diff --git a/examples/plot_huggingface_model.py b/examples/plot_huggingface_model.py
index 9f56e3e..7d1c373 100755
--- a/examples/plot_huggingface_model.py
+++ b/examples/plot_huggingface_model.py
@@ -179,7 +179,7 @@ def plot_quantiles_by_latlon(df, quantiles):
         min=0,
         max=1,
         step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1),
-        name="Quantile:",
+        name="Quantile: ",
     )
 
     q_val = alt.selection_point(
diff --git a/examples/plot_predict_custom.py b/examples/plot_predict_custom.py
index 03e6c20..d70f563 100755
--- a/examples/plot_predict_custom.py
+++ b/examples/plot_predict_custom.py
@@ -23,7 +23,7 @@
 
 np.random.seed(0)
 
-n_test_samples = 10
+n_test_samples = 100
 
 
 def predict(reg, X, quantiles=0.5, what=None):
@@ -98,7 +98,7 @@ def predict(reg, X, quantiles=0.5, what=None):
 def plot_ecdf(df):
     min_idx = df["sample_idx"].min()
     max_idx = df["sample_idx"].max()
-    slider = alt.binding_range(min=min_idx, max=max_idx, step=1, name="Sample Index:")
+    slider = alt.binding_range(min=min_idx, max=max_idx, step=1, name="Sample Index: ")
     sample_selection = alt.param(value=0, bind=slider, name="sample_idx")
 
     tooltip = [
diff --git a/examples/plot_quantile_conformalized.py b/examples/plot_quantile_conformalized.py
index 58b0e98..62689b6 100755
--- a/examples/plot_quantile_conformalized.py
+++ b/examples/plot_quantile_conformalized.py
@@ -8,9 +8,9 @@
 while QRF may require additional calibration for reliable interval estimates.
 Notice that in this example, by using CQR we obtain a level of coverage (i.e.,
 percentage of samples that actaully fall within their prediction interval)
-that is closer to the target level. Adapted from "Prediction intervals:
-Quantile Regression Forests" by Carl McBride Ellis:
-https://www.kaggle.com/code/carlmcbrideellis/prediction-intervals-quantile-regression-forests.
+that is generally closer to the target level. Adapted from `"Prediction
+intervals: Quantile Regression Forests" by Carl McBride Ellis
+<https://www.kaggle.com/code/carlmcbrideellis/prediction-intervals-quantile-regression-forests>`_.
 """
 
 import altair as alt
@@ -30,8 +30,7 @@
 random_state = 0
 rng = check_random_state(random_state)
 
-cov_pct = 95  # the "coverage level"
-alpha = (100 - cov_pct) / 100
+coverages = list(np.arange(11) / 10)  # the "coverage level"
 
 # Load the California Housing Prices dataset.
 california = datasets.fetch_california_housing()
@@ -113,7 +112,7 @@ def cqr_strategy(alpha, X_train, X_test, y_train, y_test):
     conf_scores = (np.vstack((a, b)).T).max(axis=1)
 
     # Get the 1-alpha quantile `s` from the distribution of conformity scores.
-    s = np.quantile(conf_scores, (1 - alpha) * (1 + (1 / (len(y_calib)))))
+    s = np.quantile(conf_scores, np.clip((1 - alpha) * (1 + (1 / (len(y_calib)))), 0, 1))
 
     # Subtract `s` from the lower quantile and add it to the upper quantile.
     y_conf_low = y_pred_low - s
@@ -129,12 +128,15 @@ def cqr_strategy(alpha, X_train, X_test, y_train, y_test):
 
 
 # Get strategy outputs as a data frame.
-args = (alpha, X_train, X_test, y_train, y_test)
-df = pd.concat([qrf_strategy(*args), cqr_strategy(*args)])
+dfs = []
+for cov_frac in coverages:
+    alpha = float(round(1 - cov_frac, 2))
+    args = (alpha, X_train, X_test, y_train, y_test)
+    dfs.append(pd.concat([qrf_strategy(*args), cqr_strategy(*args)]).assign(alpha=alpha))
+df = pd.concat(dfs)
 
-# Calculate coverage and width metrics.
 metrics = (
-    df.groupby("strategy")
+    df.groupby(["alpha", "strategy"])
     .apply(
         lambda grp: pd.Series(
             {
@@ -147,10 +149,14 @@ def cqr_strategy(alpha, X_train, X_test, y_train, y_test):
 )
 
