diff --git a/bambi/plots/__init__.py b/bambi/plots/__init__.py
index 8a28e23ea..a472f4620 100644
--- a/bambi/plots/__init__.py
+++ b/bambi/plots/__init__.py
@@ -1,12 +1,5 @@
 from bambi.plots.effects import comparisons, predictions, slopes
-from bambi.plots.plotting import plot_comparisons, plot_predictions, plot_slopes
+from bambi.plots.plotting import plot_cap, plot_comparison, plot_slopes
 
 
-__all__ = [
-    "comparisons",
-    "slopes",
-    "predictions",
-    "plot_comparisons",
-    "plot_predictions",
-    "plot_slopes",
-]
+__all__ = ["comparisons", "slopes", "predictions", "plot_cap", "plot_comparison", "plot_slopes"]
diff --git a/bambi/plots/create_data.py b/bambi/plots/create_data.py
index 16bea877a..7a83d7b14 100644
--- a/bambi/plots/create_data.py
+++ b/bambi/plots/create_data.py
@@ -142,7 +142,7 @@ def create_differences_data(
         return _grid_level(condition_info, variable_info, user_passed, kind)
 
 
-def create_predictions_data(model: Model, covariates: dict) -> pd.DataFrame:
+def create_cap_data(model: Model, covariates: dict) -> pd.DataFrame:
     """Creates a data grid for conditional adjusted predictions using the covariates
     passed by the user.
 
diff --git a/bambi/plots/effects.py b/bambi/plots/effects.py
index 2203a3d08..f1c7b1d44 100644
--- a/bambi/plots/effects.py
+++ b/bambi/plots/effects.py
@@ -10,7 +10,7 @@
 import xarray as xr
 
 from bambi.models import Model
-from bambi.plots.create_data import create_differences_data, create_predictions_data
+from bambi.plots.create_data import create_cap_data, create_differences_data
 from bambi.plots.utils import (
     average_over,
     ConditionalInfo,
@@ -478,7 +478,7 @@ def predictions(
     if not 0 < prob < 1:
         raise ValueError(f"'prob' must be greater than 0 and smaller than 1. It is {prob}.")
 
-    cap_data = create_predictions_data(model, covariates)
+    cap_data = create_cap_data(model, covariates)
 
     if target != "mean":
         component = model.components[target]
diff --git a/bambi/plots/plotting.py b/bambi/plots/plotting.py
index 39384f207..455298231 100644
--- a/bambi/plots/plotting.py
+++ b/bambi/plots/plotting.py
@@ -81,7 +81,7 @@ def _plot_differences(
     return fig, axes
 
 
-def plot_predictions(
+def plot_cap(
     model: Model,
     idata: az.InferenceData,
     covariates: Union[str, list],
@@ -212,7 +212,7 @@ def plot_predictions(
     return fig, axes
 
