docs

Author: LegrandNico

README.md

@@ -4,7 +4,7 @@
 
 <img src="https://github.com/embodied-computation-group/metadpy/raw/master/docs/source/images/logo.png" align="left" alt="metadpy" height="250" HSPACE=30>
 
-**metadpy** is a Python implementation of standard Bayesian models of behavioural metacognition. It is aimed to provide simple yet powerful functions to compute standard indexes and metrics of signal detection theory (SDT) and metacognitive efficiency (meta-d’ and hierarchical meta-d’) [**1**, **2**, **3**]. The only input required is a data frame encoding task performances and confidence ratings at the trial level.
+**metadpy** is a Python implementation of standard Bayesian models of behavioural metacognition. It is aimed to provide simple yet powerful functions to compute standard indexes and metrics of signal detection theory (SDT) and metacognitive efficiency (meta-d’ and hierarchical meta-d’). The only input required is a data frame encoding task performances and confidence ratings at the trial level.
 
 **metadpy** is written in Python 3. It uses [Numpy](https://numpy.org/), [Scipy](https://www.scipy.org/) and [Pandas](https://pandas.pydata.org/>) for most of its operation, comprizing meta-d’ estimation using maximum likelihood estimation (MLE). The (Hierarchical) Bayesian modelling is implemented in [Aesara](https://github.com/aesara-devs/aesara) (now renamed [PyTensor](https://github.com/pymc-devs/pytensor) for versions of [pymc](https://docs.pymc.io/>) >=5.0).
 

docs/source/examples/1-What metacognition looks like.ipynb

@@ -123,3 +123,7 @@ You can see in this figure that a higher task performance (d') results in a bigg
 ```{admonition} Exercise 2
 Change any of the parameters above and re-run this section to get an idea as to how each parameter may change the observed confidence scores.
 ```
+
+```{code-cell} ipython3
+
+```

docs/source/examples/1-What metacognition looks like.md

@@ -275,3 +275,7 @@ Here we can see that there is some variance in the model fits for each value of
 ```{Exercise 5}
 Drop the trial numbers (Ntrials) per simulation down to just 100, and re-run the simulation and recovery procedure you just performed. What happens to the recovered values of meta-d with lower numbers of trials per subject? Do you think this may or may not be a problem? Feel free to try other trial numbers if you are interested.
 ```
+
+```{code-cell} ipython3
+
+```

docs/source/examples/2-Fitting the model-MLE.ipynb

@@ -15,7 +15,7 @@ kernelspec:
 +++ {"id": "copyrighted-border"}
 
 (example_1)=
-Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>  
+Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>
 
 ```{code-cell} ipython3
 :id: unavailable-groove
@@ -211,7 +211,7 @@ sns.despine()
 
 ```{code-cell} ipython3
 %load_ext watermark
-%watermark -n -u -v -iv -w -p metadpy,jax,numpyro,pymc
+%watermark -n -u -v -iv -w -p metadpy,pytensor,pymc
 ```
 
 ```{code-cell} ipython3

docs/source/examples/2-Fitting the model-MLE.md

@@ -32,7 +32,6 @@ from metadpy.bayesian import hmetad
 +++ {"id": "operating-aerospace"}
 
 ## From response-signal arrays
-### Using pymc
 
 ```{code-cell} ipython3
 :id: worldwide-utility
@@ -83,7 +82,7 @@ az.summary(traces, var_names=["c1", "d1", "meta_d", "cS1", "cS2"])
 
 ```{code-cell} ipython3
 %load_ext watermark
-%watermark -n -u -v -iv -w -p metadpy,pymc
+%watermark -n -u -v -iv -w -p metadpy,pytensor,pymc
 ```
 
 ```{code-cell} ipython3