Merge branch 'main' into logs-trunc

docs/api/crps.md

@@ -63,6 +63,8 @@ also be viewed as the Brier score integrated over all real-valued thresholds.
 
 ::: scoringrules.crps_normal
 
+::: scoringrules.crps_2pnormal
+
 ::: scoringrules.crps_poisson
 
 ::: scoringrules.crps_t

docs/api/logarithmic.md

@@ -1,9 +1,29 @@
 # Logarithmic Score
 
+::: scoringrules.logs_binomial
+
+::: scoringrules.logs_exponential
+
+::: scoringrules.logs_gamma
+
+::: scoringrules.logs_hypergeometric
+
+::: scoringrules.logs_logistic
+
+::: scoringrules.logs_loglogistic
+
+::: scoringrules.logs_lognormal
+
+::: scoringrules.logs_normal
+
+::: scoringrules.logs_poisson
+
+::: scoringrules.logs_t
+
 ::: scoringrules.logs_tlogistic
 
 ::: scoringrules.logs_tnormal
 
 ::: scoringrules.logs_tt
 
-::: scoringrules.logs_normal
+::: scoringrules.logs_uniform

scoringrules/__init__.py

@@ -28,6 +28,7 @@
     crps_lognormal,
     crps_negbinom,
     crps_normal,
+    crps_2pnormal,
     crps_poisson,
     crps_t,
     crps_uniform,
@@ -85,6 +86,7 @@
     "crps_lognormal",
     "crps_negbinom",
     "crps_normal",
+    "crps_2pnormal",
     "crps_poisson",
     "crps_quantile",
     "crps_t",

scoringrules/_crps.py

@@ -1621,12 +1621,75 @@ def crps_normal(
 
     Examples
     --------
-    >>> from scoringrules import crps
-    >>> crps.normal(0.1, 0.4, 0.0)
+    >>> import scoringrules as sr
+    >>> sr.crps_normal(0.0, 0.1, 0.4)
     """
     return crps.normal(observation, mu, sigma, backend=backend)
 
 
+def crps_2pnormal(
+    observation: "ArrayLike",
+    scale1: "ArrayLike",
+    scale2: "ArrayLike",
+    location: "ArrayLike",
+    /,
+    *,
+    backend: "Backend" = None,
+) -> "ArrayLike":
+    r"""Compute the closed form of the CRPS for the two-piece normal distribution.
+
+    It is based on the following relationship given in
+    [Jordan et al. (2019)](https://www.jstatsoft.org/article/view/v090i12):
+
+    $$ \mathrm{CRPS}(F_{\sigma_{1}, \sigma_{2}, \mu}, y) =
+    \sigma_{1} \mathrm{CRPS} \left( F_{-\infty,0}^{0, \sigma_{2}/(\sigma_{1} + \sigma_{2})}, \frac{\min(0, y - \mu)}{\sigma_{1}} \right) +
+    \sigma_{2} \mathrm{CRPS} \left( F_{0, \sigma_{1}/(\sigma_{1} + \sigma_{2})}^{\infty, 0}, \frac{\min(0, y - \mu)}{\sigma_{2}} \right), $$
+
+    where $F_{\sigma_{1}, \sigma_{2}, \mu}$ is the two-piece normal distribution with
+    scale1 and scale2 parameters $\sigma_{1}, \sigma_{2} > 0$ and location parameter $\mu$,
+    and $F_{l, L}^{u, U}$ is the CDF of the generalised truncated and censored normal distribution.
+
+    Parameters
+    ----------
+    observations: ArrayLike
+        The observed values.
+    scale1: ArrayLike
+        Scale parameter of the lower half of the forecast two-piece normal distribution.
+    scale2: ArrayLike
+        Scale parameter of the upper half of the forecast two-piece normal distribution.
+    mu: ArrayLike
+        Location parameter of the forecast two-piece normal distribution.
+
+    Returns
+    -------
+    crps: array_like
+        The CRPS between 2pNormal(scale1, scale2, mu) and obs.
+
+    Examples
+    --------
+    >>> import scoringrules as sr
+    >>> sr.crps_2pnormal(0.0, 0.4, 2.0, 0.1)
+    """
+    B = backends.active if backend is None else backends[backend]
+    lower = float("-inf")
+    upper = 0.0
+    lmass = 0.0
+    umass = scale2 / (scale1 + scale2)
+    z = B.minimum(B.asarray(0.0), B.asarray(observation - location)) / scale1
+    s1 = scale1 * crps.gtcnormal(
+        z, 0.0, 1.0, lower, upper, lmass, umass, backend=backend
+    )
+    lower = 0.0
+    upper = float("inf")
+    lmass = scale1 / (scale1 + scale2)
+    umass = 0.0
+    z = B.maximum(B.asarray(0.0), B.asarray(observation - location)) / scale2
+    s2 = scale2 * crps.gtcnormal(
+        z, 0.0, 1.0, lower, upper, lmass, umass, backend=backend
+    )
+    return s1 + s2
+
+
 def crps_poisson(
     observation: "ArrayLike",
     mean: "ArrayLike",
@@ -1649,7 +1712,7 @@ def crps_poisson(
     observation: ArrayLike
         The observed values.
     mean: ArrayLike
-        Mean parameter of the forecast exponential distribution.
+        Mean parameter of the forecast poisson distribution.
 
     Returns
     -------