, for performing*consistency-based*thresholding. See Roberts et al., 2017.

`set_brainGraph_attr`

no longer calculates the graph’s*clique number*, which takes exceedingly long in denser and/or larger graphs (e.g.,`craddock200`

)

2017-04-29

`plot_brainGraph`

: now returns`NA`

(instead of throwing an error) if the specified*subgraph*expression results in a network with 0 vertices.`edge_asymmetry`

fixed bug when the input graph had only one contralateral connection (usually only encountered in the GUI with neighborhood plots)

`create_mats`

: you can specify`threshold.by='mean'`

, which will threshold the matrices such that a connection will be kept if`mean(A_ij) + 2*sd(A_ij) > mat.thresh`

, for each of`mat.thresh`

.

`make_empty_brainGraph`

: this is not a new function, but rather was not exported in previous versions`s_core`

: calculate the*s-core*membership of a graph’s vertices (Eidsaa & Almaas, 2013)- Adds a vertex attributes called
`s.core`

to the graph through`set_brainGraph_attr`

. - Analogous to the
*k-core*but for weighted networks. - The vertex attribute for
*k-core*has been changed from`coreness`

to`k.core`

to distinguish these metrics.

- Adds a vertex attributes called

2017-04-22

`plot_brainGraph_gui`

had multiple issues and a few features have been changed:- Overall execution should be faster than in previous versions
*Lobe*,*neighborhood*, and*community*selection are now in “scrolled windows” instead of drop-down lists. Multiple selections can be made either by pressing`Ctrl`

and clicking, or by holding`Shift`

and moving the arrow keys- Fixed problem with vertex colors
- When choosing to plot
*neighborhoods*, you can color the vertices based on which neighborhood they belong to (useful if multiple vertices are selected)

`gateway_coeff`

returned an error if the number of communities equals 1; this has been fixed

`centr_betw_comm`

: calculate vertex*communicability betweenness centrality*(Estrada et al., 2009)`communicability`

: calculate network*communicability*(Estrada & Hatano, 2008)`mtpc`

: the*multi-threshold permutation correction (MTPC)*method for statistical inference of either vertex- or graph-level measures (Drakesmith et al., 2015)`symmetrize_mats`

: symmetrize a connectivity matrix by either the*maximum*,*minimum*, or*average*of the off-diagonal elements. You may select one of these as an argument to`create_mats`

.

`brainGraph_GLM`

has 2 new function arguments:`level`

allows you to perform inference for graph- or vertex-level measures`perms`

lets you specify the permutation set explicitly

`create_mats`

: All`A.norm.sub`

matrices will be symmetrized, regardless of the value of`threshold.by`

(previously they were only symmetrized if using`threshold.by='density'`

).- This should not pose a problem, as the default (to take the
*maximum*of the off-diagonal elements) is also the default when creating graphs in`igraph`

.

- This should not pose a problem, as the default (to take the

`get.resid`

: no longer requires a*covars*argument, as it was redundant`sim.rand.graph.par`

: the argument*clustering*is no longer TRUE by default

2017-04-10

*First major release; Fifth CRAN release*

`plot_perm_diffs`

previously didn’t work with a low number of permutations, but now will work with any number`sim.rand.graph.par`

previously didn’t work with graphs lacking a`degree`

vertex attribute- Fixed problem with
`plot_brainGraph_GUI`

when plotting in the sagittal view for neighborhood graphs

- Multiple functions now run significantly faster after I updated the code to be more efficient
`permute.group.auc`

has been removed, and now`permute.group`

accepts multiple densities and returns the same results. It can still take a single density for the old behavior- The
`lobe`

and`network`

vertex attributes are now*character*vectors `NBS`

now handles more complex designs and contrasts through`brainGraph_GLM_design`

and`brainGraph_GLM_fit`

. The function arguments are different from previous versions`SPM`

has been removed and is replaced by`brainGraph_GLM`

- Added atlas
`craddock200`

(with coordinates from`DPABI/DPARSF`

)

`brainGraph_GLM`

: replaces`SPM`

and allows for more complex designs and contrasts`brainGraph_GLM_design`

: function that creates a design matrix from a`data.table`

`brainGraph_GLM_fit`

: function that calculates the statistics from a design matrix and response vector`create_mats`

: replaces`dti_create_mats`

and adds functionality for resting-state fMRI data; also can create matrices that will have a specific graph density`gateway_coeff`

: calculate the*gateway coefficient*(Vargas & Wahl, 2014); graphs will have vertex attributes`GC`

or`GC.wt`

(if weighted graph)`plot_brainGraph_multi`

: function to write a PNG file of 3-panel brain graphs (see User Guide for example)

