Rabies

**Documentation:** new data quality assessment documentation page, which documents how the reports generated using --data_diagnosis at the analysis stage can inform quality control at the analysis stage

**New parameters:**
- --includion_ids/--exclusion_ids: these new parameters allow to specify which list of scan should be included/excluded, at any stage of the pipeline
- --bids_filter: allow to specify which BIDS filters to use to select the functional and anatomical files of interest
- --oblique2card: new option to modify the affine in oblique images so these image don't raise an error at later stages
- --inherit_unbiased_template: this novel option allows providing the path to preprocessing outputs from a previous RABIES run, and use the already-generated unbiased template and register images directly onto it instead of creating a new one

**Docker container and testing:**
- important re-writing of the Dockerfile. The container is much smaller, using only minimal requirements from ANTs, AFNI and FSL, and constructing conda environment based on exact dependencies
- container built and maintained on Github https://github.com/CoBrALab/RABIES/pkgs/container/rabies instead of docker hub
- testing with error_check_rabies.py is more complete (i.e. now tests almost all parameters across pipeline stages), and can take in custom commands to test. Complete testing is also conducted during container build
- we've attached to this release a pre-built singularity image for version 0.5.1 (the file is 1.8Gb). This image can be downloaded and used directly instead of building the container from scratch using singularity.

* Introduced new QC visual outputs for the denoising steps as well as some temporal diagnosis (tSTD,tSNR)
* all data outputs from the analysis are in .csv or .nii.gz formats
* upgraded generic registration scripts to latest version as implemented in https://github.com/CoBrALab/minc-toolkit-extras/blob/master/antsRegistration_affine_SyN.sh
* introduction of a new bias field correction strategy which relies on iterative otsu masking for more robust correction of EPIs

RABIES image processing workflow was extended to include confound regression and some basic functional connectivity analysis within a unified workflow.
The novel features, as well as their usage and outputs, are all described in the README.

Improved the memory specifications for running in parallel through SGE and MultiProc.
Changed the resampling the options to user-defined resampling dimensions in native and/or common spaces.
Can now specify the data type of output files to control for file size.
Fixed an issue where the STC option couldn't be turned off.
The detection of dummy scans to generate a reference EPI volume is now optional, and is turned off by default.
The boolean parser options are now controlled through action=store_true

Fixed bugs from the previous version on commonspace registration. Now provides complete installation of nifti format for the DSURQE template (without mnc2nii conversion) and provides a vascular mask as well for confound regression. The docker container is available on Docker hub and can be downloaded as a singularity image.