Package: afni Version: 0.20100917~dfsg.1-1~lucid.nd1 Architecture: amd64 Maintainer: Michael Hanke Installed-Size: 28128 Depends: afni-common (= 0.20100917~dfsg.1-1~lucid.nd1), tcsh, gifsicle, libjpeg-progs, freeglut3, lesstif2 (>= 1:0.94.4), libc6 (>= 2.11), libf2c2, libgiftiio0, libgl1-mesa-glx | libgl1, libglib2.0-0 (>= 2.12.0), libglu1-mesa | libglu1, libglw1-mesa | libglw1, libgomp1 (>= 4.2.1), libgsl0ldbl (>= 1.9), libgts-0.7-5 (>= 0.7.6), libice6 (>= 1:1.0.0), libnetcdf4, libnifti1 (>> 1.1.0-2), libsm6, libvolpack1, libx11-6 (>= 0), libxext6 (>= 0), libxi6, libxmu6, libxt6 Recommends: nifti-bin, bzip2, ffmpeg Homepage: http://afni.nimh.nih.gov Priority: extra Section: science Filename: pool/main/a/afni/afni_0.20100917~dfsg.1-1~lucid.nd1_amd64.deb Size: 11176416 SHA256: 5919e5f2bb301c55b16c55172c0d31d10c4199aff1abac915dc121162d593a60 SHA1: e484bf3f2a715b1f029572d6df3a8c82dae16fa6 MD5sum: 71725715f89ea019429092e115ef59da Description: toolkit for analyzing and visualizing functional MRI data AFNI is an environment for processing and displaying functional MRI data. It provides a complete analysis toolchain, including 3D cortical surface models, and mapping of volumetric data (SUMA). . In addition to its own format AFNI understands the NIfTI format and is therefore easily usable in combination with FSL and Freesurfer. Package: afni-common Source: afni Version: 0.20100917~dfsg.1-1~lucid.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 6544 Depends: python, tcsh Homepage: http://afni.nimh.nih.gov Priority: extra Section: science Filename: pool/main/a/afni/afni-common_0.20100917~dfsg.1-1~lucid.nd1_all.deb Size: 3960960 SHA256: 4bb167b893b2884a8b25525f9f47a340cb75c4d35e29dc950cfa4e4ecb0d5f56 SHA1: 64eaec07a6e2446e98deca247e2682e74693b55b MD5sum: b643cdb626704de706674fe56afce0a6 Description: miscellaneous scripts and data files for AFNI This package provides the required architecture independent parts of AFNI. Package: afni-dev Source: afni Version: 0.20100917~dfsg.1-1~lucid.nd1 Architecture: amd64 Maintainer: Michael Hanke Installed-Size: 14992 Homepage: http://afni.nimh.nih.gov Priority: extra Section: science Filename: pool/main/a/afni/afni-dev_0.20100917~dfsg.1-1~lucid.nd1_amd64.deb Size: 3866054 SHA256: c7c2061faa44b91c6f15858818e4639e75e6e34209a2fcfb4e24dadeee603040 SHA1: 0a6d2e75d10a615fbf1352fd6a34d078974283e9 MD5sum: ea8b321ed98043d60d69278191e4ce47 Description: header and static libraries for AFNI plugin development AFNI is an environment for processing and displaying functional MRI data. It provides a complete analysis toolchain, including 3D cortical surface models, and mapping of volumetric data (SUMA). . This package provides the necessary libraries and header files for AFNI plugin development. Package: ants Version: 1.9+svn532-4~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 39084 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libinsighttoolkit3.16, libstdc++6 (>= 4.4.0) Suggests: fsl, gridengine-client Homepage: http://www.picsl.upenn.edu/ANTS/ Priority: extra Section: science Filename: pool/main/a/ants/ants_1.9+svn532-4~lucid.nd1_amd64.deb Size: 11565954 SHA256: 992131a96a4503eff8970070d1d96276d0c6297bc1bae0afe17e65e9e52efede SHA1: 24b51529bcf5b1e447d2ceecc578d5d193f5ff95 MD5sum: 0cbc4a90fba2591bf4331f93a26b01b2 Description: advanced normalization tools for brain and image analysis Advanced Normalization Tools (ANTS) is an ITK-based suite of normalization, segmentation and template-building tools for quantitative morphometric analysis. Many of the ANTS registration tools are diffeomorphic, but deformation (elastic and BSpline) transformations are available. Unique components of ANTS include multivariate similarity metrics, landmark guidance, the ability to use label images to guide the mapping and both greedy and space-time optimal implementations of diffeomorphisms. The symmetric normalization (SyN) strategy is a part of the ANTS toolkit as is directly manipulated free form deformation (DMFFD). Package: arno-iptables-firewall Version: 1.9.2.k-3~lucid.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 844 Depends: iptables (>= 1.2.11), gawk, debconf (>= 1.3.22) | cdebconf (>= 0.43), debconf (>= 0.5) | debconf-2.0, iproute Recommends: lynx, dnsutils Homepage: http://rocky.eld.leidenuniv.nl/ Priority: optional Section: net Filename: pool/main/a/arno-iptables-firewall/arno-iptables-firewall_1.9.2.k-3~lucid.nd1_all.deb Size: 132470 SHA256: 2e22eebc483d94eeac7b8dede959f75add3268e432e7a878c5d39e61a129c58a SHA1: 51c20f3f9a968af47adc9bc6dff312a0ad1abd2a MD5sum: b24e68173b3ce290b28f85c017f3a7b6 Description: single- and multi-homed firewall script with DSL/ADSL support Unlike other lean iptables frontends in Debian, arno-iptables-firewall will setup and load a secure, restrictive firewall by just asking a few question. This includes configuring internal networks for internet access via NAT and potential network services (e.g. http or ssh). . However, it is in no way restricted to this simple setup. Some catch words of additional features, that can be enabled in the well documented configuration file are: DSL/ADSL, Port forwarding, DMZ's, portscan detection, MAC address filtering. Package: biosig-tools Source: biosig4c++ Version: 0.94.1+svn2521-1~pre0~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 656 Depends: libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/biosig-tools_0.94.1+svn2521-1~pre0~lucid.nd1_amd64.deb Size: 250066 SHA256: 269c93884903d4f46bf83ce1569795e123d313f942f8d9fbd585f43d54eccb2b SHA1: 3e626e145dfcc63813a7219d5626d66ea2d8b69c MD5sum: f5f2e5d4313cf731f171bff19acb08b4 Description: format conversion tools for biomedical data formats Based on libbiosig4c++ library, this package provides command line tools, such as * save2gdf: converter between different file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF, BDF, CWFB. save2gdf can be also used to upload or retrieve data from a bscs server. TODO... Extend? ship client/server? Package: caret-data Version: 5.6~dfsg.1-1 Architecture: all Maintainer: Michael Hanke Installed-Size: 236780 Homepage: http://brainmap.wustl.edu/caret Priority: optional Section: science Filename: pool/main/c/caret-data/caret-data_5.6~dfsg.1-1_all.deb