`efficiency`

replaces`graph.efficiency`

; the old function name is still accessible (but may be removed eventually)`set_brainGraph_attr`

replaces`set.brainGraph.attributes`

; the old function name is still accessible (but may be removed eventually)`part_coeff`

replaces`part.coeff`

- All of the
`rich.`

functions have been renamed. The period/point/dot in each of those functions is replaced by the*underscore*. So,`rich.club.norm`

is now`rich_club_norm`

, etc. `set_vertex_color`

and`set_edge_color`

replace`color.vertices`

and`color.edges`

(these functions are not exported, in any case)`contract_brainGraph`

replaces`graph.contract.brain`

`make_ego_brainGraph`

replaces`graph_neighborhood_multiple`

(so it is a similar name to*igraph*’s function`make_ego_graph`

)`write_brainnet`

replaces`write.brainnet`

- In the GUI, vertex order in circle plots now more closely reflect their anatomical position, being ordered by y- and x-coordinates (and within
*lobe*)

2016-10-10

*Fourth CRAN release*

`sim.rand.graph.clust`

previously returned a list; now it correctly returns an`igraph`

graph object`aop`

and`loo`

: regional contributions were calculated incorrectly (without an absolute value)`rich.club.norm`

: changed the p-value calculation again; this shouldn’t affect many results, particularly if N=1,000 (random graphs)`NBS`

:- the
`t.stat`

edge attribute was, under certain situations, incorrectly assigning the values; this has been fixed in the latest version - fixed bug when permutations didn’t result in any connected components
- fixed bug w/ data randomization; the bug didn’t seem to affect the results

- the
`SPM`

:- the permutation p-values were previously incorrect; has been fixed
- added an argument to remove
`NA`

values

`vec.transform`

: fixed bug which occurred when the input vector is the same number repeated (i.e., when`range(x) = 0`

)

`dti_create_mats`

: new function argument`algo`

can be used to specify either ‘probabilistic’ or ‘deterministic’. In the case of the latter, when dividing streamline count by ROI size, you can supply absolute streamline counts with the`mat.thresh`

argument.- Changed instances of
`.parallel`

to`use.parallel`

; also, added it as an argument to`set.brainGraph.attributes`

to control all of the functions that it calls; also added the argument to`part.coeff`

and`within_module_deg_z_score`

- Added atlases
`aal2.94`

,`aal2.120`

, and`dosenbach160`

`plot_brainGraph`

: can now specify the orientation plane, hemisphere to plot, showing a legend, and a character string of logical expressions for plotting subgraphs (previously was in`plot_brainGraph_list`

)

`auc_diff`

: calculates the area-under-the-curve across densities for two groups`cor.diff.test`

: calculates the significance of the difference between correlation coefficients`permute.group.auc`

: does permutation testing across all densities, and returns the permutation distributions for the difference in AUC between two groups`rich.club.attrs`

: give a graph attributes based on rich-club analysis

- Removed the
`x`

,`y`

, and`z`

columns from the atlas data files; now only the MNI coordinates are used. This should simplify adding a personal atlas to use with the package - Added a column,
`name.full`

to some of the atlas data files `NBS`

:- New edge attribute
`p`

, the p-value for that specific connection - Returns the
`p.init`

value for record-keeping

- New edge attribute
`brainGraph_init`

: can now provide a`covars`

data table if you want to subset certain variables yourself, or if the file is named differently from`covars.csv`

`plot_brainGraph`

: can now manually specify a subtitle;`plot_brainGraph_gui`

:- Option for specifying maximum values for edge widths

`plot_corr_mat`

: color cells based on weighted community or network`plot_global`

:- legend position is now “bottom” by default
- can specify
`xvar`

to be either “density” or “threshold”; if the latter, the x-axis is reversed - If data has a
`Study.ID`

column, the`ggplot2`

function`stat_smooth`

is used and the statistic is based on a generalized additive model

`plot_perm_diffs`

: added argument`auc`

for using the area-under-the-curve across densities`plot_rich_norm`

:- Added argument
`fdr`

to choose whether or not to use FDR-adjusted p-values - Should work for more than 2 groups
- Now works with multi-subject data; collapses by
*Group*and plots the group mean

- Added argument
`plot_vertex_measures`

: can facet by different variables (e.g., lobe, community, network, etc.)`set.brainGraph.attributes`

:- calculate graph
`strength`

, which is the mean of vertex strength (weighted networks) - Invert edge weights for distance-based measures

- calculate graph
`write.brainnet`

:- Now allows for writing weighted adjacency matrices, using the
`edge.wt`

function argument - Can color vertices by multiple variables

- Now allows for writing weighted adjacency matrices, using the

2016-04-22

*Third CRAN release*

`rich.club.norm`

had a bug in calculating the p-values. If you have already gone through the process of creating random graphs and the object`phi.norm`

