DOC Move some things around in related projects
@@ -43,9 +43,6 @@ enhance the functionality of scikit-learn's estimators.
**Experimentation frameworks**
-- `PyMC <http://pymc-devs.github.io/pymc/>`_ Bayesian statistical models and
- fitting algorithms.
-
- `REP <https://github.com/yandex/REP>`_ Environment for conducting data-driven
research in a consistent and reproducible way
@@ -222,18 +219,19 @@ Other packages useful for data analysis and machine learning.
statistical models. More focused on statistical tests and less on prediction
than scikit-learn.
+- `PyMC <http://pymc-devs.github.io/pymc/>`_ Bayesian statistical models and
+ fitting algorithms.
+
- `Sacred <https://github.com/IDSIA/Sacred>`_ Tool to help you configure,
organize, log and reproduce experiments
-- `gensim <https://radimrehurek.com/gensim/>`_ A library for topic modelling,
- document indexing and similarity retrieval
-
- `Seaborn <http://stanford.edu/~mwaskom/software/seaborn/>`_ Visualization library based on
matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
- `Deep Learning <http://deeplearning.net/software_links/>`_ A curated list of deep learning
software libraries.
+
Domain specific packages
~~~~~~~~~~~~~~~~~~~~~~~~
@@ -243,6 +241,9 @@ Domain specific packages
- `Natural language toolkit (nltk) <http://www.nltk.org/>`_ Natural language
processing and some machine learning.
+- `gensim <https://radimrehurek.com/gensim/>`_ A library for topic modelling,
+ document indexing and similarity retrieval
+
- `NiLearn <https://nilearn.github.io/>`_ Machine learning for neuro-imaging.
- `AstroML <http://www.astroml.org/>`_ Machine learning for astronomy.
