diff --git a/bangmetric/dprime.py b/bangmetric/dprime.py
index bd15b87..3e5ac94 100644
--- a/bangmetric/dprime.py
+++ b/bangmetric/dprime.py
@@ -6,17 +6,17 @@
 #
 # License: BSD
 
-__all__ = ['dprime', 'dprime_ova_from_confusion']
+__all__ = ['dprime', 'dprime_from_confusion_ova']
 
 import numpy as np
 from scipy.stats import norm
 
 DEFAULT_FUDGE_FACTOR = 0.5
 DEFAULT_FUDGE_MODE = 'correction'
-ATOL = 1e-7
+ATOL = 1e-6
 
 
-def dprime(y_pred, y_true):
+def dprime(y_pred, y_true, **kwargs):
     """Computes the d-prime sensitivity index of the predictions.
 
     Parameters
@@ -29,10 +29,14 @@ def dprime(y_pred, y_true):
     y_pred: array, shape = [n_samples]
         Predicted values (real).
 
+    kwargs: named arguments, optional
+        Passed to ``dprime_from_samp()``.
+
     Returns
     -------
     dp: float or None
-        d-prime, None if d-prime is undefined
+        d-prime, None if d-prime is undefined and raw d-prime value (``safedp=False``)
+        is not requested (default).
 
     References
     ----------
@@ -51,23 +55,78 @@ def dprime(y_pred, y_true):
     assert y_pred.ndim == 1
 
     # -- actual computation
-    pos = y_true > 0
-    neg = ~pos
+    i_pos = y_true > 0
+    i_neg = ~i_pos
+
+    pos = y_pred[i_pos]
+    neg = y_pred[i_neg]
+
+    dp = dprime_from_samp(pos, neg, bypass_nchk=True, **kwargs)
+    return dp
+
+
+def dprime_from_samp(pos, neg, maxv=None, minv=None, safedp=True, bypass_nchk=False):
+    """Computes the d-prime sensitivity index from positive and negative samples.
+
+    Parameters
+    ----------
+    pos: array-like
+        Positive sample values (e.g., raw projection values of the positive classifier).
+
+    neg: array-like
+        Negative sample values.
+
+    maxv: float, optional
+        Maximum possible d-prime value. If None (default), there's no limit on
+        the maximum value.
+
+    minv: float, optional
+        Minimum possible d-prime value. If None (default), there's no limit.
+
+    safedp: bool, optional
+        If True (default), this function will return None if the resulting d-prime 
+        value becomes non-finite.
+
+    bypass_nchk: bool, optional
+        If False (default), do not bypass the test to ensure that enough positive 
+        and negatives samples are there for the variance estimation.
 
-    assert pos.sum() > 1, 'Not enough positives to estimate the variance'
-    assert neg.sum() > 1, 'Not enough negatives to estimate the variance'
+    Returns
+    -------
+    dp: float or None
+        d-prime, None if d-prime is undefined and raw d-prime value (``safedp=False``)
+        is not requested (default).
+
+    References
+    ----------
+    http://en.wikipedia.org/wiki/D'
+    """
+
+    pos = np.array(pos)
+    neg = np.array(neg)
 
-    pos_mean = y_pred[pos].mean()
-    neg_mean = y_pred[neg].mean()
-    pos_var = y_pred[pos].var(ddof=1)
-    neg_var = y_pred[neg].var(ddof=1)
+    if not bypass_nchk:
+        assert pos.size > 1, 'Not enough positive samples to estimate the variance'
+        assert neg.size > 1, 'Not enough negative samples to estimate the variance'
+
+    pos_mean = pos.mean()
+    neg_mean = neg.mean()
+    pos_var = pos.var(ddof=1)
+    neg_var = neg.var(ddof=1)
 
     num = pos_mean - neg_mean
     div = np.sqrt((pos_var + neg_var) / 2.)
-    if div == 0:
+
+    # from Dan's suggestion about clipping d' values...
+    if maxv is None:
+        maxv = np.inf
+    if minv is None:
+        minv = -np.inf
+
+    dp = np.clip(num / div, minv, maxv)
+
+    if safedp and not np.isfinite(dp):
         dp = None
-    else:
-        dp = num / div
 
     return dp