Size: 175205418 SHA256: 329a14cfd5547064496d4f6909db62578412857ad7d5f73e129335481f550b47 SHA1: 7d6cbdd77b04f258327d2bc9fcc4b0494fcf71bc MD5sum: e5f41497554088124975dfc27ba6378b Description: common data files for Caret This package provides online help, tutorials and atlas datasets for Caret. Package: dicomnifti Version: 2.28.14-2~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 524 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnifti2, libstdc++6 (>= 4.4.0) Homepage: http://cbi.nyu.edu/software/dinifti.php Priority: optional Section: science Filename: pool/main/d/dicomnifti/dicomnifti_2.28.14-2~lucid.nd1_amd64.deb Size: 158658 SHA256: b2005d847469a33bcd594ba4a3202e373a41ceb7327f72dc222f8e09934c9656 SHA1: 045409c68a634ae0e86619f76ba5ff3afdf2386c MD5sum: b0ee895556cce5f212e0d20bc00568c5 Description: converts DICOM files into the NIfTI format The dinifti program converts MRI images stored in DICOM format to NIfTI format. The NIfTI format is thought to be the new standard image format for medical imaging and can be used with for example with FSL, AFNI, SPM, Caret or Freesurfer. . dinifti converts single files, but also supports fully automatic batch conversions of complete dicomdirs. Additionally, converted NIfTI files can be properly named, using image series information from the DICOM files. Package: fslview Version: 3.1.8+4.1.6-2~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 4168 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnewmat10ldbl, libnifti2, libqt3-mt (>= 3:3.3.8-b), libqwt4c2, libstdc++6 (>= 4.4.0), libvtk5.2, libvtk5.2-qt3 Recommends: fslview-doc Suggests: fsl-atlases Conflicts: fsl-fslview Replaces: fsl-fslview Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: science Filename: pool/main/f/fslview/fslview_3.1.8+4.1.6-2~lucid.nd1_amd64.deb Size: 1524282 SHA256: bd1c21144d8fb58994970e16b6a35e888f4302d8051a261c20118b0f33253c4a SHA1: ad70cfd2d70edbf4e365c429a0d314ccfaddbd1c MD5sum: ce50c0711e8d662f355f65b23914bd4c Description: viewer for (f)MRI and DTI data This package provides a viewer for 3d and 4d MRI data as well as DTI images. FSLView is able to display ANALYZE and NIFTI files. The viewer supports multiple 2d viewing modes (orthogonal, lightbox or single slices), but also 3d volume rendering. Additionally FSLView is able to visualize timeseries and can overlay metrical and stereotaxic atlas data. . FSLView is part of FSL. Package: fslview-doc Source: fslview Version: 3.1.8+4.1.6-2~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3216 Depends: qt3-assistant Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_3.1.8+4.1.6-2~lucid.nd1_all.deb Size: 2378978 SHA256: e281d1abfbaced29fa7c68ed41607acf19ebbd20e5e0a77a024717153dcb658c SHA1: 2f911edd963e4a484195d720ad3fa669eab86a48 MD5sum: 221a08bcb9ac0db48525a6b7e6e4bed2 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gifti-bin Source: gifticlib Version: 1.0.9-1~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 128 Depends: libc6 (>= 2.3.4), libexpat1 (>= 1.95.8), libgiftiio0, libnifti2, zlib1g (>= 1:1.1.4) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: utils Filename: pool/main/g/gifticlib/gifti-bin_1.0.9-1~lucid.nd1_amd64.deb Size: 29244 SHA256: 46bf711ca88f18e5ad2d2e175bb3cec6925c901aeb45eca7243a84c2820921d1 SHA1: 76d719b5e81cdd6aeba7fafc4762209f1e9a7daa MD5sum: 939e433013601359b181258a85041993 Description: tools shipped with the GIFTI library GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package provides the tools that are shipped with the library (gifti_tool and gifti_test). Package: itksnap Version: 2.0.0+cvs20100615-1~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 8600 Depends: libc6 (>= 2.4), libfltk1.1 (>= 1.1.8~rc1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libinsighttoolkit3.16, libstdc++6 (>= 4.4.0), libvtk5.2 Homepage: http://www.itksnap.org Priority: extra Section: science Filename: pool/main/i/itksnap/itksnap_2.0.0+cvs20100615-1~lucid.nd1_amd64.deb Size: 3689532 SHA256: 50f15256db9482ce1f8ca61042c16233e305b0e3fea12e135c3f75b5aa7e2610 SHA1: 0315fad5516f0e11a3022effdce8cf21ca42fbeb MD5sum: aea2cf534e0b8e19efe7a232a3cf977a Description: semi-automatic segmentation of structures in 3D images SNAP provides semi-automatic segmentation of structures in medical images (e.g. magnetic resonance images of the brain) using active contour methods, as well as manual delineation and image navigation. Noteworthy features are: . * Linked cursor for seamless 3D navigation * Manual segmentation in three orthogonal planes at once * Support for many different 3D image formats, including NIfTI * Support for concurrent, linked viewing and segmentation of multiple images * Limited support for color images (e.g., diffusion tensor maps) * 3D cut-plane tool for fast post-processing of segmentation results Package: libbiosig-dev Source: biosig4c++ Version: 0.94.1+svn2521-1~pre0~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1608 Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_0.94.1+svn2521-1~pre0~lucid.nd1_amd64.deb Size: 375540 SHA256: be1230c8e9f65eadb3078e25fe9e6a84dc183c17077fb42f08807ad1264efd79 SHA1: f8eb41dc5c8b83fe97f7a92f30785c377a0c6322 MD5sum: e2c01b2305e620db4a95b5ea13b4dbf8 Description: library for accessing files in biomedical data formats A library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://hci.tugraz.at/schloegl/biosig/TESTED . . This package provides header files and static library. Package: libbiosig0 Source: biosig4c++ Version: 0.94.1+svn2521-1~pre0~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 888 Depends: libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1) Homepage: http://biosig.sf.net/ Priority: extra Section: libs Filename: pool/main/b/biosig4c++/libbiosig0_0.94.1+svn2521-1~pre0~lucid.nd1_amd64.deb Size: 299910 SHA256: 50b14ea361d6a7bc0b5a95c92ac520f2adc571fb26c449c9d2ee923b911f792d SHA1: 4d7f5776f89c1dfb684bccc04661b8ed6015dbee MD5sum: 4faa44edad17fad1d6f8e4c6cae24641 Description: library for accessing files in biomedical data formats A library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://hci.tugraz.at/schloegl/biosig/TESTED . . This package provides dynamic library. Package: libgiftiio-dev Source: gifticlib Version: 