 
-def plot_comparisons(
+def plot_comparison(
     model: Model,
     idata: az.InferenceData,
     contrast: Union[str, dict, list],
diff --git a/docs/examples.rst b/docs/examples.rst
index 99f91581a..67244cbec 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -26,6 +26,6 @@ Examples
   notebooks/distributional_models
   notebooks/hsgp_1d
   notebooks/hsgp_2d
-  notebooks/plot_predictions
+  notebooks/plot_cap
   notebooks/plot_comparisons
   notebooks/plot_slopes
\ No newline at end of file
diff --git a/docs/notebooks/plot_predictions.ipynb b/docs/notebooks/plot_cap.ipynb
similarity index 69%
rename from docs/notebooks/plot_predictions.ipynb
rename to docs/notebooks/plot_cap.ipynb
index a806b98c2..2a44fbb64 100644
--- a/docs/notebooks/plot_predictions.ipynb
+++ b/docs/notebooks/plot_cap.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Plot Conditional Adjusted Predictions\n",
     "\n",
-    "This notebook shows how to use, and the capabilities, of the `plot_predictions` function. The `plot_predictions` function is a part of Bambi's sub-package `plots` that features a set of tools used to interpret complex regression models that is inspired by the R package [marginaleffects](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#conditional-adjusted-predictions-plot). "
+    "This notebook shows how to use, and the capabilities, of the `plot_cap` function. The `plot_cap` function is a part of Bambi's sub-package `plots` that features a set of tools used to interpret complex regression models that is inspired by the R package [marginaleffects](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#conditional-adjusted-predictions-plot). "
    ]
   },
   {
@@ -31,7 +31,7 @@
     "2. the mean $\\mu = g^{-1}(\\eta)$\n",
     "3. the response variable $Y \\sim \\mathcal{D}(\\mu, \\theta)$ where $\\mu$ is the mean parameter and $\\theta$ is (possibly) a vector that contains all the other \"nuissance\" parameters of the distribution.\n",
     "\n",
-    "As modelers, we are usually more interested in interpreting (2) and (3). However, $\\mu$ is not always on the same scale of the response variable and can be more difficult to interpret. Rather, the response scale is a more interpretable scale. Additionally, it is often the case that modelers would like to analyze how a model parameter varies across a range of explanatory variable values. To achieve such an analysis, Bambi has taken inspiration from the R package marginaleffects, and implemented a `plot_predictions` function that plots the conditional adjusted predictions to aid in the interpretation of GLMs. Below, it is briefly discussed what are conditionally adjusted predictions, how they are computed, and ultimately how to use the `plot_predictions` function."
+    "As modelers, we are usually more interested in interpreting (2) and (3). However, $\\mu$ is not always on the same scale of the response variable and can be more difficult to interpret. Rather, the response scale is a more interpretable scale. Additionally, it is often the case that modelers would like to analyze how a model parameter varies across a range of explanatory variable values. To achieve such an analysis, Bambi has taken inspiration from the R package marginaleffects, and implemented a `plot_cap` function that plots the conditional adjusted predictions to aid in the interpretation of GLMs. Below, it is briefly discussed what are conditionally adjusted predictions, how they are computed, and ultimately how to use the `plot_cap` function."
    ]
   },
   {
@@ -51,16 +51,25 @@
    "source": [
     "### Computing Adjusted Predictions\n",
     "\n",
-    "The objective of plotting conditional adjusted predictions is to visualize how a parameter of the (conditional) response distribution varies as a function of (some) interpolated explanatory variables. This is done by holding all other explanatory variables constant at some specified value, a _reference grid_, that may or may not correspond to actual observations in the dataset used to fit the model. By default, the `plot_predictions` function uses a grid of 200 equally spaced values between the minimum and maximum values of the specified explanatory variable as the reference grid.\n",
+    "The objective of plotting conditional adjusted predictions is to visualize how a parameter of the (conditional) response distribution varies as a function of (some) interpolated explanatory variables. This is done by holding all other explanatory variables constant at some specified value, a _reference grid_, that may or may not correspond to actual observations in the dataset used to fit the model. By default, the `plot_cap` function uses a grid of 200 equally spaced values between the minimum and maximum values of the specified explanatory variable as the reference grid.\n",
     "\n",
-    "The `plot_predictions` function uses the fitted model to then compute the predicted values of the model parameter at each value of the reference grid. The `plot_predictions` function then uses these predictions to plot the model parameter as a function of (some) explanatory variable. "
+    "The `plot_cap` function uses the fitted model to then compute the predicted values of the model parameter at each value of the reference grid. The `plot_cap` function then uses these predictions to plot the model parameter as a function of (some) explanatory variable. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "import arviz as az\n",
     "import bambi as bmb\n",
@@ -68,7 +77,7 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
-    "from bambi.plots import plot_predictions\n",
+    "from bambi.plots import plot_cap\n",
     "\n",
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -81,7 +90,7 @@
    "source": [
     "## Gaussian Linear Model\n",
     "\n",
-    "For the first demonstration, we will use a Gaussian linear regression model with the `mtcars` dataset to better understand the `plot_predictions` function and its arguments. The `mtcars` dataset was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973--74 models). The following is a brief description of the variables in the dataset:\n",
+    "For the first demonstration, we will use a Gaussian linear regression model with the `mtcars` dataset to better understand the `plot_cap` function and its arguments. The `mtcars` dataset was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973--74 models). The following is a brief description of the variables in the dataset:\n",
     "\n",
     "- mpg: Miles/(US) gallon\n",
     "- cyl: Number of cylinders\n",
@@ -97,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -107,7 +116,7 @@
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [mpg_sigma, hp, wt, hp:wt, cyl, gear]\n"
+      "NUTS: [response_sigma, hp, wt, hp:wt, cyl, gear]\n"
      ]
     },
     {
@@ -189,17 +198,17 @@
    "source": [
     "Now that we have fitted the model, we can visualize how a model parameter varies as a function of (some) interpolated covariate. For this example, we will visualize how the mean response `mpg` varies as a function of the covariate `hp`. \n",
     "\n",
-    "The Bambi model, ArviZ inference data object (containing the posterior samples and the data used to fit the model), and a list or dictionary of covariates, in this example only `hp`, are passed to the `plot_predictions` function. The `plot_predictions` function then computes the conditional adjusted predictions for each `covariate` in the list or dictionary using the method described above. The `plot_predictions` function returns a `matplotlib` figure object that can be further customized.  "
+    "The Bambi model, ArviZ inference data object (containing the posterior samples and the data used to fit the model), and a list or dictionary of covariates, in this example only `hp`, are passed to the `plot_cap` function. The `plot_cap` function then computes the conditional adjusted predictions for each `covariate` in the list or dictionary using the method described above. The `plot_cap` function returns a `matplotlib` figure object that can be further customized.  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 840x360 with 1 Axes>"
       ]
@@ -210,7 +219,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-    "plot_predictions(model, idata, \"hp\", ax=ax);"
+    "plot_cap(model, idata, \"hp\", ax=ax);"
    ]
   },
   {
@@ -220,19 +229,19 @@
    "source": [
     "The plot above shows that as `hp` increases, the mean `mpg` decreases. As stated above, this insight was obtained by creating the reference grid and then using the fitted model to compute the predicted values of the model parameter, in this example `mpg`, at each value of the reference grid.\n",
     "\n",
-    "By default, `plot_predictions` uses the highest density interval (HDI) of the posterior distribution to compute the credible interval of the conditional adjusted predictions. The HDI is a Bayesian analog to the frequentist confidence interval. The HDI is the shortest interval that contains a specified probability of the posterior distribution. By default, `plot_predictions` uses the 94% HDI.\n",
+    "By default, `plot_cap` uses the highest density interval (HDI) of the posterior distribution to compute the credible interval of the conditional adjusted predictions. The HDI is a Bayesian analog to the frequentist confidence interval. The HDI is the shortest interval that contains a specified probability of the posterior distribution. By default, `plot_cap` uses the 94% HDI.\n",
     "\n",
-    "`plot_predictions` uses the posterior distribution by default to visualize some mean outcome parameter . However, the posterior predictive distribution can also be plotted by specifying `pps=True` where `pps` stands for posterior predictive samples of the response variable."
+    "`plot_cap` uses the posterior distribution by default to visualize some mean outcome parameter . However, the posterior predictive distribution can also be plotted by specifying `pps=True` where `pps` stands for posterior predictive samples of the response variable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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KQMgjo7nc6qs74sXKsAcBt3ORfioiWu7GskXsORhHxCdjc2cQnSGGbSJafhiy64zkENERcqPjhD9KWU1HIqtVFpAYy9n3iWwRRWPqYie760AR47kiDo9m8f8dSQAAmjxOrIp4sTLixcqIBy1+BaLAGW8iWjiJbBF7D8XhVRzoDnuwKuJFk1eu9bCIiBYFQ/YS4VMk+BQJKyPeGdsty0JW0yvhO5Gze+MmskXEMxqMci8ve+Y7idcGkgAAt3Pyj54H3REvVjS54TyLxS2IiM5UTjNwYDiDA8MZBNxS+R/8nkpvbyKiRsSQvcQJggC/ywm/y4nVzTMDeMmwF7DoS+TQl8ijbzxXafFVKBk4NJrBodEMAMAhCugMubGiyY2OoBvtIReifhfru4moqtIFHW8OpvDmYAoRn4xVES+6w57TLh1vWRY03SzfDGglE0XDhOwQ4XfZkxBnswomEdFCY8huYE6HiNXN3kr4Ni0L8YyGY4kc+hN5HEvkMJEvAQAM00L/eB794/nK4yVRQGvAhfagyy5hCbrQFnRDlviHjIjmL5EtIpEt4tX+CbQGFET9LpQMcypMlwwUDbMSqE+3yI5HdsCnSHbodkkIuJwM4ERUMwzZy4go2KG5NeDCB1ZHAADpQgl94/nKbPdIWoVRbm+imxaOJws4niwAfRMA7M4mzT4F7SEXOoJ27Xg0oMCvSBBY401E58CygJGUhpGUNq/nyRcN5IsGYpnZz+OWRfgVO3R7J4O4Yn/tcp56Fn2hWJbF35tEDYwhe5kLuJ3Y0hnEls4gAHtGO5ZRMZxUMZQqYChpdzfRdLvMxAIQz2qIZzW8OZiqPI/LKSLqdyHqVxAN2PetARcCLoZvIqq9QtFEoajNGcCdDqESvidnwie/9soSxCqWzemGieGUiuPJAoaSBUgOEWvKnzh6ufIvUUPhO5pmcIgC2oNutAfdeB+aANhlJhO5IoZSKoaSBQyXw3dW0yuPU0vmrHITAFAk0Q7efhdagy50BF1oD7pPW39JRLRYSoaF8VwJ47nSrH2iAHgVCUG3c8Yt4Hae8TUrasnA4EQBgxN5xNIadHNa3UvJrNSotwYUrG62a9RZ3kK09DFk02mJgoBIebXJyRlvAEirJYykVMQzGmIZFbG0htGMWrm4EgA03cTARAED5ZUtJzV5nHaYL5edtAddCLqdnPUmorpiWkBG1ZFRdQxO+z0mlMN3qBy4K+HbZdd/p/IlDCbzGJwoIJEtntFr2cvUa/hd3wS6mjxY0+LlUvVESxhDNp2zgMuJgMuJ9a3+yrbJloKxjIZYWrXvMxpG0yry0xbUmVxM553hdGWbR3bYF1kG3WgPudEWdCHildlakIjqjmUBWVVHVtWBE8K37BArJXbnQjcsHB3L4ehYDl7FUbmAnS0PiZYWhmyqquktBde2+CrbLctCWtUxnCxgqLyK5VCyUOluAtgXLR2J53Aknpt6Pth142GvjEj5FvYp9r1XrtkFS0REc7EszCtgnyinGXjreBpvHU+jxa9gVcSDJq+MoNvJCQiiOldXIfvll1/Gv/3bv+HXv/41BgYGEAqFcNFFF+G+++7DhRdeOOPYV199FXfeeSf27dsHSZJwxRVX4JFHHsGaNWtqNHo6FUEQKh+nbmgPVLYXigaG0wUMJ9VKrXcso2KyZNECkCqUkCqUcHQsN+t5vYo0LXzLiPpd6Ay50eRh6QkRNZZ4RkN82oWbXsVRKVUJTStZYT03UX2oq5D93e9+F4lEArfffjs2btyIeDyORx99FDt27MAvfvELXHHFFQCAgwcP4rLLLsP27dvxox/9CKqq4r777sMll1yC119/HS0tLTX+SehMuWUH1jT7sKZ5atZbN0yMlktMJpeIn1zNcnrJCQDkNB05TZ91waXLKaI96EZnyF3p882l5ImokeQ0AznNwHBSnbHdqzjs4O2xZ7wn2xXykz+ixVVXIfuf//mfEY1GZ2y7+uqr0dPTg4cffrgSsu+77z4oioLnn38egYA9K3rhhRdi3bp1eOSRR/C3f/u3iz52qh7JIaIzZAfkExWKhh26c1o5fBeRyBUxntOQVmd2O5msaZzkdAhoC9iBuzNk1323+hXO+hBRQ5kM30MnhG+HCHhkO3B7ZAe85XufIsGjSPA4HVVtV0i03NVVyD4xYAOAz+fDxo0bMTAwAADQdR3PP/88/tt/+2+VgA0AK1euxOWXX44f//jHDNkNzC070Cm70dk0O4AXdRMjqUKl1eBQqoDRlAajvExcybBmdToRBSDiVdDit2/R8n2LT4HCWR8iaiCGOdUpZS6CYF+A7pUlhH0yWnz270POgBOdm7oK2XNJpVJ49dVXK7PYR44cQaFQwNatW2cdu3XrVvzyl7+Eqqpwudj2aLmRJRHdES+6I97KNt00EUtrldA9ubhOybCDt2lNLa6D4ZnPF3BJ5fDtqgTvFr/CBXaIqCFZ1tQseCyj4SAyAAC/S0Kzb2oyIuhmlxOiM1H3IfvWW29FLpfD1772NQBAIpEAAITD4VnHhsNhWJaFiYkJtLe3n/Q5Y7EY4vH4jG29vb1VHDXVC0kU0RGyl3+fZFoWxjIahlIqRlKFcp9vu/xk2hIRSKs60qo+o9sJYIf5Zp889UfHp6C5fJMllp4QUWOZnP2eLL9TJBERn4wWv4ImjwyvIsErO1h6R3SCug7Z9957L5566il85zvfmdVd5FQziaebZXzsscewe/fuqoyRlh5REOyl3wMuoCtU2a4bJhK5YiV0j2XtRXbiGa0y8w3YZSlDSXVWvSMAhNxONPvtwB31K1jV7EWrX+HMNxE1DO0kvwNdTrEcuCX4XHbw9ipTS9SfbIVM3TBRMiyUTBMlvfy1YZZvFkIeJxfloSWpbkP27t278eCDD+Khhx7CbbfdVtkeiUQATM1oTzc+Pg5BEBAKhU753LfccgtuuOGGGdt6e3uxc+fOeY+bli7JIaI14Jr1y9y0LKQLJcSzGsYyWvm+iHhWQ6owcxnmZKGEZKGE3li2ss2rSFjT7MWaFi/WtvgQ8coM3UTUcNSSCbVURAJzr3DpcorwyBJMy6oEaN0wMX2V+ZPpCLlwQVcTgh6WqtDSUZche/fu3di1axd27dqFe+65Z8a+tWvXwu12Y//+/bMet3//fvT09Jy2Hjsajc55kSXRXERBQMgjI+SRsS7qn7GvqJsYy2onBHD7fnL2O6fp2H88hf3HUwCAoNtZDt0+rG3xIuSRF/1nIiJabJMh/FzY19MMY22LD1tXBHkxZgOZXKyuEWv96y5kP/DAA9i1axe+/vWv4/7775+1X5IkfPKTn8Rzzz2Hv/u7v4Pfb4ee/v5+7NmzB3fcccdiD5mWMVmaXfMN2LPfiWwR741lcSSew3vxbKXHd6pQwmsDSbw2kAQAhL0y1jTbs9zdYQ+CHif7eRMRncCygN5YFscSOWxsD2BDm39J14GrJQNZTUdW1ZHV7Lr3nKYjV9TR5JGxMuJBZ8i9pH/G0xkYz+PNwRTSaglbVwSxqSNY6yFVVV2F7EcffRT33Xcfrr76anz84x/Hvn37ZuzfsWMHAHum++KLL8YnPvEJ3H333ZXFaJqbm/HVr361FkMnmkEUhMqV+B9YHYFpWYilNRyJZ/FePIv3xnKVpZcnF9z5Xd8EALuft91O0FVpKRj1uxD2yietaSQiWi50w8KbgykciWexdUUIq5u9p3/QHNSSgdG0ipGUirSqw+10wC07Km0MJ792n0X/cNO0a8t1w4JerjMv6iZymo7MtECd1XToxsnrZHJaAYMTBUiigM4mN7rDHnSE3A3zN2AoWcCbg0mM56ZKLt8YSCGVL+EDayIN83PWVcj+6U9/CgB48cUX8eKLL87ab5X7HW/YsAGvvPIK7rrrLlx//fUzllXnao9Uj0RBQFvQhbagCx/qaYZhWhhOFfBePIcjcXtmZrK8pGRYc15U5BAERHxyOXjbATwasLubNPJMBxHRXHKagd8cSeDQSBrv626yL2Y/Bd0wEc9qGEmp5RWFS6c8fpIg2PXkbqe9eI+FqYs1ddOEYVpnVV9+NnTTQl8ij75EHk6HgBVNHqyMeNAWcC3JhYNiaRVvDKYQz2hz7j+WyCOt6vjw+mZ45LqKqOdEsCaT6zL39ttvY/PmzXjrrbewadOmRXvdF/YPI5k/szc6NS7dNHF8ooDhlFrubmLfp0+yaMR0ogBE/S60B+1bW9BeSt6rLP1fUEREZ6qzyY3tXaFKba9lWUjkipVQPZbVYJg1HmSVKJKIrrAduKML2MEqVShhYDyPoNs5r4WJElkNbw6mMJya3ZVrLm5ZxCXrWtDsU87p9RbK2WZF/hUmqgOSKGJlxIuVkZkfe6ole1GIeEZFLKMhlrYvqpyY1tPbtICRtIqRtIrXBqYeG3BJaC8H7vaQG+0BF8I+mfXeRNSQjk8UMJwsYFWzF0XdRCyjoag3SKo+gaab6I1l0RvLwi2LWBXx4vz2QNUuCE3mi3h7KI3+8TymT8V6FUdlXYiIT0bYI59yRj2ZL+LNwRQGp620fCYKRRMvHRjFxavCWNPiO9cfo+YYsonqmMvpQHfYg+6wZ8b2kmFWZryHU+VbsoBc+eJKYHIxnQwOjWYq22SHiGigvHx8eTGdZr+CiFdpmBo4Ilq+TAt474QFxBpdoWjiwHAG745m0dPqw8Z5hO3xXBFvD6UwMD53KLZXBLXLVwDAIQIhj1wO3va9V5GQUUvYP5hC3wkh/WwYJrDvvXFM5Et4X3doSba+ZcgmWoKcjqmuJtu77G2WZSGj6RgpB+7htIrhpP0R6eTvuKJhYnCiMGtWQRSAiHdq2eSWaUsos1UWEVH9000LB4cz6B3NYm3UDttu+cx+f49lNbx1PDXnImunYphAIltEIlvEofI2tyxCK1WvPv3QSAbpQgkf7IlAkZbW3yOGbKIGIQgCAi4nAi4n1rdO9fMu6iZG05Mz3oVy+YmGrDZV721aQLzc7xvDM5834JLKgXt6txMFPkVakjMLRESNTDctHBrJoDeWQU/Uh43twZOG7VhGxdvH02dcK30mCsXql+gMp1T8n7dH8eH1LUuqnzZDNlGDk8sXyHSdUHJSKBp2sC7XfMfLi+iM54ozZiDsshMdR074CNblFBH1uyqhu8WvoNXvQsjjZPgmIqoxwwQOjdh122tbfNjYEah07BhNq9g/mELsJF0+6lFG1fF/3h7BB3ua0XnC2hT1iiGbaJlyy3PXe+umifFs0Z7xLofwyW4npWl9XdWSif7xPPrH8zMer0gi2gIutAZdaAvYHU9aAy6WnRAR1YBhAodHszgSz2J1sw/JfBFj2XNbebPWSoaF/3s4vmQWrmHIJqIZJFFENOCa1XPWtCykCqVy6J7qeBLPaJXVLAH7qve+8Tz6TgjfTR7njPDdFnTxgksiokVimPaKmUudZdkL1zBkE1HDEAUBTR4ZTR55Rs03AGQ1HfGMVlk9bbKl4PT2WRP5EibyJRwYmep2Ion2Ij12j2/7Qs62gAuyxMV1iIhoaWPIJqJ58ykSfIo0Y3lj07KQzJfKobtQCd+J7FSPb920pnU7sZeVFwA0+xS0h1zoCLor91xch4iIlhL+1SKiBSEKAsJeGWGvjI0dgcp2e5EIe8bbbjNor3SplWe9LUx1OnlzMFV5XNDttFe0DLgQKfdkDXtldjkhIqK6xJBNRItKlkSsaPJgRdPUBZemZWEiV8RQaip0D6UKyExbVj5VKCFVKOHgtHITwL7QMuKVEfYpiHjlcvi2Q7jftXRaPRERUWNhyCaimhMFARGfgohPwZbOqYtZMmqpsprlUErFULKA8WlLygP2hZZDKRVDc/R5DXmcWNPsxepmH9a0eNHkkRfhpyEiImLIJqI65nc54T9hcR3dMDGeL2I8W0QiV8RYubd3IlfExAkBPJkv4dX+JF7tTwKwO5ysbvZhTbMXa1q8CDF0ExHRAmHIJqIlRXLYi+BE/a5Z+3TTRDJXQiKnYTSt4ehYDscSuUq990S+hIn+Cbzab19k2eRxYk2zD6tbvFjTzNBNRETVw5BNRA1DEkU0+xU0+xWc1wZ8eH0LDNPCcKqA9+K5OUP37/sn8Pty6A66nWgN2CtXRgNKOcwrULiQDhERnSWGbCJqaA5RqFxoeWLofm8si2OJfKWf9+TFlYdHZy7YEHQ7EfUraA3YoTtavucqlkREdDIM2US0rMwVuoeSBbw3lsNQsoBYRsVYpgjDmqrungzf756wWlrAJaE1YC8b31a+jwYUOB1cTIeIaLmbd8hevXr1SXvUiqKIUCiEiy++GH/+53+O888/f74vR0RUVQ5RQFfYg67wVEtBw7SQyGmIpTXEysvHx9J2727DnArfaVVHWs3OCN8CgLBXRlvQVQngrQGFS8gTES0z8w7Zl156Kfbu3YuhoSF86EMfQmtrK0ZGRvDrX/8aHR0d6OrqwnPPPYd/+Zd/wd69e3HRRRdVY9xERAvGIQrTLq6cailomHY/79Fy8B5Nq3b4zmiVmW8LQKLc7eTtoXTlsZIooMVvtyl0SSLkyZvDvlckEbLkqHwvSyKU8tc+lwSRC+4QES0p8w7ZV111Ffbt24fe3l50dXVVtvf39+NjH/sYdu7ciSeffBKXXXYZ7r//fvzsZz+b70sSEdWEQxQqF1ZumrbdMC2MZe3QPZJWMZq2v57eUlA3Lbvn9xz9vE/HKztwXlsAG9r8WNfqgyKxFpyIqN7NO2Q/9NBD2LVr14yADQDd3d2477778MADD+Czn/0s7rjjDvzFX/zFfF+OiKjuOEShUhqyddr26UvIj5bDd7JQRFE3UTRMFHUTpnXSp63IFQ28Wm496BAFrG3xYkM5dLPtIBFRfZp3yO7t7UUwGJxzX1NTE44dOwYAWLVqFfL5/HxfjohoyZhrCfnpLMuCblozQndRN6HpJkqGfZ8v6jgSy6I3nkXJsGCYFg6PZnF4NIt/fwNoD7qwoS2A89v96Ai5WVZCRFQn5h2yV65ciSeffBJ/9Ed/NGvfE088ge7ubgBAIpFAOBye78sRETUMQRDgdAhwOkR4T3HcB9c2o2SYOBLP4sBwBodG0kirOgBUSlD2HIrBq0joanJjRZMHXU1udDa54ZHZRIqIqBbm/dv3L//yL/Fnf/ZnGBwcxA033IDW1laMjo7iRz/6Ef7zP/8Tjz/+OABgz549vOiRiOgcOR1iuUQkAMvqwFBSxYGRNA4OpzFUrvPOaToOjmRwcCRTeVzEK2NFOXivaHKjI+Rmi0EiokUw75D9pS99CZZlYdeuXfjKV75S2d7W1obvfe97+MIXvgAA+NrXvgZFUeb7ckREy54gCOgsz1R/9PxWJPNFHBzJ4OhYDseTBYznipVjJzudvDGYAgCIAtAacFWCd2fIjWhAgSQyeBMRVVNVPkf88pe/jC996Us4dOgQEokEIpEIzjvvvBn9s1tbW6vxUkREdIKQR8aONRHsWBMBYM9oH08WMDCRx+B4AYPJAnKaXV5iWlMlJr89Zi8n7xAERAMKOoJutIdc9n3QxeXkiYjmoWrFeoIgYMOGDdV6OiIiOkdeRcL6Vj/Wt/oB2BdYJgslDE4UMDiRx+BEAceThcpy8oY1rb1g/9TzRLwy2kNudARd6AjZwdvvctbiRyIiWnKqErKPHTuGb3zjG9izZ09lJvvyyy/H3XffjdWrV1fjJYiI6BwJgoAmj4wmj4wtnXY3KNOyEM9oGEoWMJxSMZQsYChVgFoyK4+bLDV563iqss2nSGjyOBHyyAh5nAi5p38twy1z9puICKhCyH799ddx+eWXI5/P44Mf/CAuvPBCjIyM4Mknn8TTTz+NV155Bdu3b6/CUImIqFpEYaq39wXlbZMz3sPJAobKwXs4pSJVKFUel9V0ZDUdAxOFOZ9XkcRK4A6Vw3jUr2BFk5uz4ES0rMw7ZP/FX/wFWlpa8Ktf/arSrg8A+vr6cOWVV+KOO+7Anj175vsyRES0wKbPeG/smFr/IKvpGE4VMJxUEcuoSOZLSBZKSOVLleXkJ2m6WV7xUpv1/EG3c0ank86QGy7WfRNRg5p3yP6v//ov/I//8T9mBGzA7p+9a9cufPGLX5zvSxARUQ35FAnron6si/pnbDctC1lVRzJfRLJQQjJfwkS+iNS0rzV9qvwkVSghVSjh7aE0AEAA0OxXKr29O0NuNPsUlpwQUUOYd8gOBoMnXfExFAohEAjM9yWIiKgOiYKAgNuJgNuJ7pMcUygaGEoVZlx0OVl+YgGIZzTEMxpe7U9WHuORHYh4ZYS9MiI+BRGvbH/vU+CVHTM6VxER1at5h+ybb74ZP/zhD3HNNdfM2veDH/wAN91003xfgoiIlii37MDaFh/Wtvgq29JqCcenhe6BifyMCy7zRQP5YmHOum9FEiuBO+KV0RFyo6fFx9lvIqo78w7Z73vf+/Dss8/i/e9/P2666Sa0tbVhZGQE/+t//S/EYjHccMMNeO655yrHX3fddfN9SSIiWsICLicC7U6c325/0mlZFsZzRQylVIznihjPaUhk7c4m0y+6BOya76GUWlnlErDLTrrCHqxr9WF91I/OJjdEznYTUY3NO2T/yZ/8CQBgYGAAv/vd7+bcb5UvjBEEAYZhzPcliYiogQiCYJeF+GavClwyTEyUWwkmckUkshrGy18n80WYll120j+eR/94Hi8diMEjO9ATtQP3ulYfu5oQUU3MO2SzcwgRES0Up0NENOBCNOCata9kmOhL5HF4NIPDoxnEMnZHk3zRwJuDKbxZXkq+PejC+lY7cHeHPVxCnogWxbxD9qWXXlqNcRAREZ0Vp0NET9SHnqgP12xpR6pQwrvlwN0bz1bqvCdXs9x7OA7ZIaLZLyPiVdDsU9Dssy+ubPbK8ChVWwSZiKg6Kz7+5Cc/wVNPPYW+vj6oqjpjnyAIeOONN6rxMkRERCcVdDtx0aowLloVhmFaGJzI4/BoFu/GMjg+UYAFoGiYGEqqGEqqsx7vdjoqoTvik+0Q7lUQ9nIlSyI6e/MO2d/85jdx1113oaWlBT09PfB6vdUYFxER0TlziAJWRrxYGfHiyo2tyGo6emNZ9CVySOSKGMtqSOVLmL6UTqFkYGBi7q4mLqeIJo+MkEdGk8dZXrTHWf6eIZyIZpt3yH7sscfw+c9/Ht///vfhcPCXDBER1R+fImF7Vwjbu0KVbSXDtC+izGoYyxaRyJXvsxrSqj7j8WrJrJSdzKUSwt1ONHlldIc9WNvig5clKETL1rzf/YlEAjfffDMDNhERLSlOh4jWgAutc1xUqelGpY3gRK6IifzkrYRkvoiSMXM5+RND+K+PJADYF132tPiwNurDqogXssSLLomWi3mH7A996EM4cOAArrjiimqMB5lMBg888ABef/11vPbaaxgbG8P999+PXbt2zTjuc5/7HP7n//yfsx5/3nnn4eDBg1UZCxERLU+K5EBHyI2OkHvWPsuykCsaSJZD92QIn1xKfjxXhG7aIXwyeP+/3jE4BAHdEQ/WtnjR0+JDZ5MHDpH9vIka1bxD9re//W1ce+216OrqwtVXXw1Zluf1fIlEAo8//ji2bduGnTt34oc//OFJj3W73Xj55ZdnbWs0ggA0eZwQBAG6YUE3TRimBd20YJgWLOv0zzGdJAqQHAIkhwinaN9LDgEOQUAso6Gom6d/EiKiZUoQBPgUCT5Fwoqm2ft1w0T/eB698SyOxLIYLF90aVgWjo7lcHQsh18diEGRRHSFPWgLuNAaUNAacCHqd3G2m6hBzDtk9/T04KMf/SiuvfZaCIIAj8czY78gCEilUmf8fCtXrsTExAQEQcDY2NgpQ7YoitixY8c5j72ehTzOM/6lqxtmJXBP3ZuwrMlALcLpECCJ9r1wipXQDNPCwHgevbFspecsERGdOckhYk2LD2tafMBGoFA0cHQsZ4fueBbx8u9WTTfRG8uiN5ad8fiwV0bUr1RKWVoDClp8CiQHwzfRUjLvkH3nnXfin/7pn7B9+3acf/75857JPlUAbGQ+l4RWv4K2oP1L1eU88xp3eya6OuNwiAJWNXuxqtmLVKGEI/EsjsZz0Di7TUR0TtyyAxs7AtjYYS8jnyqU8F45cA8lVcQzGoxpH0naS8sXcXAkU9kmCkDEqyAasFsKhjwymtx2d5OQx3lWfzOIaHHMO2Q/+eSTuOuuu/CNb3yjGuM5K4VCAW1tbYjH42hvb8fOnTvxN3/zNwiHw6d8XCwWQzwen7Gtt7d3IYd6UltXBBHyyPDV4RXoQbcT7+tuwvYVIQxM5HEknsVIirPbRETzEXQ7cUF3Ey7otmtNDNNCIqthNKNhNK2WbxoSWa3SYtC0gHhWQzw79+/g6S0GQx7njAAe8SpsMUhUA/NOdoZh4Morr6zGWM7Ktm3bsG3bNmzevBkAsHfvXnzrW9/CSy+9hN/+9rfw+Xwnfexjjz2G3bt3L9ZQT2lFk+f0B9WYOK3fbEYt4Ug8h/emraZGRETnziEKlaXjt3QGK9tLhomxrFYJ3aNpFbGM3d/bsE7d3eREYa+MzpDbvjW50RF0M3gTLbB5h+yPfexj2LdvX9W6i5ypO+64Y8b3V155JS644AJcf/31+MEPfjBr/3S33HILbrjhhhnbent7sXPnzoUYakPxu5zY3hXC1s4gjicL6EvkMZ4vIntCT1kiIpofp0NEe9CN9uDMC/pNy0JW1SvdTZL5IiYK9n0yX0IyX0LRmDkJMlmCsv/41DVSEa+MziZ3JXx3hNwsOyGqonmH7HvvvRc33ngjvF4vPv7xj89ZqnG68o1qufbaa+H1erFv375THheNRhGNRhdlTI1KFAV0hT3oCtsz8UXdRDJfxHi+iImc3cYqXSjBPMvOJ0REdGqiICDgdiLgdqI7Mnu/ZVkoFI1K8B5Nqzg+UcDxZGHGIjuJnN0H/M3BqeDd7JPRMS10c8ab6NzNO2Rv27YNAPCVr3wFX/nKV+Y8xjCM+b7MGbMsC6LIK7AXmyyJlY87JxmmVZ5psWdbxjIakoXSWbccPJsxmJYF3WCyJ6LlSxAEeBQJHkVCZ8iNTR1TJSgZtYTjyUIldB+fKCCjTQXvsWwRY9mZwbvJ40R7sBy6Qy50hNwIuJyL+jMRLUXzDtn33Xdf3XQEefbZZ5HP5xu2rd9S4xAFRHwKIj6lsm1yGeN4xr6AJ5EtnlNfbskhIOyREfbJaPYqCPumLh5VSwbSaglZVUdW05FR7VtW09kDnIiWNb/LiQ1tTmxoC1S2pQvl4D0tfGenBe+JfAkT+RLeGU5XtvkUqRK4u5rsTzXr8QJ+olqa9zvixJUYq+HnP/85crkcMhm7fdE777yDZ599FgBwzTXXIB6P4+abb8Yf//Efo6enB4IgYO/evfj2t7+NTZs24Ytf/GLVx0TVMdcyxqlCCfGMfSV9PKshXZhZ3y0KQMgjI+KTEfHKiHgVBNzSSf9x53I64HI6EPXP3qfphh24y6E7p+lT/cUtC2b5a7P8vWFO3RyiAFkS4XSIkB0iZEmsfO90CFAkEbLDAackwOkQYVnA4EQe/eN55LTF+zSHiOhsTJaenN8+LXirJQwlCxhKqhhOFTCULGAiX6rsz2o6Do9mcXh0qsd3k8dplxGWQ3dH0MXe3rSsCZa1UB/en7tVq1ahr69vzn1Hjx5FMBjEF77wBbz22msYHR2FYRhYuXIlrr32Wtxzzz0IBoNzPvZU3n77bWzevBlvvfUWNm3aNN8fgeZB0w0kskXkizqaPDKaPDLEJb708FhWQ/94Hv2JPPJFBm4iWnryRR3DKRVDyQKGUyqOJwsYy0y1GTyRQxTQHnRVgnd32FNZvZhovm7+QPeiv+bZZsW6/Gzn2LFjpz3mueeeW/iBUE0okgMdIffpD1xCmn0Kmn0K3tfdhHhGQ/94Dv3jeRSKLF8hoqXBI0tY2+LD2papFrlqycDgRAGDE3kMjOfRP1FArlxqYphWeV8Bv0Gi/BwOtAZc9qeSPgURr4xmn73ADpeTp0ZTlyGbqJG1+BW0+KcCd9+4/cfpTPqOiwIqZSuSKMIh2kszF4oGO7kQ0aJzOR3oifrQE7WDt2VZSOZL6J/IY3A8j4EJu9REL/+CypeXmD86lpv1XAGXhIhPQbPPLguM+KaCuJNlJ7QEMWQT1YggTC1AcdHKJoymNeSLOpwOEZJDgCTatd6SQ4Qk2nXejlOUzWi6AbVkQi0ZUEsGCiX7+0LRgKob0MrbOHtORAtFEAQ0eWU0eWVsWxECAOimiZGUiv7xPI5PFDCW1ZDIFWeVzqVVHWlVnxXABdiTEx0hN9qDrsq9R2aEofrG/0OJ6oAgCGgLuk5/4CkokgOK5EDQferWWqlCCb2xDN6L51Biu0MiWmCSKGJFk2fWCseFooFETsNYtohEzu42lZgjgFsAYhkNsYyG1wemHh9yO9EecqNjWvAOulnzTfWDIZtomQm6nbhwZRjbVoRwLJHDu6PZGV0DiIgWg1t2YIU8O3wDdgC3Z7w1jKa1cocTdUZrwWShhGShhAPTWgt6ZAc6gm60h1yV+2afApHBm2qAIZtomZIcInqifvRE/YhlVPSOZtE/nmdtNxHVnFt2zFhVeFJaLWG43N1kKFnAUErFeK5Y2Z8vGuiNZ9Ebn2otKDtEtAVddl/voBvtITda/QrbC9KCY8gmIkT9LkT9LryvZKA3lsWReHZWb29ZEuF2OuBy2veK0zH1veyAS3Igq+k4Es9iOKUu2MqeRLR8BVxOBNqcOG/aYjpqyZjWWtCe8Y5l1MqEQdEw7Raq4/nKYxyCgGhAqcx2twfdiHhl+F0nX4OB6GwxZBNRhcvpwObOIDZ1BBDPanAIQiVAn0mv8iavjK6wB/mijvfiObw3lkNW1U/7OCKic+VyOrC62YvVzd7KtpJhIpbWyrPddoeTkbRauQ7FsCwMp1QMp1Sgf+q5nA4BTR4ZYe+0W/n7JnY5obPEkE1EswiCgKj/3C/E9MgSNncGsbkziNG0iiOxLAYm8jDY2ISIFoHTIaKzyY3Opqk1F0zLQjwzVd89Gb6nt08tGVblIsu5BFwSQh4ZAbcTIbcTwRNuPpfE+m+qYMgmogXVGnChNeBCUTfRl8jhSDyL8RwvtCSixSUKQuX30fYue9tkX+/RjIqJXBHjk7e8fX9iB6bJNoMnfw17mfrpwTvsldEd9qA14GIAX2YYsoloUciSiHWtfqxr9WMiV8SReBZ9iTwMy4LTIcAhinCKAhzTeoJLot0n3N5m77MsoFAykC8aKBQNFEo61JLJGnAiOmvT+3qfyLIsZDV9KniXb6lCqXLTT7hS3LSAZL6E5Bwdm1xOESvDXqxq9mJVxIPOJjckkeUnjYwhm4gWXZNXxkXeMC5aFa7K81mWdULwNpDVdPQlclx8h4jOiSAI8Luc8LucWBnxztpvWRZyRcMO3PkSUmr5vjAVxNMFHUZ5BkAtmTg0msGh0QwAu/57RZMHqyJerGr2oDvsgSI5FvVnpIXFkE1ES54gCPDI0qwV4LavCKFvPI9DI5kZbb6IiOZLEAT4FAk+RUJnyD3nMaZlYSyroW8sj2OJHI4lcpV1CUqGNbXE/CG71KQ96MbqZi/ag67KhZc+hR1PliqGbCJqWKIoVLoOjGU1HB7JsBc4ES0asXwRedTvwsWr7U/uUoUSjo3ZgfvoWK5ykaVpAceTBRxPFmY8hyyJlQ4nEa+MsG/yawVBtxOOM+j8RLXBkE1Ey0KzT0Fzj4ILigbejWXQG8vO6CpARLQYgm4ntnWFsK0rBADIazr6xvOV4H08WZgxEVDUTYykVYyk1VnPJQpAyCOj2SejI2h3U+kMubm8fJ1gyCaiZcUtO7B1RQibOoLoS+RweDTDbidEVDMeRcL57QGc324vsFMyzMpFlolcEeM5zf46W0QyX6rUeAP27PfksYdHp1a5nCxh6WxyY0U5ePtdzkX/2ZY7hmwiWpYcooA1LT6safEhntFweDSDoWQBummxUwkR1YzTIVZaDZ7ItCyk8qVy+LYDeCJXRCyjYSyjYfJXV1bTZ1xkCdgz6J2hqdDdGXLDozAGLiT+1yWiZa/Fr6DFr1S+1w0TummhZJgoGRZ0s3w/7XvdsFA0TOQ0HemCjqxW4mI7RLSgxFO0HNR0A0NJFceTBQxO5HF8ooDEtAu+JzuevDOcrmxr8jixoslTmfXuDLnhcrLDSbUwZBMRnUByiJAcOKs/NpPtvDKq3bYro5aQUXWk1RJymrGAoyUiAhRp9vLyhaKBoVQBxyfs4D2YLMzo4T2RL2Ein8L+46nKtmafMmO2uyPkhiyxn/e5YMgmIqqC6e282oMz9+mGiWx5xlvVDYiCPSMlCvYCO6IoQBQAh2B/PXkvCvbz9sayODSS5kw5EZ0Vt+zA2hYf1rb4Kttyml7pYjI4UcDxifyMVSzHshrGshpeH0gCAATYaxu0+hVEAy5E/QpaAy60+BU4HQzfp8KQTUS0wCSHiJBHRsgz+yPeM7G9K4SeqA+v9yfRP56v8uiIaDnxKhLWt/qxvtVf2ZZWSxiaKGAwOTXrnSvan8BZmLq48sDIVI23ACDslRENuMoBXEHUz/A9HUM2EdES4FMk/OG6ZsQyKl7tS3JxHSKqmoDLiUC7ExvKHU4sy0KqUMLxZAFDSRWxjIrRtIbxnFZpL2gBSJQ7oBwYnnouAUBb0FUJ8t1hz7Lt5c2QTUS0hET9Lly9uQ3vxbN4YzDJZeOJqOoEQah8+rapY6r+TTdMxLMaYmkNoxkVsbSGWEZFIlusdDaxAAynVAynVOw9HIciieiJ+nBeqx/rWv0IupdPK0GGbCKiJWhNiw/dYQ/eGU7j4HAG+mmWsfQqDgTcTgTdTgRc9r3fJcEwLeSKOvKaYd8XDeS0qfuSwX6GRGSTHCLag260B2cuI18yTIxlNYymNYymVRyJZ3F8ogALgKabeHsojbeH7K4mbQEX1rf6sL7Vj5URb0PPcjNkExEtUZJDxNYVIaxt8eGNAbte2+eSKiF6KlRLkE5RI+lVJMA/976ibiJf1JErh+7xXBF9iRwvwiSiCucc4Tur6Xh3NIPDoxm8G8siX67xnly98v++OwZFErG2xYd1rT40+xS7bMUlQWmQNoIM2URES5xXkfDBnmb8gWVVfSllWRIhSzJCnqlt27tC9h/O0Sw0nWmbiGbzKRIu6G7CBd1NMC0LxycKOFQO3dNnud8ZTs/o3Q0AiiQi4HLC75YQdNkTBgGXBP+0CQTTtCDW+Sw4QzYRUYOodsA+GZfTXpp+Y3sAR8dyODCSQXZaCzAioulEQUBX2IOusAcfPb8VWU1HbyyDw6NZHB7NVGa5J2m6Xfsdz2onfc6bP9Bd9/XdDNlERHROJIeIda1+9ER9GJwo4J3hNBJZdj0holPzKRK2dzVhe5c9yz2eLSKllpAulJBW9fL91PcZtYTpl504HQICrvqPsPU/QiIiqmvCtFmqWEbFweEMjicLsHjNJBGdhigIaPYraPYrJz3GtCzkygt6pdUSNN1YtE/u5oMhm4iIqibqdyHqdyGtlnBoJIOj8dxpO58QEZ2KKAjwu5zwu5zohPv0D6gTDNlERFR1AZcTF68KY0tnEMeTBYym7I4CaokXShLR8sCQTUREC8bldGBtiw9rW3wAgGS+aLfwSqmIZTTo7MNNRA2KIZuIiBbN5CpyG9oCME0LYzkNIyk7dI/nimBlCRE1CoZsIiKqCVEUKjXcW1cApmmhUDLsW9FAvmggX9Snvi4ZKBR1LoRDREsCQzYREdUFURTgVSR7BcpTUEsG1JKBXNHARK6I4ZSKRFbjLDgR1RWGbCIiWlJcTgdcTgdCHqAz5MbmziB0w0Qso2EkrWI0pWIiX6r1MIlomWPIJiKiJU9yiOgIudERstt7qSUDsbQdukfSKlekJKJFx5BNREQNx+V0oDviQXfEAwDIanrlAsuhZIG9u4lowTFkExFRw/MpEnqiPvREfdANE8eTBfQl8hhOFXghJREtCIZsIiJaViSHiJURL1ZGvCjqJgYn8uhL5DGaVnnxJBFVDUM2EREtW7IkYk2LD2tafFBLBgbG7cAdz2qwziJwK5IIr2JfkKlIDmQ1HalCCUWd0+REyxVDNhEREew67nWtfqxr9SNf1NFfDtxqyYDb6YBHluCWHfCUb/bXEjxOB0RRmPM5C0UDqUIJqUIJyXyx8nWJK10SNby6C9mZTAYPPPAAXn/9dbz22msYGxvD/fffj127ds069tVXX8Wdd96Jffv2QZIkXHHFFXjkkUewZs2axR84ERE1DI8sYUNbABvaAvN6Hnc5jLcFXTO254v2TPfAeAFHx7KsCydqQGKtB3CiRCKBxx9/HJqmYefOnSc97uDBg7jssstQLBbxox/9CE888QQOHz6MSy65BPF4fPEGTEREdJY8soT2oBvvXx3Gp7d3YktnEIpUd3+SiWge6m4me+XKlZiYmIAgCBgbG8MPf/jDOY+77777oCgKnn/+eQQC9kzDhRdeiHXr1uGRRx7B3/7t3y7msImIiM6Jy+nAlhVBbOwI4OhYFgdHMkgX2NebaKmru382C4IAQZi7tm2Srut4/vnn8ZnPfKYSsAE7oF9++eX48Y9/vNDDJCIiqiqHKKAn6scntnbgw+ubEfUrtR4SEc1D3c1kn4kjR46gUChg69ats/Zt3boVv/zlL6GqKlwu1xyPBmKx2KySkt7e3gUZKxER0dla0eTBiiYPxnNFHBhOY2A8z/aCREvMkgzZiUQCABAOh2ftC4fDsCwLExMTaG9vn/Pxjz32GHbv3r2gYyQiIpqvsFfGh3qakdN0HBrNoDeWhc7OJERLwpIM2ZNOVVZyqn233HILbrjhhhnbent7T3mhJRERUa14FQnv627CthUhJHIa4hn7NpYtshc3UZ1akiE7EokAmJrRnm58fByCICAUCp308dFoFNFodKGGR0REtCAcooCo34Wof6ocMpUvIZ4tB++shqzKiyaJ6sGSDNlr166F2+3G/v37Z+3bv38/enp6TlqPTURE1EiCHieCHid6oj4A9gI4Y1kNsYyGsayG8VzxrFavJKLqqLvuImdCkiR88pOfxHPPPYdMJlPZ3t/fjz179uC6666r4eiIiIhqxy070BX24MKVTbhqUxs+ua0D57X5ITlO3bmLiKqrLmeyf/7znyOXy1UC9DvvvINnn30WAHDNNdfA4/Fg9+7duPjii/GJT3wCd999N1RVxX333Yfm5mZ89atfreXwiYiI6oZPkXDhyiZs6QziSDyLw6MZ5DSj1sMianh1GbL/+3//7+jr66t8/8wzz+CZZ54BABw9ehSrVq3Chg0b8Morr+Cuu+7C9ddfP2NZ9ZaWlloNnYiIqC7Jkojz2wM4r9WPgYk8Do5kkMgWaz0sooZVlyH72LFjZ3TchRdeiF/96lcLOxgiIqIGIooCVka8WBnxIpZRcWgkg8GJAuu2iaqsLkM2ERERLbzJTiUZtYTDoxkciefYh5uoShiyiYiIljm/y4kLV4axpTOEI/Esjo3lMJEv1XpYREsaQzYREREBmKrbPr89gLRaQn8ij75EHqkCAzfR2WLIJiIiolkCLic2dwaxuTOIVL6EvvEc+hJ5ZLjYDdEZYcgmIiKiUwp6nNjqCWHrihDGc0X0j+fRl8ixFSDRKTBkExER0RkLe2WEvTK2d4WQUUvIqDrSagnpgo6MWkJaLaFQNGs9TKKaY8gmIiKic+J3OeF3OdEB94ztRd0sB24d6cJUEM+oJRjM37RMMGQTERFRVcmSiIhPQcSnzNhuWRZShRLGc0VM5ItIZItI5kvQTbYNpMbDkE1ERESLQhAEhDwyQh65ss2yLKQLOhI5jcGbGgpDNhEREdWMIAgIepwIepyVbdOD97FEDiMprYYjJDo3DNlERERUV6YH7zUtPrw7msFr/UnObtOSItZ6AERERESnsq7Vjz/a0oYWv3L6g4nqBEM2ERER1T2/y4mPnh/FBd0hOJheaAng/6ZERES0JAiCgPPbA7h6czsiPvn0DyCqIYZsIiIiWlKCbic+trEVW1cEIQq1Hg3R3BiyiYiIaMkRBAGbO4O4enMbmqZ1JjkbToeAFr+C9qCLYZ2qjt1FiIiIaMkKeWRctakNbw2l8M5QGidrQOKWRTR5ZDR57GXhQx57tcpJasnAwHgeR8dyGMsWF2n01MgYsomIiGhJE0UBW1eE0Bly4z+PjsO0LDR57CAd9trB2uV0nPI5XE4H1rX6sa7Vj4xaQl8ij2OJHNIFfZF+Cmo0DNlERETUECI+BddsaZ/38/hdTmzuDGJzZxDjuSKOJXLoS+RQKJpVGCUtFwzZRERERCcR9trlJRd0hTCa1jCSVhHPaEhktZOWphABDNlEREREpyUIAtqCLrQFXQAAw7SQyGqIZTTEsxrGMhpKBlM3TWHIJiIiIjpLDlFANOBCNGCHbsuyMJEvIZ7REM9oiGVUqCWWlyxnDNlERERE8yQIQqW05Lw2PwAgrZYQS9uBO57RkNOMGo+SFhNDNhEREdECCLicCLic6In6AABZTUcsrSKW0TCaVhm6GxxDNhEREdEi8CkSfC0+rGmxQ3dO0xHLaJXgnVHZLrCRMGQTERER1YBXkbBakbC62QsAKBQNjKZVvBvLIp7Rajw6mi+GbCIiIqI64JYdWNXsxapmL0ZSKvYfTzFsL2EM2URERER1ZrJdIMP20sWQTURERFSnJsP2aFrFm4MM20sJQzYRERFRnWsNuHDlRjts7x9MIcawXfcYsomIiIiWiNaAC60M20sCQzYRERHREjMZtmPlbiSFooGiYULTDRR1EwYXm6w5hmwiIiKiJWr60u7T6YaJomGiqJvQ9Ml7A32JPEbTnP1eDAzZRERERA1GcoiQHCI88sztPVE/4hkN7wyncXyiUJvBLRMM2URERETLSItfwaX+FiTzRbwzlEbfeB6WVetRNR6GbCIiIqJlKOSR8cGeZmxRSzgwnMHRsSxruatIrPUAiIiIiKh2/C4n3r86jE9t68SGdj8kh1DrITUEzmQTEREREdyyA+/rbsKmjgAOj2RxaDSDos6p7XPFkE1EREREFYrkwJYVQWxo9+P4RAG6acK0AMO0YFoWLAswLav8PWCd8LUgCJAcAhyiAEm0750O8aTfF0oGft2bgNZggZ4hm4iIiIhmcTpErGr2LvjrhAB8bFMrXjkUR0bVF/z1FgtrsomIiIiopvwuJ67c2Ipmn3z6g5eIJRuyX3nlFQiCMOdt3759tR4eEREREZ0Fl9OBj5zfiu6wp9ZDqYolXy7y8MMP4/LLL5+xbfPmzTUaDRERERGdK4co4A/XNeO1/gkcGM7UejjzsuRD9rp167Bjx45aD4OIiIiIquSC7ib4FAm/65tYsgvlLNlyESIiIiJqXOta/fjw+hZI4tLs273kQ/att94KSZIQCARw1VVX4T/+4z9O+5hYLIa33357xq23t3cRRktEREREZ6oz5MZHN7bCLS+9yLpky0WCwSBuv/12XHbZZYhEIujt7cU3v/lNXHbZZfjZz36Gq6666qSPfeyxx7B79+5FHC0RERERnYuwV8bHNrbhlUNxpAqlWg/njAmWtVQrXWZLJpPYsmULwuEw3njjjZMeF4vFEI/HZ2zr7e3Fzp078dZbb2HTpk0LPVQiIiIiOgtF3cT/ezeO0bSGmz/Qveiv//bbb2Pz5s1nnBWX7Ez2XEKhED7xiU/ge9/7HgqFAtxu95zHRaNRRKPRRR4dEREREZ0rWRJx+XlR/OfR8VoP5YwsvQKX05icmBeEpVkkT0RERERzE0UBf7A2UuthnJGGCtkTExN4/vnnsX37drhcrloPh4iIiIiWqSVbLnLzzTeju7sbF110EZqbm/Huu+/i0UcfxejoKJ588slaD4+IiIiIlrElG7K3bt2Kp59+Gt/73veQzWYRDofxh3/4h/jXf/1XXHzxxbUeHhEREREtY0s2ZN999924++67az0MIiIiIqJZGqomm4iIiIioHjBkExERERFVGUM2EREREVGVMWQTEREREVUZQzYRERERUZUxZBMRERERVRlDNhERERFRlS3ZPtnVpmkaAKC3t7fGIyEiIiKiejOZEScz4+kwZJcNDAwAAHbu3FnbgRARERFR3RoYGMD73ve+0x4nWJZlLcJ46l4ymcTevXvR1dUFRVEW5DV6e3uxc+dO/OQnP0FPT8+CvAbND89RfeP5qX88R/WN56f+8RzVL03TMDAwgEsvvRShUOi0x3MmuywUCuHTn/70orxWT08PNm3atCivReeG56i+8fzUP56j+sbzU/94jurTmcxgT+KFj0REREREVcaQTURERERUZQzZRERERERVxpC9iFpaWnD//fejpaWl1kOhk+A5qm88P/WP56i+8fzUP56jxsHuIkREREREVcaZbCIiIiKiKmPIJiIiIiKqMoZsIiIiIqIqY8gmIiIiIqoyhuwqeuWVVyAIwpy3ffv2zTj21VdfxUc/+lH4fD6EQiFcd911eO+992o08saTyWRw55134mMf+xhaWlogCAJ27do157Fncy6+853vYMOGDVAUBatXr8bu3btRKpUW8CdpXGd6jj73uc/N+Z7asGHDnM/Lc1QdL7/8Mj7/+c9jw4YN8Hq96OzsxKc//Wn8/ve/n3Us30O1cabniO+h2nj99dfx8Y9/HN3d3XC73QiHw/iDP/gD/Nu//dusY/keakwM2Qvg4Ycfxm9+85sZt82bN1f2Hzx4EJdddhmKxSJ+9KMf4YknnsDhw4dxySWXIB6P13DkjSORSODxxx+HpmnYuXPnSY87m3Px0EMP4fbbb8d1112HX/ziF7jlllvw8MMP49Zbb13gn6Yxnek5AgC32z3rPfX000/POo7nqHq++93v4tixY7j99tvxwgsv4B/+4R8Qi8WwY8cOvPzyy5Xj+B6qnTM9RwDfQ7WQTCbR1dWFhx9+GC+88AL+5V/+BatWrcKf/Mmf4MEHH6wcx/dQA7Ooavbs2WMBsJ555plTHnfDDTdYzc3NViqVqmw7duyY5XQ6rTvvvHOhh7ksmKZpmaZpWZZlxeNxC4B1//33zzruTM/F2NiY5XK5rC9/+cszHv/QQw9ZgiBYb7/99sL8IA3sTM/RZz/7Wcvr9Z72+XiOqmt0dHTWtkwmY7W2tlof+chHKtv4HqqdMz1HfA/Vlw984ANWV1dX5Xu+hxoXZ7IXma7reP755/GZz3wGgUCgsn3lypW4/PLL8eMf/7iGo2sckx+HnsrZnIsXX3wRqqriT//0T2c8x5/+6Z/Csiz85Cc/qer4l4MzOUdng+eouqLR6KxtPp8PGzduxMDAAAC+h2rtTM7R2eA5WhzNzc2QJAkA30ONjiF7Adx6662QJAmBQABXXXUV/uM//qOy78iRIygUCti6deusx23duhW9vb1QVXUxh7tsnc25eOuttwAAW7ZsmXFce3s7mpubK/tpYRQKBbS1tcHhcGDFihW47bbbMD4+PuMYnqOFl0ql8Oqrr2LTpk0A+B6qRyeeo0l8D9WOaZrQdR3xeByPPfYYfvGLX+Cuu+4CwPdQo5NqPYBGEgwGcfvtt+Oyyy5DJBJBb28vvvnNb+Kyyy7Dz372M1x11VVIJBIAgHA4POvx4XAYlmVhYmIC7e3tiz38ZedszkUikYCiKPB6vXMeO/lcVH3btm3Dtm3bKtc17N27F9/61rfw0ksv4be//S18Ph8A8BwtgltvvRW5XA5f+9rXAPA9VI9OPEcA30O1dsstt+D73/8+AECWZfzjP/4j/uzP/gwA30ONjiG7ii644AJccMEFle8vueQSXHvttdiyZQvuvPNOXHXVVZV9p/qYvJofodPpnem54DmrjTvuuGPG91deeSUuuOACXH/99fjBD34wYz/P0cK599578dRTT+E73/kOLrzwwhn7+B6qDyc7R3wP1dY999yDL37xi4jFYvjpT3+K2267DblcDn/5l39ZOYbvocbEcpEFFgqF8IlPfAJvvvkmCoUCIpEIAMz5L87x8XEIgoBQKLTIo1yezuZcRCIRqKqKfD4/57FzzULQwrn22mvh9XpntMbkOVo4u3fvxoMPPoiHHnoIt912W2U730P142Tn6GT4Hlo83d3duOiii3DNNdfgu9/9Lr785S/jr//6rxGPx/keanAM2YvAsiwA9r8y165dC7fbjf379886bv/+/ejp6YHL5VrsIS5LZ3MuJmvgTjx2ZGQEY2NjM1o00uKwLAuiOPUrjOdoYezevRu7du3Crl27cM8998zYx/dQfTjVOToVvodq4/3vfz90Xcd7773H91CDY8heYBMTE3j++eexfft2uFwuSJKET37yk3juueeQyWQqx/X392PPnj247rrrajja5eVszsXVV18Nl8uFJ598csZzPPnkkxAE4bR9nqm6nn32WeTzeezYsaOyjeeo+h544AHs2rULX//613H//ffP2s/3UO2d7hydDN9DtbNnzx6Ioog1a9bwPdToatM5sDHddNNN1l133WU988wz1p49e6zHH3/cOu+88yxJkqxf/vKXleMOHDhg+Xw+68Mf/rD1wgsvWM8995y1efNmq6Ojw4rFYjX8CRrLCy+8YD3zzDPWE088YQGwbrjhBuuZZ56xnnnmGSuXy1mWdXbn4sEHH7QEQbDuuece65VXXrG++c1vWoqiWF/60pdq8eM1hNOdo2PHjlkf/OAHrX/8x3+0XnjhBevnP/+5dffdd1sul8vatGmTlc1mZzwfz1H1PPLIIxYA6+qrr7Z+85vfzLpN4nuods7kHPE9VDtf+tKXrK9+9avW008/bb3yyivWs88+a914440WAOuv/uqvKsfxPdS4GLKr6Bvf+Ia1fft2KxgMWg6Hw2ppabGuvfZa67/+679mHfu73/3O+shHPmJ5PB4rEAhYO3futHp7e2sw6sa1cuVKC8Cct6NHj1aOO5tz8Q//8A/W+vXrLVmWre7ubuv++++3isXiIv1Ejed052h8fNy69tprrVWrVllut9uSZdlat26ddeedd1rJZHLO5+Q5qo5LL730pOfmxPkZvodq40zOEd9DtfPEE09Yl1xyidXc3GxJkmSFQiHr0ksvtf71X/911rF8DzUmwbLKBcNERERERFQVrMkmIiIiIqoyhmwiIiIioipjyCYiIiIiqjKGbCIiIiKiKmPIJiIiIiKqMoZsIiIiIqIqY8gmIiIiIqoyhmwiIiIioipjyCYiIiIiqjKGbCKiZWjXrl0QBAFjY2O1HgoRUUNiyCYiIiIiqjKGbCIiIiKiKmPIJiJaxkZHR3HTTTchGAyitbUVn//855FKpSr7BUHAbbfdhu9///tYv349FEXBxo0b8b//9/+u4aiJiOqfVOsBEBFR7XzmM5/BjTfeiC984QvYv38//vqv/xoA8MQTT1SO+fd//3fs2bMHf/M3fwOv14vHHnsMN910EyRJwvXXX1+roRMR1TXBsiyr1oMgIqLFtWvXLuzevRt/93d/h7/6q7+qbL/11lvxxBNPIJ/PQxAECIIAt9uNo0ePorW1FQBgGAY2b94MXdfx7rvv1upHICKqaywXISJaxj71qU/N+H7r1q1QVRWxWKyy7SMf+UglYAOAw+HAjTfeiN7eXgwODi7aWImIlhKGbCKiZSwSicz4XlEUAEChUKhsa2trm/W4yW2JRGIBR0dEtHQxZBMR0SmNjIycdNuJIZ2IiGwM2UREdEovvfQSRkdHK98bhoGnn34aa9euxYoVK2o4MiKi+sXuIkREdErNzc244oorcO+991a6ixw8eJBt/IiIToEhm4iITulTn/oUNm3ahK9//evo7+/H2rVr8dRTT+HGG2+s9dCIiOoWW/gREdFJCYKAW2+9Ff/0T/9U66EQES0prMkmIiIiIqoyhmwiIiIioipjTTYREZ0UKwqJiM4NZ7KJiIiIiKqMIZuIiIiIqMoYsomIiIiIqowhm4iIiIioyhiyiYiIiIiqjCGbiIiIiKjKGLKJiIiIiKqMIZuIiIiIqMoYsomIiIiIqowhm4iIiIioyv5/YiOvG2YkU9YAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 840x360 with 1 Axes>"
       ]
@@ -243,7 +252,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-    "plot_predictions(model, idata, \"hp\", pps=True, ax=ax);"
+    "plot_cap(model, idata, \"hp\", pps=True, ax=ax);"
    ]
   },
   {
@@ -253,12 +262,12 @@
    "source": [
     "Here, we notice that the uncertainty in the conditional adjusted predictions is much larger than the uncertainty when `pps=False`. This is because the posterior predictive distribution accounts for the uncertainty in the model parameters and the uncertainty in the data. Whereas, the posterior distribution only accounts for the uncertainty in the model parameters.\n",
     "\n",
-    "`plot_predictions` allows up to three covariates to be plotted simultaneously where the first element in the list represents the main (x-axis) covariate, the second element the group (hue / color), and the third element the facet (panel). However, when plotting more than one covariate, it can be useful to pass specific `group` and `panel` arguments to aid in the interpretation of the plot. Therefore, `subplot_kwargs` allows the user to manipulate the plotting by passing a dictionary where the keys are `{\"main\": ..., \"group\": ..., \"panel\": ...}` and the values are the names of the covariates to be plotted. For example, passing two covariates `hp` and `wt` and specifying `subplot_kwargs={\"main\": \"hp\", \"group\": \"wt\", \"panel\": \"wt\"}`. "
+    "`plot_cap` allows up to three covariates to be plotted simultaneously where the first element in the list represents the main (x-axis) covariate, the second element the group (hue / color), and the third element the facet (panel). However, when plotting more than one covariate, it can be useful to pass specific `group` and `panel` arguments to aid in the interpretation of the plot. Therefore, `subplot_kwargs` allows the user to manipulate the plotting by passing a dictionary where the keys are `{\"main\": ..., \"group\": ..., \"panel\": ...}` and the values are the names of the covariates to be plotted. For example, passing two covariates `hp` and `wt` and specifying `subplot_kwargs={\"main\": \"hp\", \"group\": \"wt\", \"panel\": \"wt\"}`. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -273,7 +282,7 @@
     }
    ],
    "source": [
-    "plot_predictions(\n",
+    "plot_cap(\n",
     "    model=model, \n",
     "    idata=idata, \n",
     "    covariates=[\"hp\", \"wt\"],\n",
@@ -290,12 +299,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Furthermore, categorical covariates can also be plotted. We plot the the mean `mpg` as a function of the two categorical covariates `gear` and `cyl` below. The `plot_predictions` function automatically plots the conditional adjusted predictions for each level of the categorical covariate. Furthermore, when passing a list of covariates into the `plot_predictions` function, the list will be converted into a dictionary object where the key is taken from (\"horizontal\", \"color\", \"panel\") and the values are the names of the variables. By default, the first element of the list is specified as the \"horizontal\" covariate, the second element of the list is specified as the \"color\" covariate, and the third element of the list is mapped to different plot panels."
+    "Furthermore, categorical covariates can also be plotted. We plot the the mean `mpg` as a function of the two categorical covariates `gear` and `cyl` below. The `plot_cap` function automatically plots the conditional adjusted predictions for each level of the categorical covariate. Furthermore, when passing a list of covariates into the `plot_cap` function, the list will be converted into a dictionary object where the key is taken from (\"horizontal\", \"color\", \"panel\") and the values are the names of the variables. By default, the first element of the list is specified as the \"horizontal\" covariate, the second element of the list is specified as the \"color\" covariate, and the third element of the list is mapped to different plot panels."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -311,7 +320,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-    "plot_predictions(model, idata, [\"gear\", \"cyl\"], ax=ax);"
+    "plot_cap(model, idata, [\"gear\", \"cyl\"], ax=ax);"
    ]
   },
   {
@@ -330,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -376,7 +385,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [8000/8000 00:01&lt;00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -420,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -444,7 +453,7 @@
        "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -455,7 +464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -471,7 +480,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-    "plot_predictions(\n",
+    "plot_cap(\n",
     "    model_interaction, \n",
     "    idata_interaction, \n",
     "    \"math\", \n",
@@ -485,12 +494,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The plot above shows that as `math` increases, the mean `daysabs` decreases. However, as the model contains an interaction term, the effect of `math` on `daysabs` depends on the value of `prog`. Therefore, we will use `plot_predictions` to plot the conditional adjusted predictions for each level of `prog`."
+    "The plot above shows that as `math` increases, the mean `daysabs` decreases. However, as the model contains an interaction term, the effect of `math` on `daysabs` depends on the value of `prog`. Therefore, we will use `plot_cap` to plot the conditional adjusted predictions for each level of `prog`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -506,7 +515,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-    "plot_predictions(\n",
+    "plot_cap(\n",
     "    model_interaction, \n",
     "    idata_interaction, \n",
     "    [\"math\", \"prog\"], \n",
@@ -525,12 +534,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x500 with 3 Axes>"
       ]
@@ -540,7 +549,7 @@
     }
    ],
    "source": [
-    "plot_predictions(\n",
+    "plot_cap(\n",
     "    model_interaction, \n",
     "    idata_interaction, \n",
     "    covariates=[\"math\", \"prog\"],\n",
@@ -558,7 +567,7 @@
    "source": [
     "## Logistic Regression\n",
     "\n",
-    "To further demonstrate the `plot_predictions` function, we will implement a logistic regression model. This example is taken from the marginaleffects `plot_predictions` [documentation](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#prediction-type-or-scale). The internet movie database, http://imdb.com/, is a website devoted to collecting movie data supplied by studios and fans. It claims to be the biggest movie database on the web and is run by Amazon. The movies in this dataset were selected for inclusion if they had a known length and had been rated by at least one imdb user. The dataset below contains 28,819 rows and 24 columns. The variables of interest in the dataset are the following:\n",
+    "To further demonstrate the `plot_cap` function, we will implement a logistic regression model. This example is taken from the marginaleffects `plot_predictions` [documentation](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#prediction-type-or-scale). The internet movie database, http://imdb.com/, is a website devoted to collecting movie data supplied by studios and fans. It claims to be the biggest movie database on the web and is run by Amazon. The movies in this dataset were selected for inclusion if they had a known length and had been rated by at least one imdb user. The dataset below contains 28,819 rows and 24 columns. The variables of interest in the dataset are the following:\n",
     "- title. Title of the movie.\n",
     "- year. Year of release.\n",
     "- budget. Total budget (if known) in US dollars\n",
@@ -572,7 +581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -586,55 +595,11 @@
       "NUTS: [length, style, length:style]\n"
      ]
     },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 04:45&lt;00:00 Sampling 4 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 286 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 421 seconds.\n"
      ]
     }
    ],
@@ -663,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -684,7 +649,7 @@
        "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -698,17 +663,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Again, by default, the `plot_predictions` function plots the mean outcome on the response scale. Therefore, the plot below shows the probability of a successful response `certified_fresh` as a function of `length`."
+    "Again, by default, the `plot_cap` function plots the mean outcome on the response scale. Therefore, the plot below shows the probability of a successful response `certified_fresh` as a function of `length`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 840x360 with 1 Axes>"
       ]
@@ -719,7 +684,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-    "plot_predictions(model, idata, \"length\", ax=ax);"
+    "plot_cap(model, idata, \"length\", ax=ax);"
    ]
   },
   {
@@ -732,7 +697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -748,7 +713,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-    "plot_predictions(model, idata, \"style\", ax=ax);"
+    "plot_cap(model, idata, \"style\", ax=ax);"
    ]
   },
   {
@@ -758,12 +723,12 @@
    "source": [
     "## Plotting other model parameters\n",
