Merge pull request #225 from jakobrunge/developer

further fixes for multiple dataset functionality

setup.py

@@ -63,7 +63,7 @@ def run(self):
 # Run the setup
 setup(
     name="tigramite",
-    version="5.1.0.0",
+    version="5.1.0.1",
     packages=["tigramite", "tigramite.independence_tests", "tigramite.toymodels"],
     license="GNU General Public License v3.0",
     description="Tigramite causal discovery for time series",

tutorials/tigramite_tutorial_multiple_datasets.ipynb

@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# PCMCI and PCMCI$^+$ on multiple datasets of time series\n",
+    "# Tigramite methods on multiple datasets of time series\n",
     "\n",
-    "This notebook explains the multiple datasets functionality for [TIGRAMITE](https://github.com/jakobrunge/tigramite), which allows to run PCMCI and PCMCI$^+$ on multiple datasets of time series. We refer to this as Multidata-PCMCI in short. Familiarity with the basic usage of PCMCI or PCMCI$^+$ is assumed."
+    "This notebook explains the multiple datasets functionality for [TIGRAMITE](https://github.com/jakobrunge/tigramite), which allows to run causal discovery methods such as PCMCI and PCMCI$^+$ or also the CausalEffect class tools on multiple datasets of time series. Here we focus on the PCMCI/PCMCI$^+$ functionality and refer to this as Multidata-PCMCI in short. Familiarity with the basic usage of PCMCI or PCMCI$^+$ is assumed."
    ]
   },
   {
@@ -433,6 +433,17 @@
     "For more information on missing values and masking please refer to the [respective tutorial](https://github.com/jakobrunge/tigramite/blob/master/tutorials/tigramite_tutorial_missing_masking.ipynb) in the GitHub TIGRAMITE repository."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Integration into other Tigramite methods\n",
+    "\n",
+    "As mentioned in the beginning, the highly modular setup of tigramite implies that you can use the new multiple dataset feature also in other methods of tigramite that are based on the DataFrame class, for example the CausalEffect class to estimate causal effects given causal graphs.\n",
+    "\n",
+    "For more information on missing values and masking please refer to the [respective tutorial](https://github.com/jakobrunge/tigramite/blob/master/tutorials/tigramite_tutorial_missing_masking.ipynb) in the GitHub TIGRAMITE repository."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,