updating utils.py, toml file and the docs

docs/faq.rst

@@ -131,7 +131,7 @@ and signal leakage from in-slice and multi-slice accelleration may violate this
 We have one option that is generally useful and is also a partial solution.
 ``--ica_method robustica`` will run `robustica`_.
 This is a method that, for a given number of PCA components,
-will repeated run ICA and identify components that are stable across iterations.
+will repeatedly run ICA and identify components that are stable across iterations.
 While running ICA multiple times will slow processing, as a general benefit,
 this means that the ICA results are less sensitive to the initialization parameters,
 computer hardware, and software versions.

pyproject.toml

@@ -30,8 +30,8 @@ dependencies = [
     "pandas>=2.0,<=2.2.2",
     "pybtex",
     "pybtex-apa-style",
-    "robustica>=0.1.4",
-    "scikit-learn>=0.21, <=1.5.1",
+    "robustica>=0.1.4,<=0.1.4",
+    "scikit-learn>=0.21, <=1.5.2",
     "scipy>=1.2.0, <=1.14.1",
     "threadpoolctl",
     "tqdm",

tedana/utils.py

@@ -18,12 +18,14 @@
 from nilearn._utils import check_niimg
 from numpy import __version__ as numpy_version
 from pandas import __version__ as pandas_version
+from robustica import __version__ as robustica_version
 from scipy import __version__ as scipy_version
 from scipy import ndimage
 from scipy.special import lpmv
 from sklearn import __version__ as sklearn_version
 from sklearn.utils import check_array
 from threadpoolctl import __version__ as threadpoolctl_version
+from tqdm import __version__ as tqdm_version
 
 LGR = logging.getLogger("GENERAL")
 RepLGR = logging.getLogger("REPORT")
@@ -615,9 +617,11 @@ def get_system_version_info():
         "nilearn": nilearn_version,
         "numpy": numpy_version,
         "pandas": pandas_version,
+        "robustica": robustica_version
         "scikit-learn": sklearn_version,
         "scipy": scipy_version,
         "threadpoolctl": threadpoolctl_version,
+        "tqdm": tqdm_version,
     }
 
     return {