1.0.9-1~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 256 Depends: libgiftiio0 (= 1.0.9-1~lucid.nd1) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: libdevel Filename: pool/main/g/gifticlib/libgiftiio-dev_1.0.9-1~lucid.nd1_amd64.deb Size: 64998 SHA256: 45f0db22721e17782f2d36f92ffd6b9ffd20a0f9613d56f0c2aaa846c4ca343f SHA1: 195febb32e6113c88617a7d78e8694e4881f7341 MD5sum: ae42e1bb953ebbe14e59986566635243 Description: IO library for the GIFTI cortical surface data format GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package provides the header files and static library. Package: libgiftiio0 Source: gifticlib Version: 1.0.9-1~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 180 Depends: libc6 (>= 2.4), libexpat1 (>= 1.95.8), libnifti2, zlib1g (>= 1:1.1.4) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: libs Filename: pool/main/g/gifticlib/libgiftiio0_1.0.9-1~lucid.nd1_amd64.deb Size: 57594 SHA256: f8b85858f70219645839d9e3109129208ad79a9a1546eb1a1e7f875845109718 SHA1: 00e59c2b2422da35b1a8c8e35e9714285deb93c0 MD5sum: 0ed5c2dde1ffd75079078e4c82424eac Description: IO library for the GIFTI cortical surface data format GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package contains the shared library. Package: libnifti-dev Source: nifticlib Version: 2.0.0-1~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 624 Depends: libnifti2 (= 2.0.0-1~lucid.nd1) Conflicts: libfslio-dev, libnifti0-dev, libnifti1-dev, libniftiio-dev Replaces: libnifti1-dev Homepage: http://niftilib.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/n/nifticlib/libnifti-dev_2.0.0-1~lucid.nd1_amd64.deb Size: 171290 SHA256: 113ba42ad361ebf6f7b7b2c429c02857738472be1f23bbcb2232b18cfc334d74 SHA1: 0d49b66ca760523e35331ceafa85d046a0d8d5d3 MD5sum: 9823b6daab84532d63e7dd88020e1a5a Description: IO libraries for the NIfTI-1 data format Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the header files and static libraries of libniftiio, znzlib and libnifticdf. Package: libnifti-doc Source: nifticlib Version: 2.0.0-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1896 Homepage: http://niftilib.sourceforge.net Priority: optional Section: doc Filename: pool/main/n/nifticlib/libnifti-doc_2.0.0-1~lucid.nd1_all.deb Size: 245482 SHA256: 8532e280818991cfdd6fe8e883f0bb5b3d0ca9bb86360cd2fcb98a2750f01720 SHA1: 39744b584b030525f62bd876863ebfadcac7e9ff MD5sum: cb7b7605b2710de9700586340deca337 Description: NIfTI library API documentation Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the library API reference documentation. Package: libnifti2 Source: nifticlib Version: 2.0.0-1~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 336 Depends: libc6 (>= 2.7), zlib1g (>= 1:1.1.4) Homepage: http://niftilib.sourceforge.net Priority: optional Section: libs Filename: pool/main/n/nifticlib/libnifti2_2.0.0-1~lucid.nd1_amd64.deb Size: 123024 SHA256: ba53d9dd6ebe7abd552f39127d2bc06896d367be47525ec0e29867fc833b54b0 SHA1: 33b02302b67518c8136e29fb7a52c677f78514ff MD5sum: 010aa718f728757323244a44e422706c Description: IO libraries for the NIfTI-1 data format Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package contains the shared library of the low-level IO library niftiio, low-level IO library znzlib and the nifticdf shared library that provides functions to compute cumulative distributions and their inverses. Package: libodin-dev Source: odin Version: 1.8.1-3~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 21020 Homepage: http://od1n.sourceforge.net Priority: extra Section: libdevel Filename: pool/main/o/odin/libodin-dev_1.8.1-3~lucid.nd1_amd64.deb Size: 4200000 SHA256: 4d0d631eaa5d7de777cd874a0eecc53b9ec6eaaad7780e6edd1920ca0ae9f423 SHA1: 95586553e269981ec43c4d5d8023425ce3a75209 MD5sum: 0f18c7171b948f54416b1a66f594a64e Description: static libraries and header for ODIN sequences This package provides static libraries and headers of the ODIN libraries odindata, adinpara, odinqt, odinseq and tjutils. They are required for building magnetic resonance imaging (MRI) sequences with ODIN. Package: libopenmeeg-dev Source: openmeeg Version: 2.0.0.dfsg-2~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 276 Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: libdevel Filename: pool/main/o/openmeeg/libopenmeeg-dev_2.0.0.dfsg-2~lucid.nd1_amd64.deb Size: 43856 SHA256: b0f360865c523d9912f14db82537e8202c02a4b64387b5e14f30231ce05f242d SHA1: 232d51205a9618c0020027427b543e87dfb1eeea MD5sum: f4b6ec1254a767e780bf41c692522849 Description: library for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides static libraries and header files. Package: libopenmeeg1 Source: openmeeg Version: 2.0.0.dfsg-2~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 960 Depends: libatlas3gf-base | libatlas.so.3gf, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libmatio0, libstdc++6 (>= 4.4.0) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/libopenmeeg1_2.0.0.dfsg-2~lucid.nd1_amd64.deb Size: 252508 SHA256: 9b38eae709217b949c4fb23666f10060d501153b4bd3e46fa79a8bb577a9c6a8 SHA1: dd48a79df4a904587ae6609d51b4761d8bc9f648 MD5sum: 94b5eb327f14882834730ceb33299fe0 Description: library for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. Package: lipsia Version: 1.6.0-4~lucid.nd1 Architecture: amd64 Maintainer: Michael Hanke Installed-Size: 3940 Depends: libc6 (>= 2.7), libdcmtk1 (>= 3.5.4), libfftw3-3, libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libgsl0ldbl (>= 1.9), libice6 (>= 1:1.0.0), libnifti2, libqt3-mt (>= 3:3.3.8-b), libsm6, libstdc++6 (>= 4.4.0), libvia0, libx11-6, libxext6, zlib1g (>= 1:1.1.4), via-bin Recommends: dcmtk, lipsia-doc Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: science Filename: pool/main/l/lipsia/lipsia_1.6.0-4~lucid.nd1_amd64.deb Size: 1350678 SHA256: e58b7a05472046790c2f66fc0f171f874baeee44138732e2afd4660f7a6e29ce SHA1: 2dcd43247f3fb012a68ea1a66d4d324608ceba1a MD5sum: f8464286082303e2b11f90212c5321bc Description: analysis suite for MRI and fMRI data Leipzig Image Processing and Statistical Inference Algorithms (LIPSIA) . This is a software package for the data processing and evaluation of functional magnetic resonance images. The analysis of fMRI data comprises various aspects including filtering, spatial transformation, statistical evaluation as well as segmentation and visualization. All these aspects are covered by LIPSIA. For the statistical evaluation, a number of well established and peer-reviewed algorithms were implemented in LIPSIA that allow an efficient and user-friendly processing of fMRI data sets. As the amount of data that must be handled is enormous, an important aspect in the development of LIPSIA was the efficiency of the software implementation. . LIPSIA operates exclusively on data in the VISTA data format. However, the package contains converters for medical image data in iBruker, ANALYZE and NIfTI format -- converting VISTA images into NIfTI files is also supported. Package: lipsia-doc Source: lipsia Version: 1.6.0-4~lucid.