 # Merge the metrics into the data frame.
-df = df.merge(metrics, on="strategy", how="left")
+df = df.merge(metrics, on=["alpha", "strategy"], how="left")
 
 
 def plot_prediction_intervals(df, domain):
+    slider = alt.binding_range(min=0, max=1, step=0.1, name="Coverage: ")
+    cov_selection = alt.param(value=0.9, bind=slider, name="coverage")
+    cov_tol = 0.01
+
     click = alt.selection_point(fields=["y_label"], bind="legend")
 
     color_circle = alt.Color(
@@ -168,10 +174,17 @@ def plot_prediction_intervals(df, domain):
         alt.Tooltip("y_label:N", title="Within Interval"),
     ]
 
-    base = alt.Chart(df).transform_calculate(
-        y_label=(
-            "((datum.y_test >= datum.y_pred_low) & (datum.y_test <= datum.y_pred_upp))"
-            " ? 'Yes' : 'No'"
+    base = (
+        alt.Chart(df)
+        .transform_filter(
+            (1 - alt.datum["alpha"] - cov_tol <= cov_selection)
+            & (1 - alt.datum["alpha"] + cov_tol >= cov_selection)
+        )
+        .transform_calculate(
+            y_label=(
+                "((datum.y_test >= datum.y_pred_low) & (datum.y_test <= datum.y_pred_upp))"
+                " ? 'Yes' : 'No'"
+            )
         )
     )
 
@@ -224,11 +237,13 @@ def plot_prediction_intervals(df, domain):
     )
 
     text_coverage = (
-        base.transform_aggregate(coverage="mean(coverage)", groupby=["strategy"])
+        base.transform_aggregate(
+            alpha="mean(alpha)", coverage="mean(coverage)", groupby=["strategy"]
+        )
         .transform_calculate(
             coverage_text=(
-                f"'Coverage: ' + format({alt.datum['coverage'] * 100}, '.1f') + '%'"
-                f" + ' (target = {cov_pct}%)'"
+                f"'Coverage: ' + format(datum.coverage * 100, '.1f') + '%'"
+                f" + ' (target = ' + format((1 - datum.alpha) * 100, '.1f') + '%)'"
             )
         )
         .mark_text(align="left", baseline="top")
@@ -251,7 +266,9 @@ def plot_prediction_intervals(df, domain):
         )
     )
 
-    chart = bar + tick_low + tick_upp + circle + diagonal + text_coverage + text_with
+    chart = (bar + tick_low + tick_upp + circle + diagonal + text_coverage + text_with).add_params(
+        cov_selection
+    )
 
     return chart
 
diff --git a/examples/plot_quantile_interpolation.py b/examples/plot_quantile_interpolation.py
index a34bd5d..2cabb1f 100755
--- a/examples/plot_quantile_interpolation.py
+++ b/examples/plot_quantile_interpolation.py
@@ -76,8 +76,8 @@
 
 
 def plot_interpolations(df, legend):
-    slider = alt.binding_range(min=0, max=1, step=0.01, name="Prediction Interval:")
-    interval_selection = alt.param(value=0.95, bind=slider, name="interval")
+    slider = alt.binding_range(min=0, max=1, step=0.01, name="Prediction Interval: ")
+    interval_selection = alt.param(value=0.9, bind=slider, name="interval")
     interval_tol = 0.001
 
     click = alt.selection_point(fields=["method"], bind="legend")
diff --git a/examples/plot_quantile_multioutput.py b/examples/plot_quantile_multioutput.py
index 1524931..b922605 100755
--- a/examples/plot_quantile_multioutput.py
+++ b/examples/plot_quantile_multioutput.py
@@ -80,7 +80,7 @@ def make_func_Xy(funcs, bounds, n_samples):
 
 
 def plot_multioutputs(df, legend):
-    slider = alt.binding_range(min=0, max=1, step=0.05, name="Prediction Interval:")
+    slider = alt.binding_range(min=0, max=1, step=0.05, name="Prediction Interval: ")
     interval_selection = alt.param(value=0.95, bind=slider, name="interval")
     interval_tol = 0.001
 
diff --git a/examples/plot_quantile_vs_standard.py b/examples/plot_quantile_vs_standard.py
index bc511e1..6a608d1 100755
--- a/examples/plot_quantile_vs_standard.py
+++ b/examples/plot_quantile_vs_standard.py
@@ -6,11 +6,13 @@
 forest and a standard random forest regressor on a synthetic, right-skewed
 dataset. In a right-skewed distribution, the mean is to the right of the
 median. As illustrated by a greater overlap in the frequencies of the actual
-and predicted values, the median estimated by a quantile regressor can be a
-more reliable estimator of a skewed distribution than the mean.
+and predicted values, the median (quantile = 0.5) estimated by a quantile
+regressor can be a more reliable estimator of a skewed distribution than the
+mean.
 """
 