, you can fix with the following code: (add another loop if you have single-subject graphs, e.g. DTI data)

```
for (i in seq_along(groups)) {
for (j in seq_along(densities)) {
max.deg <- max(V(g[[i]][[j]])$degree)
phi.norm[[i]][[j]]$p <- sapply(seq_len(max.deg), function(x)
sum(phi.norm[[i]][[j]]$phi.rand[, x] >= phi.norm[[i]][[j]]$phi.orig[x]) / N)
}
}
```

where `N`

is the number of random graphs generated. * `dti_create_mats`

: there was a bug when *sub.thresh* equals 0; it would take matrix entries, even if they were below the *mat.thresh* values. This has been fixed. Argument checking has also been added.

- Now requires the package
`RcppEigen`

for fast linear model calculations; resulted in major speed improvements - Now requires the package
`permute`

for the`NBS`

function `group.graph.diffs`

:- Uses the function
`fastLmPure`

from`RcppEigen`

for speed/efficiency - Can specify multiple alternative hypotheses
- Linear model specification is more limited now, though

- Uses the function
- Added data table for the
`destrieux.scgm`

atlas

`SPM`

: new function that replaces and improves upon both`group.graph.diffs`

and`permute.vertex`

`NBS`

: implements the network-based statistic`analysis_random_graphs`

: perform all the steps for getting*small-world*parameters and normalized*rich-club*coefficients and p-values`plot_global`

: create a line plot across all densities of global graph measures in the same figure`vertex_spatial_dist`

: calculates the mean edge distance for all edges of a given vertex

`dti_create_mats`

: changed a few arguments`edge_spatial_dist`

: re-named from`spatial.dist`

`group.graph.diffs`

: returns a graph w/ spatial coord’s for plotting`plot_brainGraph_list`

:- You can now specify a condition for removing vertices (e.g.
`hemi == "R"`

will keep only right hemisphere vertices; includes complex logical expressions (i.e., with multiple ‘&’ and ‘|’ conditions) - Vertex sizing and coloring is a bit more flexible

- You can now specify a condition for removing vertices (e.g.
- New vertex attribute
`Lp`

(average path length for each vertex) `plot_brainGraph_gui`

:- Added a checkbox for displaying a color legend or not
- Can color vertices by weighted community membership
- Added an
*Other*option for adjusting edge widths by a custom attribute - More options for adjusting vertex sizes when the graph is weighted
- Made the GUI window more compact to fit lower screen resolutions

`plot_rich_norm`

:- New argument
`facet.by`

to group the plots by either “density” (default) or “threshold” (for multi-subject, e.g. DTI data)

- New argument
`set.brainGraph.attributes`

: New calculations for weighted graphs:*Modularity*and community membership*Participation coefficient*and*within-module degree z-score*- Vertex-level
*transitivity* - Vertex-level
*shortest path lengths*

2015-12-24

*Second CRAN release*

`aop`

and`loo`

calculate measures of*individual contribution*(see Reference within the function help)- Now requires the package
`ade4`

- Now requires the package
`plot_boot`

: new function based on the removed plotting code from`boot_global`

`plot_rich_norm`

: function to plot normalized rich club coefficient curves

`boot_global`

:- added an OS check to get multicore functionality on Windows
- removed the code that created some plots
- updated to work with the newer version of
`corr.matrix`

`brainGraph_init`

:- does a better job of dealing with subcortical gray matter data
- now also returns the “tidied” dataset

`corr.matrix`

:- was basically reverted back for speed purposes
- minor syntax change

`count_interlobar`

no longer takes`atlas.dt`

as an argument`dti_create_mats`

now accepts argument`P`

for “number of samples”`edge_asymmetry`

now works on Windows (changed from*mclapply*to*foreach*)`get.resid`

:- got a complete overhaul; now works with
*data.table*syntax - now returns
*data.table*of residuals with a*Study.ID*column - fixed minor bug when
`use.mean=FALSE`

but*covars*has columns*mean.lh*and/or*mean.rh*; fixed minor bug w/ RH residual calculation - fixed bug when
`use.mean=TRUE`

(syntax error for RH vertices)

- got a complete overhaul; now works with
`graph.efficiency`

: now works on Windows (changed from*mclapply*to*foreach*)`part.coeff`

: has a workaround to work on Windows`permute.group`

:- updated to work with new version of
`corr.matrix`

- no longer takes
`atlas.dt`

as an argument

- updated to work with new version of
`vertex_attr_dt`

is now essentially a wrapper for`igraph`

’s function`as_data_frame`

Exported

`plot_perm_diffs`

Added argument checking for most functions

2015-12-08

*Initial CRAN release*