     "\n",
-    "`plot_predictions` also has the argument `target` where `target` determines what parameter of the response distribution is plotted as a function of the explanatory variables. This argument is useful in distributional models, i.e., when the response distribution contains a parameter for location, scale and or shape. The default of this argument is `mean` and passing a parameter into `target` only works when the argument `pps=False` because when `pps=True` the posterior predictive distribution is plotted and thus, can only refer to the outcome variable (instead of any of the parameters of the response distribution). For this example, we will simulate our own dataset."
+    "`plot_cap` also has the argument `target` where `target` determines what parameter of the response distribution is plotted as a function of the explanatory variables. This argument is useful in distributional models, i.e., when the response distribution contains a parameter for location, scale and or shape. The default of this argument is `mean` and passing a parameter into `target` only works when the argument `pps=False` because when `pps=True` the posterior predictive distribution is plotted and thus, can only refer to the outcome variable (instead of any of the parameters of the response distribution). For this example, we will simulate our own dataset."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -845,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -871,7 +836,7 @@
        "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -890,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -907,7 +872,7 @@
    "source": [
     "# First, the mean of the response (default)\n",
     "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-    "plot_predictions(model, idata, \"x\", ax=ax);"
+    "plot_cap(model, idata, \"x\", ax=ax);"
    ]
   },
   {
@@ -920,7 +885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -937,29 +902,31 @@
    "source": [
     "# Second, another param. of the distribution: alpha\n",
     "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-    "plot_predictions(model, idata, \"x\", target='alpha', ax=ax);"
+    "plot_cap(model, idata, \"x\", target='alpha', ax=ax);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Wed Aug 16 2023\n",
+      "The watermark extension is already loaded. To reload it, use:\n",
+      "  %reload_ext watermark\n",
+      "Last updated: Sat Jun 24 2023\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.11.0\n",
       "IPython version      : 8.13.2\n",
       "\n",
+      "numpy     : 1.24.2\n",
+      "bambi     : 0.10.0.dev0\n",
       "pandas    : 2.0.1\n",
       "matplotlib: 3.7.1\n",
-      "bambi     : 0.10.0.dev0\n",
       "arviz     : 0.15.1\n",
-      "numpy     : 1.24.2\n",
       "\n",
       "Watermark: 2.3.1\n",
       "\n"
diff --git a/docs/notebooks/plot_comparisons.ipynb b/docs/notebooks/plot_comparisons.ipynb
index 727ff1e01..d1dec6bb1 100644
--- a/docs/notebooks/plot_comparisons.ipynb
+++ b/docs/notebooks/plot_comparisons.ipynb
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,7 +70,7 @@
     "\n",
     "\n",
     "import bambi as bmb\n",
-    "from bambi.plots import comparisons, plot_comparisons"
+    "from bambi.plots import comparisons, plot_comparison"
    ]
   },
   {
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -118,11 +118,48 @@
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " |████████████████████████████████| 100.00% [8000/8000 00:03<00:00 Sampling 4 chains, 0 divergences]\r"
-     ]
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:03&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "name": "stderr",
@@ -159,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -174,7 +211,7 @@
     }
    ],
    "source": [
-    "fig, ax = plot_comparisons(\n",
+    "fig, ax = plot_comparison(\n",
     "    model=fish_model,\n",
     "    idata=fish_idata,\n",
     "    contrast={\"persons\": [1, 4]},\n",
@@ -195,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -220,8 +257,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>term</th>\n",
-       "      <th>estimate_type</th>\n",
-       "      <th>value</th>\n",
+       "      <th>contrast</th>\n",
        "      <th>child</th>\n",
        "      <th>livebait</th>\n",
        "      <th>camper</th>\n",
@@ -234,8 +270,7 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
-       "      <td>(1.0, 4.0)</td>\n",
+       "      <td>(1, 4)</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -246,8 +281,7 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
-       "      <td>(1.0, 4.0)</td>\n",
+       "      <td>(1, 4)</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -258,8 +292,7 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
-       "      <td>(1.0, 4.0)</td>\n",
+       "      <td>(1, 4)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -270,8 +303,7 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
-       "      <td>(1.0, 4.0)</td>\n",
+       "      <td>(1, 4)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -282,8 +314,7 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
-       "      <td>(1.0, 4.0)</td>\n",
+       "      <td>(1, 4)</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -294,8 +325,7 @@
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
-       "      <td>(1.0, 4.0)</td>\n",
+       "      <td>(1, 4)</td>\n",
        "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -308,18 +338,16 @@
        "</div>"
       ],
       "text/plain": [
-       "      term estimate_type       value  ...   estimate lower_3.0% upper_97.0%\n",
-       "0  persons          diff  (1.0, 4.0)  ...   4.834472   2.563472    7.037150\n",
-       "1  persons          diff  (1.0, 4.0)  ...  26.423188  23.739729   29.072748\n",
-       "2  persons          diff  (1.0, 4.0)  ...   1.202003   0.631629    1.780965\n",
-       "3  persons          diff  (1.0, 4.0)  ...   6.571943   5.469275    7.642248\n",
-       "4  persons          diff  (1.0, 4.0)  ...   0.301384   0.143676    0.467608\n",
-       "5  persons          diff  (1.0, 4.0)  ...   1.648417   1.140415    2.187190\n",
-       "\n",
-       "[6 rows x 9 columns]"
+       "      term contrast  child livebait camper   estimate  lower_3.0%  upper_97.0%\n",
+       "0  persons   (1, 4)    0.0      0.0    1.0   4.834472    2.563472     7.037150\n",
+       "1  persons   (1, 4)    0.0      1.0    1.0  26.423188   23.739729    29.072748\n",
+       "2  persons   (1, 4)    1.0      0.0    1.0   1.202003    0.631629     1.780965\n",
+       "3  persons   (1, 4)    1.0      1.0    1.0   6.571943    5.469275     7.642248\n",
+       "4  persons   (1, 4)    2.0      0.0    1.0   0.301384    0.143676     0.467608\n",
+       "5  persons   (1, 4)    2.0      1.0    1.0   1.648417    1.140415     2.187190"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -353,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -378,8 +406,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>term</th>\n",
-       "      <th>estimate_type</th>\n",
-       "      <th>value</th>\n",
+       "      <th>contrast</th>\n",
        "      <th>child</th>\n",
        "      <th>livebait</th>\n",
        "      <th>camper</th>\n",
@@ -392,7 +419,6 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(1, 2)</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -404,7 +430,6 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(1, 2)</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -416,7 +441,6 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(1, 2)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
@@ -428,7 +452,6 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(1, 2)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -440,7 +463,6 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(1, 2)</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
@@ -452,7 +474,6 @@
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(1, 2)</td>\n",
        "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
@@ -464,7 +485,6 @@
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(1, 4)</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -476,7 +496,6 @@
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(1, 4)</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -488,7 +507,6 @@
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(1, 4)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
@@ -500,7 +518,6 @@
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(1, 4)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -512,7 +529,6 @@
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(1, 4)</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
@@ -524,7 +540,6 @@
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(1, 4)</td>\n",
        "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
@@ -536,7 +551,6 @@
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(2, 4)</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -548,7 +562,6 @@
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(2, 4)</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -560,7 +573,6 @@
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(2, 4)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
@@ -572,7 +584,6 @@
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(2, 4)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -584,7 +595,6 @@
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(2, 4)</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
@@ -596,7 +606,6 @@
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>persons</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(2, 4)</td>\n",
        "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
@@ -610,30 +619,48 @@
        "</div>"
       ],
       "text/plain": [
-       "       term estimate_type   value  ...   estimate  lower_3.0%  upper_97.0%\n",
-       "0   persons          diff  (1, 2)  ...   0.527627    0.295451     0.775465\n",
-       "1   persons          diff  (1, 2)  ...   2.883694    2.605690     3.177685\n",
-       "2   persons          diff  (1, 2)  ...   0.131319    0.067339     0.195132\n",
-       "3   persons          diff  (1, 2)  ...   0.717965    0.592968     0.857893\n",
-       "4   persons          diff  (1, 2)  ...   0.032960    0.015212     0.052075\n",
-       "5   persons          diff  (1, 2)  ...   0.180270    0.123173     0.244695\n",
-       "6   persons          diff  (1, 4)  ...   4.834472    2.563472     7.037150\n",
-       "7   persons          diff  (1, 4)  ...  26.423188   23.739729    29.072748\n",
-       "8   persons          diff  (1, 4)  ...   1.202003    0.631629     1.780965\n",
-       "9   persons          diff  (1, 4)  ...   6.571943    5.469275     7.642248\n",
-       "10  persons          diff  (1, 4)  ...   0.301384    0.143676     0.467608\n",
-       "11  persons          diff  (1, 4)  ...   1.648417    1.140415     2.187190\n",
-       "12  persons          diff  (2, 4)  ...   4.306845    2.267097     6.280005\n",
-       "13  persons          diff  (2, 4)  ...  23.539494   20.990931    26.240169\n",
-       "14  persons          diff  (2, 4)  ...   1.070683    0.565931     1.585718\n",
-       "15  persons          diff  (2, 4)  ...   5.853978    4.858957     6.848519\n",
-       "16  persons          diff  (2, 4)  ...   0.268423    0.124033     0.412274\n",
-       "17  persons          diff  (2, 4)  ...   1.468147    1.024800     1.960934\n",
+       "       term contrast  child livebait camper   estimate  lower_3.0%   \n",
+       "0   persons   (1, 2)    0.0      0.0    1.0   0.527627    0.295451  \\\n",
+       "1   persons   (1, 2)    0.0      1.0    1.0   2.883694    2.605690   \n",
+       "2   persons   (1, 2)    1.0      0.0    1.0   0.131319    0.067339   \n",
+       "3   persons   (1, 2)    1.0      1.0    1.0   0.717965    0.592968   \n",
+       "4   persons   (1, 2)    2.0      0.0    1.0   0.032960    0.015212   \n",
+       "5   persons   (1, 2)    2.0      1.0    1.0   0.180270    0.123173   \n",
+       "6   persons   (1, 4)    0.0      0.0    1.0   4.834472    2.563472   \n",
+       "7   persons   (1, 4)    0.0      1.0    1.0  26.423188   23.739729   \n",
+       "8   persons   (1, 4)    1.0      0.0    1.0   1.202003    0.631629   \n",
+       "9   persons   (1, 4)    1.0      1.0    1.0   6.571943    5.469275   \n",
+       "10  persons   (1, 4)    2.0      0.0    1.0   0.301384    0.143676   \n",
+       "11  persons   (1, 4)    2.0      1.0    1.0   1.648417    1.140415   \n",
+       "12  persons   (2, 4)    0.0      0.0    1.0   4.306845    2.267097   \n",
+       "13  persons   (2, 4)    0.0      1.0    1.0  23.539494   20.990931   \n",
+       "14  persons   (2, 4)    1.0      0.0    1.0   1.070683    0.565931   \n",
+       "15  persons   (2, 4)    1.0      1.0    1.0   5.853978    4.858957   \n",
+       "16  persons   (2, 4)    2.0      0.0    1.0   0.268423    0.124033   \n",
+       "17  persons   (2, 4)    2.0      1.0    1.0   1.468147    1.024800   \n",
        "\n",
-       "[18 rows x 9 columns]"
+       "    upper_97.0%  \n",
+       "0      0.775465  \n",
+       "1      3.177685  \n",
+       "2      0.195132  \n",
+       "3      0.857893  \n",
+       "4      0.052075  \n",
+       "5      0.244695  \n",
+       "6      7.037150  \n",
+       "7     29.072748  \n",
+       "8      1.780965  \n",
+       "9      7.642248  \n",
+       "10     0.467608  \n",
+       "11     2.187190  \n",
+       "12     6.280005  \n",
+       "13    26.240169  \n",
+       "14     1.585718  \n",
+       "15     6.848519  \n",
+       "16     0.412274  \n",
+       "17     1.960934  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -698,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -723,8 +750,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>term</th>\n",
-       "      <th>estimate_type</th>\n",
-       "      <th>value</th>\n",
+       "      <th>contrast</th>\n",
        "      <th>persons</th>\n",
        "      <th>child</th>\n",
        "      <th>camper</th>\n",
@@ -737,7 +763,6 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.0</td>\n",
@@ -749,7 +774,6 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
@@ -761,7 +785,6 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>3.0</td>\n",
@@ -773,7 +796,6 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.061224</td>\n",
        "      <td>0.0</td>\n",
@@ -785,7 +807,6 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.061224</td>\n",
        "      <td>1.0</td>\n",