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 7004 Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/l/lipsia/lipsia-doc_1.6.0-4~lucid.nd1_all.deb Size: 5539278 SHA256: fe6e759a40698cc35033edebd496733ddbb082eeeda66fcddb091906f0f1238e SHA1: 435a391bce4fdc0bbb017bf0214f4f42e18bec55 MD5sum: 2dddb5c30d3c0cbdfaa56523cc87dd21 Description: documentation for LIPSIA Leipzig Image Processing and Statistical Inference Algorithms (LIPSIA) . This package provides the LIPSIA documentation in HTML format. Package: mitools Source: odin Version: 1.8.1-3~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 6980 Depends: libblitz0ldbl (>= 0.9), libc6 (>= 2.11), libdcmtk1 (>= 3.5.4), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libnifti2, liboil0.3 (>= 0.3.10), libpng12-0 (>= 1.2.13-4), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.5.3), libqwt5-qt4, libstdc++6 (>= 4.4.0), libvia0, libvtk5.2, zlib1g (>= 1:1.1.4), dcmtk Recommends: grace Homepage: http://od1n.sourceforge.net Priority: extra Section: science Filename: pool/main/o/odin/mitools_1.8.1-3~lucid.nd1_amd64.deb Size: 2453670 SHA256: 40cc6bc31ecb80983c8f89860d92a63c083f18450efe48242565ed7d157bc9ec SHA1: c3c5a30d5978224bba135229ed48cf0cfa2cb8c5 MD5sum: 88ca85cfc6dcd978837f1d025b8851a8 Description: view, convert and perform basic maths with medical image datasets The three contained tools micalc, miconv and miview are handy command-line utilities for converting, manipulating and viewing medical image data in various formats (DICOM, NIfTI, PNG, binary data, ...). Package: mni-colin27-minc Source: mni-colin27 Version: 1.1-1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 12064 Homepage: http://packages.bic.mni.mcgill.ca/tgz/ Priority: extra Section: science Filename: pool/main/m/mni-colin27/mni-colin27-minc_1.1-1_all.deb Size: 12274320 SHA256: 53c6b97ed6d4182fd4da2502377bc1f32de4a816952eab6037e8791d85828fd0 SHA1: 467a57c00040530e387ed1183815d8591b32b2e6 MD5sum: 1ea73688b743b36778bee148076ebd4d Description: Talairach stereotaxic space template This template MRI volume was created from 27 T1-weighted MRI scans of a single individual that have been transformed into the Talairach stereotaxic space. The anatomical image is complemented by a brain and a head mask. All images are in 1x1x1 mm resolution. . This package provides the template in MINC format. Package: mni-colin27-nifti Source: mni-colin27 Version: 1.1-1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 11748 Homepage: http://packages.bic.mni.mcgill.ca/tgz/ Priority: extra Section: science Filename: pool/main/m/mni-colin27/mni-colin27-nifti_1.1-1_all.deb Size: 11952134 SHA256: 73bbe01f4f42fe966fc9308b46cedca16cda981da0492975afaaa9f731bf5581 SHA1: e1fa1c293312ea699493d2158efe70dd6649840e MD5sum: b9228cbbbd551e91de94f28d8f4da2ea Description: Talairach stereotaxic space template This template MRI volume was created from 27 T1-weighted MRI scans of a single individual that have been transformed into the Talairach stereotaxic space. The anatomical image is complemented by a brain and a head mask. All images are in 1x1x1 mm resolution. . This package provides the template in NIfTI format. Package: mni-icbm152-nlin-2009a Source: mni-icbm152-nlin Version: 0.20090623.1-1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 120332 Homepage: http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009 Priority: extra Section: science Filename: pool/main/m/mni-icbm152-nlin/mni-icbm152-nlin-2009a_0.20090623.1-1_all.deb Size: 122770998 SHA256: d1cab63c136b6898ce133ae0ac5309b77ac11774317e4710a3c567689bc64435 SHA1: 3c310421152cc482d8d99dca51d3c0145dab6553 MD5sum: dc79aa787955aa03e0d60c98cc5c3da3 Description: MNI stereotaxic space human brain template This is an unbiased standard magnetic resonance imaging template volume for the normal human population. It has been created by the Montreal Neurological Institute (MNI) using anatomical data from the International Consortium for Brain Mapping (ICBM). . The package provides a 1x1x1 mm resolution template (hemissphere-symetric and asymetric non-linearily co-registered versions), including T1w, T2w, PDw modalities, T2 relaxometry, and tissue probability maps. In addition, it contains a lobe atlas, and masks for brain, eyes and face. . The template is similar to the one in the mni-icbm152-nlin-2009c package. However, the sampling of the ICBM data is different and here intensity inhomogeneity correction was performed by N3 version 1.10.1, leading to different tissue probability maps. Package: mni-icbm152-nlin-2009b Source: mni-icbm152-nlin Version: 0.20090623.1-1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 722392 Homepage: http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009 Priority: extra Section: science Filename: pool/main/m/mni-icbm152-nlin/mni-icbm152-nlin-2009b_0.20090623.1-1_all.deb Size: 739142896 SHA256: d00b100f1a65e1e3909adddd1f9a9d3fb4c717ced6f425089a276b64780cd13d SHA1: 5d4b4d340be14ed062a5e96bc43af33d2fd9ec40 MD5sum: 0e906ff84b016a127d7cc1ecf03dfbef Description: MNI stereotaxic space human brain template This is an unbiased standard magnetic resonance imaging template volume for the normal human population. It has been created by the Montreal Neurological Institute (MNI) using anatomical data from the International Consortium for Brain Mapping (ICBM). . The package provides a 0.5x0.5x0.5 mm resolution template (hemissphere-symetric and asymetric non-linearily co-registered versions), including