 import altair as alt
+import numpy as np
 import pandas as pd
 import scipy as sp
 from sklearn.ensemble import RandomForestRegressor
@@ -29,6 +31,8 @@
 y = skewnorm_rv.rvs(n_samples)
 X = rng.randn(n_samples, 2) * y.reshape(-1, 1)
 
+quantiles = list(np.arange(101) / 100)
+
 regr_rf = RandomForestRegressor(n_estimators=10, random_state=0)
 regr_qrf = RandomForestQuantileRegressor(n_estimators=10, random_state=0)
 
@@ -38,7 +42,7 @@
 regr_qrf.fit(X_train, y_train)
 
 y_pred_rf = regr_rf.predict(X_test)  # standard RF predictions (mean)
-y_pred_qrf = regr_qrf.predict(X_test, quantiles=0.5)  # QRF predictions (median)
+y_pred_qrf = regr_qrf.predict(X_test, quantiles=quantiles)  # QRF predictions (quantiles)
 
 legend = {
     "Actual": "#c0c0c0",
@@ -46,10 +50,30 @@
     "QRF (Median)": "#006aff",
 }
 
-df = pd.DataFrame({"actual": y_test, "rf": y_pred_rf, "qrf": y_pred_qrf})
+df = pd.concat(
+    [
+        pd.DataFrame({"actual": y_test, "rf": y_pred_rf, "qrf": y_pred_qrf[..., q_idx]}).assign(
+            quantile=quantile
+        )
+        for q_idx, quantile in enumerate(quantiles)
+    ]
+)
 
 
 def plot_prediction_histograms(df, legend):
+    slider = alt.binding_range(
+        min=0,
+        max=1,
+        step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1),
+        name="Quantile: ",
+    )
+
+    q_val = alt.selection_point(
+        value=0.5,
+        bind=slider,
+        fields=["quantile"],
+    )
+
     click = alt.selection_point(fields=["label"], bind="legend")
 
     color = alt.condition(
@@ -60,14 +84,15 @@ def plot_prediction_histograms(df, legend):
 
     chart = (
         alt.Chart(df)
-        .transform_calculate(calculate=f"round({alt.datum['actual']} * 10) / 10", as_="Actual")
-        .transform_calculate(calculate=f"round({alt.datum['rf']} * 10) / 10", as_="RF (Mean)")
-        .transform_calculate(calculate=f"round({alt.datum['qrf']} * 10) / 10", as_="QRF (Median)")
-        .transform_fold(["Actual", "RF (Mean)", "QRF (Median)"], as_=["label", "value"])
+        .transform_filter(q_val)
+        .transform_calculate(calculate=f"round(datum.actual * 10) / 10", as_="Actual")
+        .transform_calculate(calculate=f"round(datum.rf * 10) / 10", as_="RF (Mean)")
+        .transform_calculate(calculate=f"round(datum.qrf * 10) / 10", as_="QRF (Quantile)")
+        .transform_fold(["Actual", "RF (Mean)", "QRF (Quantile)"], as_=["label", "value"])
         .mark_bar()
         .encode(
             x=alt.X(
-                "value:O",
+                "value:N",
                 axis=alt.Axis(
                     labelAngle=0,
                     labelExpr="datum.value % 0.5 == 0 ? datum.value : null",
@@ -83,7 +108,7 @@ def plot_prediction_histograms(df, legend):
                 alt.Tooltip("count():Q", format=",d", title="Counts"),
             ],
         )
-        .add_params(click)
+        .add_params(q_val, click)
         .configure_range(category=alt.RangeScheme(list(legend.values())))
         .properties(height=400, width=650)
     )
diff --git a/examples/plot_treeshap_example.py b/examples/plot_treeshap_example.py
index 8950926..d1f6826 100644
--- a/examples/plot_treeshap_example.py
+++ b/examples/plot_treeshap_example.py
@@ -102,7 +102,7 @@ def plot_shap_waterfall_with_quantiles(df, height=300):
         min=0,
         max=1,
         step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1),
-        name="Quantile:",
+        name="Quantile: ",
     )
 
     q_val = alt.selection_point(