@@ -797,7 +818,6 @@
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.061224</td>\n",
        "      <td>3.0</td>\n",
@@ -809,7 +829,6 @@
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.122449</td>\n",
        "      <td>0.0</td>\n",
@@ -821,7 +840,6 @@
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.122449</td>\n",
        "      <td>1.0</td>\n",
@@ -833,7 +851,6 @@
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.122449</td>\n",
        "      <td>3.0</td>\n",
@@ -845,7 +862,6 @@
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.183674</td>\n",
        "      <td>0.0</td>\n",
@@ -859,22 +875,32 @@
        "</div>"
       ],
       "text/plain": [
-       "       term estimate_type       value  ...  estimate  lower_3.0% upper_97.0%\n",
-       "0  livebait          diff  (0.0, 1.0)  ...  1.694646    1.252803    2.081207\n",
-       "1  livebait          diff  (0.0, 1.0)  ...  0.422448    0.299052    0.551766\n",
-       "2  livebait          diff  (0.0, 1.0)  ...  0.026923    0.012752    0.043035\n",
-       "3  livebait          diff  (0.0, 1.0)  ...  1.787412    1.342979    2.203158\n",
-       "4  livebait          diff  (0.0, 1.0)  ...  0.445555    0.317253    0.580117\n",
-       "5  livebait          diff  (0.0, 1.0)  ...  0.028393    0.013452    0.045276\n",
-       "6  livebait          diff  (0.0, 1.0)  ...  1.885270    1.422937    2.313218\n",
-       "7  livebait          diff  (0.0, 1.0)  ...  0.469929    0.335373    0.609249\n",
-       "8  livebait          diff  (0.0, 1.0)  ...  0.029944    0.014165    0.047593\n",
-       "9  livebait          diff  (0.0, 1.0)  ...  1.988500    1.501650    2.424762\n",
+       "       term    contrast   persons  child camper  estimate  lower_3.0%   \n",
+       "0  livebait  (0.0, 1.0)  1.000000    0.0    1.0  1.694646    1.252803  \\\n",
+       "1  livebait  (0.0, 1.0)  1.000000    1.0    1.0  0.422448    0.299052   \n",
+       "2  livebait  (0.0, 1.0)  1.000000    3.0    1.0  0.026923    0.012752   \n",
+       "3  livebait  (0.0, 1.0)  1.061224    0.0    1.0  1.787412    1.342979   \n",
+       "4  livebait  (0.0, 1.0)  1.061224    1.0    1.0  0.445555    0.317253   \n",
+       "5  livebait  (0.0, 1.0)  1.061224    3.0    1.0  0.028393    0.013452   \n",
+       "6  livebait  (0.0, 1.0)  1.122449    0.0    1.0  1.885270    1.422937   \n",
+       "7  livebait  (0.0, 1.0)  1.122449    1.0    1.0  0.469929    0.335373   \n",
+       "8  livebait  (0.0, 1.0)  1.122449    3.0    1.0  0.029944    0.014165   \n",
+       "9  livebait  (0.0, 1.0)  1.183674    0.0    1.0  1.988500    1.501650   \n",
        "\n",
-       "[10 rows x 9 columns]"
+       "   upper_97.0%  \n",
+       "0     2.081207  \n",
+       "1     0.551766  \n",
+       "2     0.043035  \n",
+       "3     2.203158  \n",
+       "4     0.580117  \n",
+       "5     0.045276  \n",
+       "6     2.313218  \n",
+       "7     0.609249  \n",
+       "8     0.047593  \n",
+       "9     2.424762  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -900,7 +926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -915,7 +941,7 @@
     }
    ],
    "source": [
-    "fig, ax = plot_comparisons(\n",
+    "fig, ax = plot_comparison(\n",
     "    model=fish_model,\n",
     "    idata=fish_idata,\n",
     "    contrast=\"livebait\",\n",
@@ -934,7 +960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -949,7 +975,7 @@
     }
    ],
    "source": [
-    "fig, ax = plot_comparisons(\n",
+    "fig, ax = plot_comparison(\n",
     "    model=fish_model,\n",
     "    idata=fish_idata,\n",
     "    contrast=\"livebait\",\n",
@@ -972,7 +998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1004,8 +1030,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>term</th>\n",
-       "      <th>estimate_type</th>\n",
-       "      <th>value</th>\n",
+       "      <th>contrast</th>\n",
        "      <th>camper</th>\n",
        "      <th>child</th>\n",
        "      <th>persons</th>\n",
@@ -1018,7 +1043,6 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1030,7 +1054,6 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1042,7 +1065,6 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1054,7 +1076,6 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1066,7 +1087,6 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1078,7 +1098,6 @@
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>2.0</td>\n",
@@ -1090,7 +1109,6 @@
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1102,7 +1120,6 @@
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>0.0</td>\n",
        "      <td>3.0</td>\n",
@@ -1114,7 +1131,6 @@
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>2.0</td>\n",
@@ -1126,7 +1142,6 @@
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1140,22 +1155,32 @@
        "</div>"
       ],
       "text/plain": [
-       "       term estimate_type       value  ...  estimate  lower_3.0%  upper_97.0%\n",
-       "0  livebait          diff  (0.0, 1.0)  ...  0.864408    0.627063     1.116105\n",
-       "1  livebait          diff  (0.0, 1.0)  ...  1.694646    1.252803     2.081207\n",
-       "2  livebait          diff  (0.0, 1.0)  ...  0.864408    0.627063     1.116105\n",
-       "3  livebait          diff  (0.0, 1.0)  ...  1.009094    0.755449     1.249551\n",
-       "4  livebait          diff  (0.0, 1.0)  ...  0.864408    0.627063     1.116105\n",
-       "5  livebait          diff  (0.0, 1.0)  ...  1.453235    0.964674     1.956434\n",
-       "6  livebait          diff  (0.0, 1.0)  ...  1.233247    0.900295     1.569891\n",
-       "7  livebait          diff  (0.0, 1.0)  ...  0.188019    0.090328     0.289560\n",
-       "8  livebait          diff  (0.0, 1.0)  ...  0.606361    0.390571     0.818549\n",
-       "9  livebait          diff  (0.0, 1.0)  ...  1.694646    1.252803     2.081207\n",
+       "       term    contrast camper  child  persons  estimate  lower_3.0%   \n",
+       "0  livebait  (0.0, 1.0)    0.0    0.0      1.0  0.864408    0.627063  \\\n",
+       "1  livebait  (0.0, 1.0)    1.0    0.0      1.0  1.694646    1.252803   \n",
+       "2  livebait  (0.0, 1.0)    0.0    0.0      1.0  0.864408    0.627063   \n",
+       "3  livebait  (0.0, 1.0)    1.0    1.0      2.0  1.009094    0.755449   \n",
+       "4  livebait  (0.0, 1.0)    0.0    0.0      1.0  0.864408    0.627063   \n",
+       "5  livebait  (0.0, 1.0)    1.0    2.0      4.0  1.453235    0.964674   \n",
+       "6  livebait  (0.0, 1.0)    0.0    1.0      3.0  1.233247    0.900295   \n",
+       "7  livebait  (0.0, 1.0)    0.0    3.0      4.0  0.188019    0.090328   \n",
+       "8  livebait  (0.0, 1.0)    1.0    2.0      3.0  0.606361    0.390571   \n",
+       "9  livebait  (0.0, 1.0)    1.0    0.0      1.0  1.694646    1.252803   \n",
        "\n",
-       "[10 rows x 9 columns]"
+       "   upper_97.0%  \n",
+       "0     1.116105  \n",
+       "1     2.081207  \n",
+       "2     1.116105  \n",
+       "3     1.249551  \n",
+       "4     1.116105  \n",
+       "5     1.956434  \n",
+       "6     1.569891  \n",
+       "7     0.289560  \n",
+       "8     0.818549  \n",
+       "9     2.081207  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1175,7 +1200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1305,7 +1330,7 @@
        "9    1.0      1.0    1.0      1.0    0.0"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1341,7 +1366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1366,8 +1391,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>term</th>\n",
-       "      <th>estimate_type</th>\n",
-       "      <th>value</th>\n",
+       "      <th>contrast</th>\n",
        "      <th>estimate</th>\n",
        "      <th>lower_3.0%</th>\n",
        "      <th>upper_97.0%</th>\n",
@@ -1377,7 +1401,6 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>3.649691</td>\n",
        "      <td>2.956185</td>\n",
@@ -1388,11 +1411,11 @@
        "</div>"
       ],
       "text/plain": [
-       "       term estimate_type       value  estimate  lower_3.0%  upper_97.0%\n",
-       "0  livebait          diff  (0.0, 1.0)  3.649691    2.956185     4.333621"
+       "       term    contrast  estimate  lower_3.0%  upper_97.0%\n",
+       "0  livebait  (0.0, 1.0)  3.649691    2.956185     4.333621"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1418,7 +1441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1430,7 +1453,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1458,7 +1481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1483,8 +1506,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>term</th>\n",
-       "      <th>estimate_type</th>\n",
-       "      <th>value</th>\n",
+       "      <th>contrast</th>\n",
        "      <th>persons</th>\n",
        "      <th>estimate</th>\n",
        "      <th>lower_3.0%</th>\n",
@@ -1495,7 +1517,6 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.374203</td>\n",
@@ -1505,7 +1526,6 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>2.0</td>\n",
        "      <td>1.963362</td>\n",
@@ -1515,7 +1535,6 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>3.0</td>\n",
        "      <td>3.701510</td>\n",
@@ -1525,7 +1544,6 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>4.0</td>\n",
        "      <td>7.358662</td>\n",
@@ -1537,16 +1555,14 @@
        "</div>"
       ],
       "text/plain": [
-       "       term estimate_type       value  ...  estimate  lower_3.0%  upper_97.0%\n",
-       "0  livebait          diff  (0.0, 1.0)  ...  1.374203    1.011290     1.708711\n",
-       "1  livebait          diff  (0.0, 1.0)  ...  1.963362    1.543330     2.376636\n",
-       "2  livebait          diff  (0.0, 1.0)  ...  3.701510    3.056586     4.357385\n",
-       "3  livebait          diff  (0.0, 1.0)  ...  7.358662    6.047642     8.655654\n",
-       "\n",
-       "[4 rows x 7 columns]"
+       "       term    contrast  persons  estimate  lower_3.0%  upper_97.0%\n",
+       "0  livebait  (0.0, 1.0)      1.0  1.374203    1.011290     1.708711\n",
+       "1  livebait  (0.0, 1.0)      2.0  1.963362    1.543330     2.376636\n",
+       "2  livebait  (0.0, 1.0)      3.0  3.701510    3.056586     4.357385\n",
+       "3  livebait  (0.0, 1.0)      4.0  7.358662    6.047642     8.655654"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1564,7 +1580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1589,8 +1605,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>term</th>\n",
-       "      <th>estimate_type</th>\n",
-       "      <th>value</th>\n",
+       "      <th>contrast</th>\n",
        "      <th>persons</th>\n",
        "      <th>camper</th>\n",
        "      <th>estimate</th>\n",
@@ -1602,7 +1617,6 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1613,7 +1627,6 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1624,7 +1637,6 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1635,7 +1647,6 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1646,7 +1657,6 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>3.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1657,7 +1667,6 @@
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>3.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1668,7 +1677,6 @@
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>4.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1679,7 +1687,6 @@
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>livebait</td>\n",
-       "      <td>diff</td>\n",
        "      <td>(0.0, 1.0)</td>\n",
        "      <td>4.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1692,20 +1699,18 @@
        "</div>"
       ],
       "text/plain": [
-       "       term estimate_type       value  ...   estimate lower_3.0%  upper_97.0%\n",
-       "0  livebait          diff  (0.0, 1.0)  ...   0.864408   0.627063     1.116105\n",
-       "1  livebait          diff  (0.0, 1.0)  ...   1.694646   1.252803     2.081207\n",
-       "2  livebait          diff  (0.0, 1.0)  ...   1.424598   1.078389     1.777154\n",
-       "3  livebait          diff  (0.0, 1.0)  ...   2.344439   1.872191     2.800661\n",
-       "4  livebait          diff  (0.0, 1.0)  ...   2.429459   1.871578     2.964242\n",
-       "5  livebait          diff  (0.0, 1.0)  ...   4.443540   3.747840     5.170052\n",
-       "6  livebait          diff  (0.0, 1.0)  ...   3.541921   2.686445     4.391176\n",
-       "7  livebait          diff  (0.0, 1.0)  ...  10.739204   9.024702    12.432764\n",
-       "\n",
-       "[8 rows x 8 columns]"
+       "       term    contrast  persons camper   estimate  lower_3.0%  upper_97.0%\n",
+       "0  livebait  (0.0, 1.0)      1.0    0.0   0.864408    0.627063     1.116105\n",
+       "1  livebait  (0.0, 1.0)      1.0    1.0   1.694646    1.252803     2.081207\n",
+       "2  livebait  (0.0, 1.0)      2.0    0.0   1.424598    1.078389     1.777154\n",
+       "3  livebait  (0.0, 1.0)      2.0    1.0   2.344439    1.872191     2.800661\n",
+       "4  livebait  (0.0, 1.0)      3.0    0.0   2.429459    1.871578     2.964242\n",
+       "5  livebait  (0.0, 1.0)      3.0    1.0   4.443540    3.747840     5.170052\n",
+       "6  livebait  (0.0, 1.0)      4.0    0.0   3.541921    2.686445     4.391176\n",
+       "7  livebait  (0.0, 1.0)      4.0    1.0  10.739204    9.024702    12.432764"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1731,7 +1736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1746,7 +1751,7 @@
     }
    ],
    "source": [
-    "fig, ax = plot_comparisons(\n",
+    "fig, ax = plot_comparison(\n",
     "    model=fish_model,\n",
     "    idata=fish_idata,\n",
     "    contrast=\"livebait\",\n",
@@ -1768,7 +1773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1785,7 +1790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1800,11 +1805,48 @@
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " |████████████████████████████████| 100.00% [8000/8000 00:15<00:00 Sampling 4 chains, 0 divergences]\r"
-     ]
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:15&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "name": "stderr",
@@ -1835,7 +1877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1850,7 +1892,7 @@
     }
    ],
    "source": [
-    "fig, ax = plot_comparisons(\n",
+    "fig, ax = plot_comparison(\n",
     "    model=titanic_model,\n",
     "    idata=titanic_idata,\n",
     "    contrast={\"PClass\": [1, 3]},\n",
diff --git a/tests/test_plots.py b/tests/test_plots.py
index 257ac792e..9ed19eb27 100644
--- a/tests/test_plots.py
+++ b/tests/test_plots.py
@@ -6,7 +6,7 @@
 import pytest
 