T1w, T2w, PDw modalities. Package: mni-icbm152-nlin-2009c Source: mni-icbm152-nlin Version: 0.20090623.1-1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 113888 Homepage: http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009 Priority: extra Section: science Filename: pool/main/m/mni-icbm152-nlin/mni-icbm152-nlin-2009c_0.20090623.1-1_all.deb Size: 116182926 SHA256: 08c25ff6564fd6860d96bf32a182ce5922aaadd2b584850d82762aa9bb4fefd5 SHA1: f515a99b9edc7dfabfb058a21acbab12a2031c1a MD5sum: c1ec21de0bd62ab68c1d2a84655d4891 Description: MNI stereotaxic space human brain template This is an unbiased standard magnetic resonance imaging template volume for the normal human population. It has been created by the Montreal Neurological Institute (MNI) using anatomical data from the International Consortium for Brain Mapping (ICBM). . The package provides a 1x1x1 mm resolution template (hemissphere-symetric and asymetric non-linearily co-registered versions), including T1w, T2w, PDw modalities, and tissue probability maps. In addition, it contains a lobe atlas, and masks for brain, eyes and face. . The template is similar to the one in the mni-icbm152-nlin-2009a package. However, the sampling of the ICBM data is different and here intensity inhomogeneity correction was performed by N3 version 1.11, leading to different tissue probability maps. Package: mricron Version: 0.20100820.1~dfsg.1-1~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 15652 Depends: libatk1.0-0 (>= 1.29.3), libc6 (>= 2.2.5), libcairo2 (>= 1.2.4), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.8.0), libpango1.0-0 (>= 1.14.0), libx11-6 (>= 0), mricron-data Suggests: mricron-doc Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron_0.20100820.1~dfsg.1-1~lucid.nd1_amd64.deb Size: 4875904 SHA256: ae541582f92beb0ed0ea5dbe74422539fca04fee8a64067442e58493daf78719 SHA1: 3edb223c84f60c82855052eb406867a4f4919610 MD5sum: de4daa8a630835ee027e178c3b212e61 Description: magnetic resonance image conversion, viewing and analysis This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . In addition to 'mricron', this package also provides 'dcm2nii' that supports converting DICOM and PAR/REC images into the NIfTI format, and 'npm' for non-parametric data analysis. Package: mricron-data Source: mricron Version: 0.20100820.1~dfsg.1-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1852 Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20100820.1~dfsg.1-1~lucid.nd1_all.deb Size: 1665590 SHA256: 32e24b2c5736a376850c7f9b6292bb40b6febd8442a97ed0aeec01b556090afd SHA1: 9645ed7465bb70cabe5e9bccc0e000ff55d842e8 MD5sum: 3b3c7a0e3272778356ff4b8b338d79be Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20100820.1~dfsg.1-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1220 Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20100820.1~dfsg.1-1~lucid.nd1_all.deb Size: 737188 SHA256: 39812dcfbce1b27faec708965260590aca72bcd738bebd724481e5546128ecb9 SHA1: a69c75a7cedda00d6c17dd50f9810824bc8e83e9 MD5sum: 04b81a73eef6da963ce54ac1c7df631c Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: nifti-bin Source: nifticlib Version: 2.0.0-1~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 200 Depends: libc6 (>= 2.7), libnifti2 Homepage: http://niftilib.sourceforge.net Priority: optional Section: utils Filename: pool/main/n/nifticlib/nifti-bin_2.0.0-1~lucid.nd1_amd64.deb Size: 62296 SHA256: 1355d318a693371abf40cf3a424a4e9a2040e5ef66c7993f0597bc3506d4991b SHA1: dfcc4f30646e93220e188ad57c8831b397e15e23 MD5sum: e611321eba41d61af7443e9c1c9cc3db Description: tools shipped with the NIfTI library Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the tools that are shipped with the library (nifti_tool, nifti_stats and nifti1_test). Package: octave-biosig Source: biosig4c++ Version: 0.94.1+svn2521-1~pre0~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1608 Depends: libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libfftw3-3, libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.3), libhdf5-serial-1.8.4 | libhdf5-1.8.4, liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libncurses5 (>= 5.6+20071006-3), libreadline6 (>= 6.0), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/octave-biosig_0.94.1+svn2521-1~pre0~lucid.nd1_amd64.deb Size: 583288 SHA256: 1d11bcd47dcf067e11ff9fc499078cc75f90346549894cca564d2fa5a19c71f9 SHA1: 48c3583c04e344e81224da567d30f082df046e8b MD5sum: 0e264c2bf33c1573076bfa98441316f2 Description: Octave bindings for BioSig4C++ library This package provides Octave bindings for biosig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDlibbiosig4c++. Package: odin Version: 1.8.1-3~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 4136 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libqtcore4 (>= 4:4.5.3), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.4.0), libvtk5.2, mitools (= 1.8.1-3~lucid.nd1), libodin-dev, libgsl0-dev, libc6-dev | libc-dev, g++, libblas-dev | libatlas-base-dev, xterm | x-terminal-emulator, gdb Recommends: liboil0.3-dev | liboil-dev, libdcmtk1-dev Homepage: http://od1n.sourceforge.net Priority: extra Section: science Filename: pool/main/o/odin/odin_1.8.1-3~lucid.nd1_amd64.deb Size: 1573324 SHA256: 657bd81a759b23fe5890886d4752f8c7d446a4abccd8e8f3afd599988ed28099 SHA1: 4e538aeab5bbb3d132027c534ccf057ec2fd4650 MD5sum: 674745b2fece68298da5f034ff8fc191 Description: develop, simulate and run magnetic resonance sequences ODIN is a framework for magnetic resonance imaging (MRI). It covers the whole toolchain of MRI, from low-level data acquisition to image reconstruction. In particular, it aims at rapid prototyping of MRI sequences. The sequences can be programmed using a high-level, object oriented, C++ programming interface. It provides advanced sequence analysis tools, such as interactive plotting of k-space trajectories, a user interface for a fast compile-link-test cycle and a powerful MRI simulator which supports different virtual samples. For fast and flexible image reconstruction, ODIN contains a highly customizable, multi-threaded data-processing framework. Package: openmeeg-tools Source: openmeeg Version: 2.0.0.dfsg-2~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 600 Depends: libatlas3gf-base | libatlas.so.3gf, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libmatio0, libopenmeeg1, libstdc++6 (>= 4.4.0) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/openmeeg-tools_2.0.0.dfsg-2~lucid.nd1_amd64.deb Size: 162662 SHA256: fce9ef3c245aceea7d087c0bd02346b105ecfea7dfc9501076d3d46841f38916 SHA1: cbf5ec9b4f3dac5718b946b6631eb5980a49da6d MD5sum: f13cd918f20ef00389618b13b43585f1 Description: tools for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides command line tools. Package: psychopy Version: 1.62.01.dfsg-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3124 Depends: python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, python-scipy, libavbin0 Suggests: python-iolabs, python-pyepl Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.62.01.dfsg-1~lucid.nd1_all.deb Size: 1393818 SHA256: 452f85fc6774668d2a4503fbc26f80eec3c67f6c1e93f0ff528cd7d0716b5f85 SHA1: 0e75725b316e8f61430e1afb998b6087da802ae4 MD5sum: 2beb6aba547d9a27d4cbb3198e809ae6 Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features - High-level powerful scripting language (Python) - Simple syntax - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.6 Package: python-biosig Source: biosig4c++ Version: 0.94.1+svn2521-1~pre0~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1016 Depends: libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: python Filename: pool/main/b/biosig4c++/python-biosig_0.94.1+svn2521-1~pre0~lucid.nd1_amd64.deb Size: 332982 SHA256: cae9f3b5812eed891ff0ce6d79bbe3f9fea76cd5baf5d7013c6beea0947df905 SHA1: 88ca658d7041be526bc6356e4eb5dc2e6fd7ad46 MD5sum: 1a23c97c2165a4043fa0b058ddd9dfca Description: Python bindings for BioSig4C++ library This package provides Python bindings for biosig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDlibbiosig4c++. Package: python-dicom Source: pydicom Version: 0.9.5~rc1-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1864 Depends: python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://code.google.com/p/pydicom/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.5~rc1-1~lucid.nd1_all.deb Size: 372938 SHA256: f69403f36f3840a1b00833c53b9d5423ad12355073192a8245781a34954569bd SHA1: bda525b6678d316b2c50a3f9d3de8517ab999de9 MD5sum: 0c83a634b771ca0cc6de5bf844aabec6 Description: DICOM medical file reading and writing pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. Package: python-mdp Source: mdp Version: 2.6-1~lucid.nd1 Architecture: all Maintainer: Yaroslav Halchenko Installed-Size: 1556 Depends: python (>= 2.4), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-shogun-modular, python-libsvm Suggests: python-pp Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_2.6-1~lucid.nd1_all.deb Size: 294294 SHA256: 79ead30dd2633c33affc627e57c6951dea120558d51c393f4dfc447eafe37469 SHA1: bfa9650905775cde71db6c7d1d07065ed33f61d3 MD5sum: a8e651ed24debc06eff296ee58561f2b Description: Modular toolkit for Data Processing Python data processing framework. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. Package: python-mlpy Source: mlpy Version: 2.2.0~dfsg1-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 424 Depends: python (>= 2.4), python-support (>= 0.90.0), python2.6, python-numpy, python-mlpy-lib (>= 2.2.0~dfsg1-1~lucid.nd1) Suggests: python-mvpa Provides: python2.6-mlpy Homepage: https://mlpy.fbk.eu/ Priority: optional Section: python Filename: pool/main/m/mlpy/python-mlpy_2.2.0~dfsg1-1~lucid.nd1_all.deb Size: 55840 SHA256: baccc7f46670565245f2d9aa65acaea015872d8bb7bf398642a714a9fea52a2f SHA1: 38f57e733f49c401922c25d85a89b5044e68181b MD5sum: cd3699a47eb5ad404aa7864cf95d69ea Description: high-performance Python package for predictive modeling mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping. . mlpy includes: SVM (Support Vector Machine), KNN (K Nearest Neighbor), FDA, SRDA, PDA, DLDA (Fisher, Spectral Regression, Penalized, Diagonal Linear Discriminant Analysis) for classification and feature weighting, I-RELIEF, DWT and FSSun for feature weighting, *RFE (Recursive Feature Elimination) and RFS (Recursive Forward Selection) for feature ranking, DWT, UWT, CWT (Discrete, Undecimated, Continuous Wavelet Transform), KNN imputing, DTW (Dynamic Time Warping), Hierarchical Clustering, k-medoids, Resampling Methods, Metric Functions, Canberra indicators. Python-Version: 2.6 Package: python-mlpy-doc Source: mlpy Version: 2.2.0~dfsg1-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1132 Depends: libjs-jquery Suggests: python-mlpy Homepage: https://mlpy.fbk.eu/ Priority: optional Section: doc Filename: pool/main/m/mlpy/python-mlpy-doc_2.2.0~dfsg1-1~lucid.nd1_all.deb Size: 478768 SHA256: 57a934ed3182a9624128bea20def7710fd79b49a10356d8a04a243aeb6ca6a7c SHA1: d9e6be19ce66c9a83358c704eed4daa666588a1d MD5sum: 24528b12939bcd8156611770fc0162e7 Description: documention and examples for mlpy mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping. . This package provides user documentation for mlpy in various formats (HTML, PDF). Package: python-mlpy-lib Source: mlpy Version: 2.2.0~dfsg1-1~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 312 Depends: libc6 (>= 2.3.4), libgsl0ldbl (>= 1.9), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-mlpy-lib Homepage: https://mlpy.fbk.eu/ Priority: optional Section: python Filename: pool/main/m/mlpy/python-mlpy-lib_2.2.0~dfsg1-1~lucid.nd1_amd64.deb Size: 70842 SHA256: 973a999bedf71bf13119caaef348bdf5156967d0a792c02d8171d6f691fa4397 SHA1: 551d017b25a89cc23feafca4e43a941a513ad880 MD5sum: f2cbf5b76110123e0b15aaaac0b94e1d Description: low-level implementations and bindings for mlpy This is an add-on package for the mlpy providing compiled core functionality. Python-Version: 2.6 Package: python-networkx Version: 1.1-2~lucid.nd1 Architecture: all Maintainer: Debian Python Modules Team Installed-Size: 2628 Depends: python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-scipy, python-pygraphviz | python-pydot, python-pkg-resources, python-matplotlib, python-yaml Homepage: http://networkx.lanl.gov/ Priority: optional Section: python Filename: pool/main/p/python-networkx/python-networkx_1.1-2~lucid.nd1_all.deb Size: 679700 SHA256: 3b794d495a6f1402468c60983b75a41ace947be4219d2708c30469d700ae4f86 SHA1: e3e60ed53545f966ce1b2bef6b05b7cbb35d65b7 MD5sum: b12742ab70af33463e848ecf7cf0d6b4 Description: tool to create, manipulate and study complex networks NetworkX is a Python-based package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. . The structure of a graph or network is encoded in the edges (connections, links, ties, arcs, bonds) between nodes (vertices, sites, actors). If unqualified, by graph we mean a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network we usually mean a graph with weights (fields, properties) on nodes and/or edges. . The potential audience for NetworkX includes: mathematicians, physicists, biologists, computer scientists, social scientists. Package: python-nifti Source: pynifti Version: 0.20100607.1-2~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1184 Depends: libc6 (>= 2.4), libnifti2, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python2.6, python-numpy, libjs-jquery Provides: python2.6-nifti Homepage: http://niftilib.sourceforge.net/pynifti/ Priority: optional Section: python Filename: pool/main/p/pynifti/python-nifti_0.20100607.1-2~lucid.nd1_amd64.deb Size: 285356 SHA256: 9c412d06a6ba605eb308f2296e8af953e5bd50828b27e2c273ea17b338490624 SHA1: acf880288dca88acbc4b241ff17759c6b2d91918 MD5sum: a2a78580df1e53a2b19f6043dba7385c Description: Python interface to the NIfTI I/O libraries Using PyNIfTI one can easily read and write NIfTI and ANALYZE images from within Python. The NiftiImage class provides Python-style access to the full header information. Image data is made available via NumPy arrays. Python-Version: 2.6 Package: python-nipype Source: nipype Version: 0.3.3-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1752 Depends: python (>= 2.5), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits Recommends: python-nifti, ipython, python-nose, python-networkx Suggests: fsl, afni, lipsia, python-nipy Provides: python2.6-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.3.3-1~lucid.nd1_all.deb Size: 277548 SHA256: e4a925dc3e2675271763c1b8a4248d73a2d38b238961a111ed182bd3c5d84020 SHA1: fec3ab250c45a1d74b28bb0a37cd7908d2224faa MD5sum: 0a4bee8815bdcc05d9c60d7739b81b9e Description: Neuroimaging data analysis pipelines in Python Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). Package: python-nipype-doc Source: nipype Version: 0.3.3-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3640 Depends: libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.3.3-1~lucid.nd1_all.deb Size: 840644 SHA256: 465c043ca61af61479dbdd011fd350442d20a2a8b08212bf7c8adc709756dad3 SHA1: eba0a4d1ca01aab7eb6d0aed1161df8d86c6ed2d MD5sum: a283b2cea992bd74f4c8017b7b23b62d Description: Neuroimaging data analysis pipelines in Python -- documentation Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). . This package contains Nipype examples and documentation in various formats. Package: python-openmeeg Source: openmeeg Version: 2.0.0.dfsg-2~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 600 Depends: libatlas3gf-base | libatlas.so.3gf, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libmatio0, libopenmeeg1, libpython2.6 (>= 2.6), libstdc++6 (>= 4.4.0), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-openmeeg Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: python Filename: pool/main/o/openmeeg/python-openmeeg_2.0.0.dfsg-2~lucid.nd1_amd64.deb Size: 157588 SHA256: 5d21094ca71038b88adacdff2df3805515f25627746e7e84d841de003ed14ef5 SHA1: 209bc128ce2ca80e74506bff570acb4d3e9cd5cc MD5sum: 03abf5e8ea6fe312c37d33b3a2b1fe55 Description: Python bindings for openmeeg library OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides Python bindings for OpenMEEG library. Python-Version: 2.6 Package: python-openpyxl Source: openpyxl Version: 1.1.0-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 448 Depends: python-support (>= 0.90.0) Recommends: python-nose Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.1.0-1~lucid.nd1_all.deb Size: 49172 SHA256: 9fb810f9f2c088e4eeb1b5a017928da93c3d27c46b4426aa9d21a5f8f7cd13d6 SHA1: 02381c55c25c230f8b8edd8f5160dc727d6688d8 MD5sum: b834dd72b36f8c3918c17734ffda076f Description: module to read/write OpenXML xlsx/xlsm files Openpyxl is a pure Python module to read/write Excel 2007 (OpenXML) xlsx/xlsm files. Package: python-pyepl Source: pyepl Version: 1.1.0-3~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1696 Depends: python (<< 2.7), python (>= 2.6), python-central (>= 0.6.11), python-pyepl-common (= 1.1.0-3~lucid.nd1), python-numpy, python-imaging, python-pygame, python-pyode, python-opengl, ttf-dejavu, libasound2 (>> 1.0.22), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libode1, libsamplerate0, libsndfile1 (>= 1.0.20), libstdc++6 (>= 4.4.0) Conflicts: python2.3-pyepl, python2.4-pyepl Replaces: python2.3-pyepl, python2.4-pyepl Provides: python2.6-pyepl Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl_1.1.0-3~lucid.nd1_amd64.deb Size: 381458 SHA256: 4addcaafc82fe9d2405811a06fde30266fdf9fbf4c785353b56cbb6d7d1d59dc SHA1: e97c7a1c5e1e4ab1dccbe6ade4a221c85742fc9f MD5sum: ce299feeb3dec63353cb835e3e526e97 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides PyEPL for supported versions of Python. Python-Version: 2.6 Package: python-pyepl-common Source: pyepl Version: 1.1.0-3~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 852 Depends: python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0-3~lucid.nd1_all.deb Size: 817818 SHA256: 774bf05fe607359b31db378294947bcb52fcc9389f4401c967a172845766d9b3 SHA1: b50b0ce486acdaa088a42413d55ea9b1fbe99cb2 MD5sum: 4f523a841595f99e74aad775107bfbc7 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides common files such as images. Package: python-pyoptical Source: pyoptical Version: 0.2-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 72 Depends: python-serial Enhances: psychopy, python-pyepl Homepage: http://github.com/esc/pyoptical Priority: extra Section: python Filename: pool/main/p/pyoptical/python-pyoptical_0.2-1~lucid.nd1_all.deb Size: 6938 SHA256: 19de2c227b5d42bf669a9f17fe2b57a673389f897f5cfc38c36c5561f4a8d688 SHA1: 3a8e1f8a435a5f8c8c73e694284096d4df474f02 MD5sum: 72a4517f5c3bc2eb5154b247fc4e12c2 Description: python interface to the CRS 'OptiCAL' photometer