 import bambi as bmb
-from bambi.plots import plot_comparisons, plot_predictions, plot_slopes
+from bambi.plots import plot_cap, plot_comparison, plot_slopes
 
 
 @pytest.fixture(scope="module")
@@ -25,28 +25,28 @@ def mtcars():
 
 class TestCommon:
     """
-    Tests argments that are common to both 'plot_predictions', 'plot_comparisons',
+    Tests argments that are common to both 'plot_cap', 'plot_comparison',
     and 'plot_slopes' such as figure object and uncertainty arguments.
     """
     @pytest.mark.parametrize("pps", [False, True])
     def test_use_hdi(self, mtcars, pps):
         model, idata = mtcars
-        plot_comparisons(model, idata, "hp", "am", use_hdi=False)
-        plot_predictions(
+        plot_comparison(model, idata, "hp", "am", use_hdi=False)
+        plot_cap(
             model, 
             idata, 
             ["hp", "cyl", "gear"], 
             pps=pps,
             use_hdi=False
         )
-        plot_slopes(model, idata, "hp", "am", use_hdi=False)
+        #plot_slopes(model, idata, "hp", "am", use_hdi=False)
         
     
     @pytest.mark.parametrize("pps", [False, True])
     def test_hdi_prob(self, mtcars, pps):
         model, idata = mtcars
-        plot_comparisons(model, idata, "am", "hp", prob=0.8)
-        plot_predictions(
+        plot_comparison(model, idata, "am", "hp", prob=0.8)
+        plot_cap(
             model, 
             idata,
             ["hp", "cyl", "gear"], 
@@ -58,8 +58,8 @@ def test_hdi_prob(self, mtcars, pps):
         with pytest.raises(
             ValueError, match="'prob' must be greater than 0 and smaller than 1. It is 1.1."
         ):
-            plot_comparisons(model, idata, "am", "hp", prob=1.1)
-            plot_predictions(
+            plot_comparison(model, idata, "am", "hp", prob=1.1)
+            plot_cap(
                 model, 
                 idata, 
                 ["hp", "cyl", "gear"], 
@@ -70,8 +70,8 @@ def test_hdi_prob(self, mtcars, pps):
         with pytest.raises(
             ValueError, match="'prob' must be greater than 0 and smaller than 1. It is -0.1."
         ):
-            plot_comparisons(model, idata, "am", "hp", prob=-0.1)
-            plot_predictions(
+            plot_comparison(model, idata, "am", "hp", prob=-0.1)
+            plot_cap(
                 model, 
                 idata, 
                 ["hp", "cyl", "gear"], 
@@ -83,8 +83,8 @@ def test_hdi_prob(self, mtcars, pps):
     @pytest.mark.parametrize("pps", [False, True])
     def test_legend(self, mtcars, pps):
         model, idata = mtcars
-        plot_comparisons(model, idata, "am", "hp", legend=False)
-        plot_predictions(model, idata, ["hp"], pps=pps,legend=False)
+        plot_comparison(model, idata, "am", "hp", legend=False)
+        plot_cap(model, idata, ["hp"], pps=pps,legend=False)
         plot_slopes(model, idata, "hp", "am", legend=False)
     