The 'OptiCAL' is a photometer that is produced by Cambridge Research Systems (CRS). This device is a standard tool for gamma-calibration of display devices in vision research. This package provides a free-software replacement for the Windows-software distributed by the manufacturer that allows querying an OptiCAL via a serial connection. pyoptical can be used as a library for third-party applications or as a standalone command line tool. Python-Version: 2.6 Package: python-scikits-learn Source: scikit-learn Version: 0.4-2~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 504 Depends: python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy, python-scikits-learn-lib (>= 0.4-2~lucid.nd1) Recommends: python-nose, python-psyco, python-matplotlib Suggests: python-dap, python-scikits-optimization, python-scikits-learn-doc Provides: python2.6-scikits-learn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-scikits-learn_0.4-2~lucid.nd1_all.deb Size: 123816 SHA256: bbd50a52504b0ef101fbe465cffd1a4ec4acd8d5a1da060248fd8f8cd078a884 SHA1: 4c2c1908774044347cd8b2c6899bd2e12edfc0c9 MD5sum: e9b73291f9e82e74c578ebe4de743a36 Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Python-Version: 2.6 Package: python-scikits-learn-doc Source: scikit-learn Version: 0.4-2~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1132 Depends: libjs-jquery Suggests: python-scikits-learn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-scikits-learn-doc_0.4-2~lucid.nd1_all.deb Size: 107938 SHA256: f52a2fc0a6f41300d6f3905f72ceab59eaeea0a8d08509a2717404824ee5d6e9 SHA1: db4e98729c79ea2c31928354af910f2a4245d801 MD5sum: b35a7372103370e0db80d5dfbbd42635 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-scikits-learn. Package: python-scikits-learn-lib Source: scikit-learn Version: 0.4-2~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 556 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), libsvm2, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-scikits-learn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-scikits-learn-lib_0.4-2~lucid.nd1_amd64.deb Size: 189162 SHA256: 1531a2b8994ecd6bca84f53caa71b1c558a18b06435252e6b1ea8f0315ebdb89 SHA1: 0b18299f07025e00d0dffc7233dcaa8526072c00 MD5sum: b50b04d14f93a320750f1de26c83e4b3 Description: low-level implementations and bindings for scikits-learn This is an add-on package for python-scikits-learn. It provides low-level implementations and custom Python bindings for the LIBSVM library. Python-Version: 2.6 Package: python-scikits-statsmodels Source: statsmodels Version: 0.2.0+bzr1990-1~lucid.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 9624 Depends: python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-rpy Provides: python2.6-scikits-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: optional Section: python Filename: pool/main/s/statsmodels/python-scikits-statsmodels_0.2.0+bzr1990-1~lucid.nd1_all.deb Size: 1874514 SHA256: d2c2c1010f11027fbdece0919ff27d9b733377f5426cd44b1518aa2a9ec43e58 SHA1: cb3452ecc1bb014bdf5b694178f935c375621d5b MD5sum: 45560609f527ffd385fca31ae084523c Description: classes and functions for the estimation of statistical models scikits.statsmodels is a pure Python package that provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are avalable for each estimation problem. Python-Version: 2.6 Package: python-scikits-statsmodels-doc Source: statsmodels Version: 0.2.0+bzr1990-1~lucid.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 2184 Depends: libjs-jquery Suggests: python-scikits-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: optional Section: doc Filename: pool/main/s/statsmodels/python-scikits-statsmodels-doc_0.2.0+bzr1990-1~lucid.nd1_all.deb Size: 307516 SHA256: a21a5c3f213685a246aec2728f360ae20eaf22942a867581bf339b744aa254c5 SHA1: ee3ea925e350a73677a9a588a48bf4891c989ee5 MD5sum: 6bb2b036b77d4fd62c57f4211845349e Description: documentation and examples for python-scikits-statsmodels This package contains HTML documentation and example scripts for python-scikits-statsmodels. Package: sigviewer Version: 0.3.0+svn362-1~pre1~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1488 Depends: libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libfftw3-3, libgcc1 (>= 1:4.1.1), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.4.0), zlib1g (>= 1:1.1.4) Homepage: http://sigviewer.sourceforge.net Priority: extra Section: science Filename: pool/main/s/sigviewer/sigviewer_0.3.0+svn362-1~pre1~lucid.nd1_amd64.deb Size: 609326 SHA256: e9569bd8481a832fe01ba45ca4a9b417e6c935b822284ccc460e96a3022dabfd SHA1: f0c0ff042d2d86b11ea4f841a908bdcf3896a6c5 MD5sum: ed4683903b7e1d1091d2baf77ec7611d Description: GUI viewer for biosignals such as EEG, EMG, and ECG SigViewer is a viewing and scoring software for biomedical signal data. It relies on biosig4c++ library which supports a number of data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://hci.tugraz.at/schloegl/biosig/TESTED . . Besides displaying biosignals, SigViewer supports creating annotations to select artifacts or specific events. Package: voxbo Version: 1.8.5~svn1172-1~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 9760 Depends: libc6 (>= 2.7), libfontconfig1 (>= 2.8.0), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libpng12-0 (>= 1.2.13-4), libqt4-network (>= 4:4.5.3), libqt4-qt3support (>= 4:4.5.3), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.6.1), libstdc++6 (>= 4.4.0), libx11-6, libxext6, libxi6, libxrender1, zlib1g (>= 1:1.1.4) Suggests: mni-colin27-nifti Homepage: http://www.voxbo.org Priority: extra Section: science Filename: pool/main/v/voxbo/voxbo_1.8.5~svn1172-1~lucid.nd1_amd64.deb Size: 3581306 SHA256: d5e3dfc07edbc015530f4ad1801fdcb462a8bcf26c6bab8f503d79977294fbff SHA1: eb561d22c3b6a485f2689157e6dbb4853347c7f7 MD5sum: 071b8dd0ccbb07e2a63b217f3600fb6d Description: processing, statistical analysis, and display of brain imaging data This is a toolkit for analysis of functional neuroimaging (chiefly fMRI) experiments and voxel-based lesion-behavior mapping. VoxBo supports the modified GLM (for autocorrelated data), as well as the standard GLM for non-autocorrelated data. The toolkit is designed to be interoperable with AFNI, FSL, SPM and others.