 
@@ -92,14 +92,14 @@ def test_legend(self, mtcars, pps):
     def test_ax(self, mtcars, pps):
         model, idata = mtcars
         fig, ax = plt.subplots()
-        fig_r, ax_r = plot_comparisons(model, idata, "am", "hp", ax=ax)
+        fig_r, ax_r = plot_comparison(model, idata, "am", "hp", ax=ax)
 
         assert isinstance(ax_r, np.ndarray)
         assert fig is fig_r
         assert ax is ax_r[0]
 
         fig, ax = plt.subplots()
-        fig_r, ax_r = plot_predictions(model, idata, ["hp"], pps=pps, ax=ax)
+        fig_r, ax_r = plot_cap(model, idata, ["hp"], pps=pps, ax=ax)
 
         assert isinstance(ax_r, np.ndarray)
         assert fig is fig_r
@@ -115,7 +115,7 @@ def test_ax(self, mtcars, pps):
 
 class TestCap:
     """
-    Tests the 'plot_predictions' function for different combinations of main, group,
+    Tests the 'plot_cap' function for different combinations of main, group,
     and panel variables.
     """
     @pytest.mark.parametrize("pps", [False, True])
@@ -129,7 +129,7 @@ class TestCap:
     )
     def test_basic(self, mtcars, covariates, pps):
         model, idata = mtcars
-        plot_predictions(model, idata, covariates, pps=pps)
+        plot_cap(model, idata, covariates, pps=pps)
 
 
     @pytest.mark.parametrize("pps", [False, True])
@@ -143,7 +143,7 @@ def test_basic(self, mtcars, covariates, pps):
     )
     def test_with_groups(self, mtcars, covariates, pps):
         model, idata = mtcars
-        plot_predictions(model, idata, covariates, pps=pps)
+        plot_cap(model, idata, covariates, pps=pps)
 
 
     @pytest.mark.parametrize("pps", [False, True])
@@ -156,13 +156,13 @@ def test_with_groups(self, mtcars, covariates, pps):
     )
     def test_with_group_and_panel(self, mtcars, covariates, pps):
         model, idata = mtcars
-        plot_predictions(model, idata, covariates, pps=pps)
+        plot_cap(model, idata, covariates, pps=pps)
 
 
     @pytest.mark.parametrize("pps", [False, True])
     def test_fig_kwargs(self, mtcars, pps):
         model, idata = mtcars
-        plot_predictions(
+        plot_cap(
             model,
             idata,
             [ "hp", "cyl", "gear"],
@@ -174,7 +174,7 @@ def test_fig_kwargs(self, mtcars, pps):
     @pytest.mark.parametrize("pps", [False, True])
     def test_subplot_kwargs(self, mtcars, pps):
         model, idata = mtcars
-        plot_predictions(
+        plot_cap(
             model,
             idata,
             ["hp", "drat"],
@@ -193,7 +193,7 @@ def test_subplot_kwargs(self, mtcars, pps):
     )
     def test_transforms(self, mtcars, transforms, pps):
         model, idata = mtcars
-        plot_predictions(model, idata, ["hp"], pps=pps, transforms=transforms)
+        plot_cap(model, idata, ["hp"], pps=pps, transforms=transforms)
 
 
     @pytest.mark.parametrize("pps", [False, True])
@@ -213,9 +213,9 @@ def test_multiple_outputs_with_alias(self, pps):
         # Without alias
         idata = model.fit(tune=100, draws=100, random_seed=1234)
         # Test default target
-        plot_predictions(model, idata,  "x", pps=pps)
+        plot_cap(model, idata,  "x", pps=pps)
         # Test user supplied target argument
-        plot_predictions(model, idata, "x", "alpha", pps=False)
+        plot_cap(model, idata, "x", "alpha", pps=False)
 
         # With alias
         alias = {"alpha": {"Intercept": "sd_intercept", "x": "sd_x", "alpha": "sd_alpha"}}
@@ -223,12 +223,12 @@ def test_multiple_outputs_with_alias(self, pps):
         idata = model.fit(tune=100, draws=100, random_seed=1234)
 
         # Test user supplied target argument
-        plot_predictions(model, idata, "x", "alpha", pps=False)
+        plot_cap(model, idata, "x", "alpha", pps=False)
 
 
 class TestComparison:
     """
-    Tests the plot_comparisons function for different combinations of
+    Tests the plot_comparison function for different combinations of
     contrast and conditional variables, and user inputs.
     """
     @pytest.mark.parametrize(
@@ -239,7 +239,7 @@ class TestComparison:
     )
     def test_basic(self, mtcars, contrast, conditional):
         model, idata = mtcars
-        plot_comparisons(model, idata, contrast, conditional)
+        plot_comparison(model, idata, contrast, conditional)
     
 
     @pytest.mark.parametrize(
@@ -250,7 +250,7 @@ def test_basic(self, mtcars, contrast, conditional):
     )
     def test_with_groups(self, mtcars, contrast, conditional):
         model, idata = mtcars
-        plot_comparisons(model, idata, contrast, conditional)
+        plot_comparison(model, idata, contrast, conditional)
     
 
     @pytest.mark.parametrize(
@@ -261,7 +261,7 @@ def test_with_groups(self, mtcars, contrast, conditional):
     )
     def test_with_user_values(self, mtcars, contrast, conditional):
         model, idata = mtcars
-        plot_comparisons(model, idata, contrast, conditional)
+        plot_comparison(model, idata, contrast, conditional)
     
 
     @pytest.mark.parametrize(
@@ -271,7 +271,7 @@ def test_with_user_values(self, mtcars, contrast, conditional):
     )
     def test_subplot_kwargs(self, mtcars, contrast, conditional, subplot_kwargs):
         model, idata = mtcars
-        plot_comparisons(model, idata, contrast, conditional, subplot_kwargs=subplot_kwargs)
+        plot_comparison(model, idata, contrast, conditional, subplot_kwargs=subplot_kwargs)
 
     
     @pytest.mark.parametrize(
@@ -282,7 +282,7 @@ def test_subplot_kwargs(self, mtcars, contrast, conditional, subplot_kwargs):
     )
     def test_transforms(self, mtcars, contrast, conditional, transforms):
         model, idata = mtcars
-        plot_comparisons(model, idata, contrast, conditional, transforms=transforms)
+        plot_comparison(model, idata, contrast, conditional, transforms=transforms)
     
 
     @pytest.mark.parametrize("average_by", ["am", "drat", ["am", "drat"]])
@@ -290,10 +290,10 @@ def test_average_by(self, mtcars, average_by):
         model, idata = mtcars
 
         # grid of values with average_by
-        plot_comparisons(model, idata, "hp", ["am", "drat"], average_by)
+        plot_comparison(model, idata, "hp", ["am", "drat"], average_by)
         
         # unit level with average by
-        plot_comparisons(model, idata, "hp", None, average_by)
+        plot_comparison(model, idata, "hp", None, average_by)
 
 
 class TestSlopes: