From 2465a0fb43a1d83048025e292d9eddaaad5c9950 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Mon, 28 Mar 2022 15:57:39 +0530
Subject: [PATCH 01/13] keep the original dims in the log_jac_det

---
 pymc/distributions/transforms.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pymc/distributions/transforms.py b/pymc/distributions/transforms.py
index ee142b46fe..355dfd76db 100644
--- a/pymc/distributions/transforms.py
+++ b/pymc/distributions/transforms.py
@@ -87,7 +87,7 @@ def forward(self, value, *inputs):
         return y
 
     def log_jac_det(self, value, *inputs):
-        return at.sum(value[..., 1:], axis=-1)
+        return at.sum(value[..., 1:], axis=-1, keepdims=True)
 
 
 class SumTo1(RVTransform):
@@ -107,7 +107,7 @@ def forward(self, value, *inputs):
 
     def log_jac_det(self, value, *inputs):
         y = at.zeros(value.shape)
-        return at.sum(y, axis=-1)
+        return at.sum(y, axis=-1, keepdims=True)
 
 
 class CholeskyCovPacked(RVTransform):

From c9a04708e766ac0c3012bf84d659eca880cd7810 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Mon, 28 Mar 2022 17:22:59 +0530
Subject: [PATCH 02/13] added tests to test_transforms.py

---
 pymc/tests/distributions/test_transform.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/pymc/tests/distributions/test_transform.py b/pymc/tests/distributions/test_transform.py
index 850b285f4d..fa04eb86d1 100644
--- a/pymc/tests/distributions/test_transform.py
+++ b/pymc/tests/distributions/test_transform.py
@@ -598,3 +598,17 @@ def test_discrete_trafo():
         with pytest.raises(ValueError) as err:
             pm.Binomial("a", n=5, p=0.5, transform="log")
         err.match("Transformations for discrete distributions")
+def test_transforms_ordered():
+    COORDS = {"question": np.arange(10), "thresholds": np.arange(4)}
+
+    with pm.Model(coords=COORDS) as model:
+        kappa = pm.Normal(
+            "kappa",
+            mu=[-3, -1, 1, 2],
+            sigma=1,
+            dims=["question", "thresholds"],
+            transform=pm.distributions.transforms.ordered,
+        )
+
+    log_prob = model.point_logps()
+    np.testing.assert_allclose(list(log_prob.values()), np.array([18.69]))

From 0862134275797643926da4a873311d7cb9f9fa99 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Mon, 28 Mar 2022 18:19:20 +0530
Subject: [PATCH 03/13] added tests for sum_to_1 transforms

---
 pymc/tests/distributions/test_transform.py | 26 ++++++++++++++++------
 1 file changed, 19 insertions(+), 7 deletions(-)

diff --git a/pymc/tests/distributions/test_transform.py b/pymc/tests/distributions/test_transform.py
index fa04eb86d1..ae3414e31e 100644
--- a/pymc/tests/distributions/test_transform.py
+++ b/pymc/tests/distributions/test_transform.py
@@ -599,16 +599,28 @@ def test_discrete_trafo():
             pm.Binomial("a", n=5, p=0.5, transform="log")
         err.match("Transformations for discrete distributions")
 def test_transforms_ordered():
-    COORDS = {"question": np.arange(10), "thresholds": np.arange(4)}
-
-    with pm.Model(coords=COORDS) as model:
-        kappa = pm.Normal(
-            "kappa",
+    with pm.Model() as model:
+        pm.Normal(
+            "x",
             mu=[-3, -1, 1, 2],
             sigma=1,
-            dims=["question", "thresholds"],
+            size=(10, 4),
             transform=pm.distributions.transforms.ordered,
         )
-
+        
     log_prob = model.point_logps()
     np.testing.assert_allclose(list(log_prob.values()), np.array([18.69]))
+
+
+def test_transforms_sumto1():
+    with pm.Model() as model:
+        pm.Normal(
+            "x",
+            mu=[-3, -1, 1, 2],
+            sigma=1,
+            size=(10, 4),
+            transform=pm.distributions.transforms.sum_to_1,
+        )
+        
+    log_prob = model.point_logps()
+    np.testing.assert_allclose(list(log_prob.values()), np.array([-56.76]))

From 7fb4c37c344365500bdf9e8ee482206dca4446c6 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Mon, 28 Mar 2022 18:20:05 +0530
Subject: [PATCH 04/13] fix pre-commit

---
 pymc/tests/distributions/test_transform.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pymc/tests/distributions/test_transform.py b/pymc/tests/distributions/test_transform.py
index ae3414e31e..efba74de89 100644
--- a/pymc/tests/distributions/test_transform.py
+++ b/pymc/tests/distributions/test_transform.py
@@ -607,7 +607,7 @@ def test_transforms_ordered():
             size=(10, 4),
             transform=pm.distributions.transforms.ordered,
         )
-        
+
     log_prob = model.point_logps()
     np.testing.assert_allclose(list(log_prob.values()), np.array([18.69]))
 
@@ -621,6 +621,6 @@ def test_transforms_sumto1():
             size=(10, 4),
             transform=pm.distributions.transforms.sum_to_1,
         )
-        
+
     log_prob = model.point_logps()
     np.testing.assert_allclose(list(log_prob.values()), np.array([-56.76]))

From 2d2bab46e2b0bfd3a194506210202216eca4ff40 Mon Sep 17 00:00:00 2001
From: "Tim Maier (Ubuntu Desktop)" <tmaier01@students.uni-mainz.de>
Date: Wed, 26 Oct 2022 17:09:44 +0200
Subject: [PATCH 05/13] ordered transform class with n_dims_supp

---
 pymc/distributions/transforms.py           | 8 +++++++-
 pymc/tests/distributions/test_transform.py | 2 ++
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/pymc/distributions/transforms.py b/pymc/distributions/transforms.py
index 355dfd76db..f9a15f6cc9 100644
--- a/pymc/distributions/transforms.py
+++ b/pymc/distributions/transforms.py
@@ -74,6 +74,9 @@ def log_jac_det(self, value, *inputs):
 class Ordered(RVTransform):
     name = "ordered"
 
+    def __init__(self, ndim_supp=0):
+        self.ndim_supp = ndim_supp
+
     def backward(self, value, *inputs):
         x = at.zeros(value.shape)
         x = at.inc_subtensor(x[..., 0], value[..., 0])
@@ -87,7 +90,10 @@ def forward(self, value, *inputs):
         return y
 
     def log_jac_det(self, value, *inputs):
-        return at.sum(value[..., 1:], axis=-1, keepdims=True)
+        if self.ndim_supp == 0:
+            return at.sum(value[..., 1:], axis=-1, keepdims=True)
+        else:
+            return at.sum(value[..., 1:], axis=-1)
 
 
 class SumTo1(RVTransform):
diff --git a/pymc/tests/distributions/test_transform.py b/pymc/tests/distributions/test_transform.py
index efba74de89..b6aec0e384 100644
--- a/pymc/tests/distributions/test_transform.py
+++ b/pymc/tests/distributions/test_transform.py
@@ -598,6 +598,8 @@ def test_discrete_trafo():
         with pytest.raises(ValueError) as err:
             pm.Binomial("a", n=5, p=0.5, transform="log")
         err.match("Transformations for discrete distributions")
+
+
 def test_transforms_ordered():
     with pm.Model() as model:
         pm.Normal(

From 0372528dc45bf61b452c677bbc35861fe97e7427 Mon Sep 17 00:00:00 2001
From: "Tim Maier (Ubuntu Desktop)" <tmaier01@students.uni-mainz.de>
Date: Fri, 28 Oct 2022 11:54:30 +0200
Subject: [PATCH 06/13] univariate_ordered and multivariate_ordered

---
 pymc/distributions/transforms.py           | 13 ++++++++++---
 pymc/tests/distributions/test_transform.py |  8 ++++++--
 2 files changed, 16 insertions(+), 5 deletions(-)

diff --git a/pymc/distributions/transforms.py b/pymc/distributions/transforms.py
index f9a15f6cc9..f6131a0182 100644
--- a/pymc/distributions/transforms.py
+++ b/pymc/distributions/transforms.py
@@ -336,10 +336,17 @@ def extend_axis_rev(array, axis):
 Instantiation of :class:`pymc.distributions.transforms.LogExpM1`
 for use in the ``transform`` argument of a random variable."""
 
-ordered = Ordered()
-ordered.__doc__ = """
+univariate_ordered = Ordered(ndim_supp=0)
+univariate_ordered.__doc__ = """
 Instantiation of :class:`pymc.distributions.transforms.Ordered`
-for use in the ``transform`` argument of a random variable."""
+for use in the ``transform`` argument of a univariate random variable."""
+
+multivariate_ordered = Ordered(ndim_supp=1)
+multivariate_ordered.__doc__ = """
+Instantiation of :class:`pymc.distributions.transforms.Ordered`
+for use in the ``transform`` argument of a multivariate random variable."""
+
+ordered = univariate_ordered
 
 log = LogTransform()
 log.__doc__ = """
diff --git a/pymc/tests/distributions/test_transform.py b/pymc/tests/distributions/test_transform.py
index b6aec0e384..99a58c45c0 100644
--- a/pymc/tests/distributions/test_transform.py
+++ b/pymc/tests/distributions/test_transform.py
@@ -327,7 +327,7 @@ def check_vectortransform_elementwise_logp(self, model):
         jacob_det = transform.log_jac_det(test_array_transf, *x.owner.inputs)
         # Original distribution is univariate
         if x.owner.op.ndim_supp == 0:
-            assert model.logp(x, sum=False)[0].ndim == x.ndim == (jacob_det.ndim + 1)
+            assert model.logp(x, sum=False)[0].ndim == x.ndim == jacob_det.ndim
         # Original distribution is multivariate
         else:
             assert model.logp(x, sum=False)[0].ndim == (x.ndim - 1) == jacob_det.ndim
@@ -569,7 +569,11 @@ def test_uniform_other(self, lower, upper, size, transform):
     def test_mvnormal_ordered(self, mu, cov, size, shape):
         initval = np.sort(np.random.randn(*shape))
         model = self.build_model(
-            pm.MvNormal, {"mu": mu, "cov": cov}, size=size, initval=initval, transform=tr.ordered
+            pm.MvNormal,
+            {"mu": mu, "cov": cov},
+            size=size,
+            initval=initval,
+            transform=tr.multivariate_ordered,
         )
         self.check_vectortransform_elementwise_logp(model)
 

From 7ef9b2e9ab91f1c66ff89dcdeed43cd4f49be013 Mon Sep 17 00:00:00 2001
From: "Tim Maier (Ubuntu Desktop)" <tmaier01@students.uni-mainz.de>
Date: Sun, 30 Oct 2022 10:28:57 +0100
Subject: [PATCH 07/13] ndim_supp cases in sumto1 transform

---
 pymc/distributions/transforms.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/pymc/distributions/transforms.py b/pymc/distributions/transforms.py
index f6131a0182..6a308469b4 100644
--- a/pymc/distributions/transforms.py
+++ b/pymc/distributions/transforms.py
@@ -104,6 +104,9 @@ class SumTo1(RVTransform):
 
     name = "sumto1"
 
+    def __init__(self, ndim_supp=0):
+        self.ndim_supp = ndim_supp
+
     def backward(self, value, *inputs):
         remaining = 1 - at.sum(value[..., :], axis=-1, keepdims=True)
         return at.concatenate([value[..., :], remaining], axis=-1)
@@ -113,7 +116,10 @@ def forward(self, value, *inputs):
 
     def log_jac_det(self, value, *inputs):
         y = at.zeros(value.shape)
-        return at.sum(y, axis=-1, keepdims=True)
+        if self.ndim_supp == 0:
+            return at.sum(y, axis=-1, keepdims=True)
+        else:
+            return at.sum(y, axis=-1)
 
 
 class CholeskyCovPacked(RVTransform):

From 9d279db3ac2516e189e225b8d0159190fd8bd400 Mon Sep 17 00:00:00 2001
From: "Tim Maier (Ubuntu Desktop)" <tmaier01@students.uni-mainz.de>
Date: Mon, 31 Oct 2022 12:24:41 +0100
Subject: [PATCH 08/13] keep transforms.ordered for backward compatibility

---
 pymc/distributions/transforms.py           | 18 ++++++++++++++++--
 pymc/tests/distributions/test_transform.py | 10 +++++-----
 2 files changed, 21 insertions(+), 7 deletions(-)

diff --git a/pymc/distributions/transforms.py b/pymc/distributions/transforms.py
index 6a308469b4..cf000f4c79 100644
--- a/pymc/distributions/transforms.py
+++ b/pymc/distributions/transforms.py
@@ -352,14 +352,28 @@ def extend_axis_rev(array, axis):
 Instantiation of :class:`pymc.distributions.transforms.Ordered`
 for use in the ``transform`` argument of a multivariate random variable."""
 
-ordered = univariate_ordered
+ordered = Ordered(ndim_supp=1)
+ordered.__doc__ = """
+Instantiation of :class:`pymc.distributions.transforms.Ordered`
+for use in the ``transform`` argument. """
+
 
 log = LogTransform()
 log.__doc__ = """
 Instantiation of :class:`aeppl.transforms.LogTransform`
 for use in the ``transform`` argument of a random variable."""
 
-sum_to_1 = SumTo1()
+univariate_sum_to_1 = SumTo1(ndim_supp=0)
+univariate_sum_to_1.__doc__ = """
+Instantiation of :class:`pymc.distributions.transforms.SumTo1`
+for use in the ``transform`` argument of a univariate random variable."""
+
+multivariate_sum_to_1 = SumTo1(ndim_supp=1)
+multivariate_sum_to_1.__doc__ = """
+Instantiation of :class:`pymc.distributions.transforms.SumTo1`
+for use in the ``transform`` argument of a multivariate random variable."""
+
+sum_to_1 = SumTo1(ndim_supp=1)
 sum_to_1.__doc__ = """
 Instantiation of :class:`pymc.distributions.transforms.SumTo1`
 for use in the ``transform`` argument of a random variable."""
diff --git a/pymc/tests/distributions/test_transform.py b/pymc/tests/distributions/test_transform.py
index 99a58c45c0..54c9e695d5 100644
--- a/pymc/tests/distributions/test_transform.py
+++ b/pymc/tests/distributions/test_transform.py
@@ -327,7 +327,7 @@ def check_vectortransform_elementwise_logp(self, model):
         jacob_det = transform.log_jac_det(test_array_transf, *x.owner.inputs)
         # Original distribution is univariate
         if x.owner.op.ndim_supp == 0:
-            assert model.logp(x, sum=False)[0].ndim == x.ndim == jacob_det.ndim
+            assert model.logp(x, sum=False)[0].ndim == x.ndim == (jacob_det.ndim + 1)
         # Original distribution is multivariate
         else:
             assert model.logp(x, sum=False)[0].ndim == (x.ndim - 1) == jacob_det.ndim
@@ -573,7 +573,7 @@ def test_mvnormal_ordered(self, mu, cov, size, shape):
             {"mu": mu, "cov": cov},
             size=size,
             initval=initval,
-            transform=tr.multivariate_ordered,
+            transform=tr.ordered,
         )
         self.check_vectortransform_elementwise_logp(model)
 
@@ -607,11 +607,11 @@ def test_discrete_trafo():
 def test_transforms_ordered():
     with pm.Model() as model:
         pm.Normal(
-            "x",
+            "x_univariate",
             mu=[-3, -1, 1, 2],
             sigma=1,
             size=(10, 4),
-            transform=pm.distributions.transforms.ordered,
+            transform=tr.univariate_ordered,
         )
 
     log_prob = model.point_logps()
@@ -625,7 +625,7 @@ def test_transforms_sumto1():
             mu=[-3, -1, 1, 2],
             sigma=1,
             size=(10, 4),
-            transform=pm.distributions.transforms.sum_to_1,
+            transform=tr.univariate_sum_to_1,
         )
 
     log_prob = model.point_logps()

From 7515c73201590eea77ca3fd55ee449cdb7b3e95a Mon Sep 17 00:00:00 2001
From: "Tim Maier (Ubuntu Desktop)" <tmaier01@students.uni-mainz.de>
Date: Sat, 19 Nov 2022 21:32:02 +0100
Subject: [PATCH 09/13] pre-commit run

---
 pymc/distributions/transforms.py           | 20 ++++-----
 pymc/tests/distributions/test_transform.py | 49 ++++++++++++++--------
 2 files changed, 41 insertions(+), 28 deletions(-)

diff --git a/pymc/distributions/transforms.py b/pymc/distributions/transforms.py
index cf000f4c79..6fd727358b 100644
--- a/pymc/distributions/transforms.py
+++ b/pymc/distributions/transforms.py
@@ -75,6 +75,11 @@ class Ordered(RVTransform):
     name = "ordered"
 
     def __init__(self, ndim_supp=0):
+        if ndim_supp > 1:
+            raise ValueError(
+                f"For Ordered transformation number of core dimensions"
+                f"(ndim_supp) must not exceed 1 but is {ndim_supp}"
+            )
         self.ndim_supp = ndim_supp
 
     def backward(self, value, *inputs):
@@ -105,6 +110,11 @@ class SumTo1(RVTransform):
     name = "sumto1"
 
     def __init__(self, ndim_supp=0):
+        if ndim_supp > 1:
+            raise ValueError(
+                f"For SumTo1 transformation number of core dimensions"
+                f"(ndim_supp) must not exceed 1 but is {ndim_supp}"
+            )
         self.ndim_supp = ndim_supp
 
     def backward(self, value, *inputs):
@@ -352,11 +362,6 @@ def extend_axis_rev(array, axis):
 Instantiation of :class:`pymc.distributions.transforms.Ordered`
 for use in the ``transform`` argument of a multivariate random variable."""
 
-ordered = Ordered(ndim_supp=1)
-ordered.__doc__ = """
-Instantiation of :class:`pymc.distributions.transforms.Ordered`
-for use in the ``transform`` argument. """
-
 
 log = LogTransform()
 log.__doc__ = """
@@ -373,11 +378,6 @@ def extend_axis_rev(array, axis):
 Instantiation of :class:`pymc.distributions.transforms.SumTo1`
 for use in the ``transform`` argument of a multivariate random variable."""
 
-sum_to_1 = SumTo1(ndim_supp=1)
-sum_to_1.__doc__ = """
-Instantiation of :class:`pymc.distributions.transforms.SumTo1`
-for use in the ``transform`` argument of a random variable."""
-
 circular = CircularTransform()
 circular.__doc__ = """
 Instantiation of :class:`aeppl.transforms.CircularTransform`
diff --git a/pymc/tests/distributions/test_transform.py b/pymc/tests/distributions/test_transform.py
index 54c9e695d5..59db3f398b 100644
--- a/pymc/tests/distributions/test_transform.py
+++ b/pymc/tests/distributions/test_transform.py
@@ -13,6 +13,8 @@
 #   limitations under the License.
 
 
+from typing import Union
+
 import aesara
 import aesara.tensor as at
 import numpy as np
@@ -139,10 +141,12 @@ def test_simplex_accuracy():
 
 
 def test_sum_to_1():
-    check_vector_transform(tr.sum_to_1, Simplex(2))
-    check_vector_transform(tr.sum_to_1, Simplex(4))
+    check_vector_transform(tr.univariate_sum_to_1, Simplex(2))
+    check_vector_transform(tr.univariate_sum_to_1, Simplex(4))
 
-    check_jacobian_det(tr.sum_to_1, Vector(Unit, 2), at.dvector, np.array([0, 0]), lambda x: x[:-1])
+    check_jacobian_det(
+        tr.univariate_sum_to_1, Vector(Unit, 2), at.dvector, np.array([0, 0]), lambda x: x[:-1]
+    )
 
 
 def test_log():
@@ -241,28 +245,30 @@ def test_circular():
 
 
 def test_ordered():
-    check_vector_transform(tr.ordered, SortedVector(6))
+    check_vector_transform(tr.univariate_ordered, SortedVector(6))
 
-    check_jacobian_det(tr.ordered, Vector(R, 2), at.dvector, np.array([0, 0]), elemwise=False)
+    check_jacobian_det(
+        tr.univariate_ordered, Vector(R, 2), at.dvector, np.array([0, 0]), elemwise=False
+    )
 
-    vals = get_values(tr.ordered, Vector(R, 3), at.dvector, np.zeros(3))
+    vals = get_values(tr.univariate_ordered, Vector(R, 3), at.dvector, np.zeros(3))
     close_to_logical(np.diff(vals) >= 0, True, tol)
 
 
 def test_chain_values():
-    chain_tranf = tr.Chain([tr.logodds, tr.ordered])
+    chain_tranf = tr.Chain([tr.logodds, tr.univariate_ordered])
     vals = get_values(chain_tranf, Vector(R, 5), at.dvector, np.zeros(5))
     close_to_logical(np.diff(vals) >= 0, True, tol)
 
 
 def test_chain_vector_transform():
-    chain_tranf = tr.Chain([tr.logodds, tr.ordered])
+    chain_tranf = tr.Chain([tr.logodds, tr.univariate_ordered])
     check_vector_transform(chain_tranf, UnitSortedVector(3))
 
 
 @pytest.mark.xfail(reason="Fails due to precision issue. Values just close to expected.")
 def test_chain_jacob_det():
-    chain_tranf = tr.Chain([tr.logodds, tr.ordered])
+    chain_tranf = tr.Chain([tr.logodds, tr.univariate_ordered])
     check_jacobian_det(chain_tranf, Vector(R, 4), at.dvector, np.zeros(4), elemwise=False)
 
 
@@ -327,7 +333,14 @@ def check_vectortransform_elementwise_logp(self, model):
         jacob_det = transform.log_jac_det(test_array_transf, *x.owner.inputs)
         # Original distribution is univariate
         if x.owner.op.ndim_supp == 0:
-            assert model.logp(x, sum=False)[0].ndim == x.ndim == (jacob_det.ndim + 1)
+            tr_steps = getattr(transform, "transform_list", [transform])
+            transform_keeps_dim = any(
+                [isinstance(ts, Union[tr.SumTo1, tr.Ordered]) for ts in tr_steps]
+            )
+            if transform_keeps_dim:
+                assert model.logp(x, sum=False)[0].ndim == x.ndim == jacob_det.ndim
+            else:
+                assert model.logp(x, sum=False)[0].ndim == x.ndim == (jacob_det.ndim + 1)
         # Original distribution is multivariate
         else:
             assert model.logp(x, sum=False)[0].ndim == (x.ndim - 1) == jacob_det.ndim
@@ -449,7 +462,7 @@ def test_normal_ordered(self):
             {"mu": 0.0, "sigma": 1.0},
             size=3,
             initval=np.asarray([-1.0, 1.0, 4.0]),
-            transform=tr.ordered,
+            transform=tr.univariate_ordered,
         )
         self.check_vectortransform_elementwise_logp(model)
 
@@ -467,7 +480,7 @@ def test_half_normal_ordered(self, sigma, size):
             {"sigma": sigma},
             size=size,
             initval=initval,
-            transform=tr.Chain([tr.log, tr.ordered]),
+            transform=tr.Chain([tr.log, tr.univariate_ordered]),
         )
         self.check_vectortransform_elementwise_logp(model)
 
@@ -479,7 +492,7 @@ def test_exponential_ordered(self, lam, size):
             {"lam": lam},
             size=size,
             initval=initval,
-            transform=tr.Chain([tr.log, tr.ordered]),
+            transform=tr.Chain([tr.log, tr.univariate_ordered]),
         )
         self.check_vectortransform_elementwise_logp(model)
 
@@ -501,7 +514,7 @@ def test_beta_ordered(self, a, b, size):
             {"alpha": a, "beta": b},
             size=size,
             initval=initval,
-            transform=tr.Chain([tr.logodds, tr.ordered]),
+            transform=tr.Chain([tr.logodds, tr.univariate_ordered]),
         )
         self.check_vectortransform_elementwise_logp(model)
 
@@ -524,7 +537,7 @@ def transform_params(*inputs):
             {"lower": lower, "upper": upper},
             size=size,
             initval=initval,
-            transform=tr.Chain([interval, tr.ordered]),
+            transform=tr.Chain([interval, tr.univariate_ordered]),
         )
         self.check_vectortransform_elementwise_logp(model)
 
@@ -536,7 +549,7 @@ def test_vonmises_ordered(self, mu, kappa, size):
             {"mu": mu, "kappa": kappa},
             size=size,
             initval=initval,
-            transform=tr.Chain([tr.circular, tr.ordered]),
+            transform=tr.Chain([tr.circular, tr.univariate_ordered]),
         )
         self.check_vectortransform_elementwise_logp(model)
 
@@ -545,7 +558,7 @@ def test_vonmises_ordered(self, mu, kappa, size):
         [
             (0.0, 1.0, (2,), tr.simplex),
             (0.5, 5.5, (2, 3), tr.simplex),
-            (np.zeros(3), np.ones(3), (4, 3), tr.Chain([tr.sum_to_1, tr.logodds])),
+            (np.zeros(3), np.ones(3), (4, 3), tr.Chain([tr.univariate_sum_to_1, tr.logodds])),
         ],
     )
     def test_uniform_other(self, lower, upper, size, transform):
@@ -573,7 +586,7 @@ def test_mvnormal_ordered(self, mu, cov, size, shape):
             {"mu": mu, "cov": cov},
             size=size,
             initval=initval,
-            transform=tr.ordered,
+            transform=tr.multivariate_ordered,
         )
         self.check_vectortransform_elementwise_logp(model)
 

From 744247457ae025641415273d7f494c1e17b23a75 Mon Sep 17 00:00:00 2001
From: "Tim Maier (Ubuntu Desktop)" <tmaier01@students.uni-mainz.de>
Date: Sat, 19 Nov 2022 22:50:02 +0100
Subject: [PATCH 10/13] updated `__all__` variable 1. updated variable
 `__all__` to contain    `univariate_ordered`,`multivariate_ordered` and   
 analogous `sum_to_1` instantiations.

---
 pymc/distributions/transforms.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/pymc/distributions/transforms.py b/pymc/distributions/transforms.py
index 6fd727358b..394a5d0dd6 100644
--- a/pymc/distributions/transforms.py
+++ b/pymc/distributions/transforms.py
@@ -37,9 +37,11 @@
     "logodds",
     "Interval",
     "log_exp_m1",
-    "ordered",
+    "univariate_ordered",
+    "multivariate_ordered",
     "log",
-    "sum_to_1",
+    "univariate_sum_to_1",
+    "multivariate_sum_to_1",
     "circular",
     "CholeskyCovPacked",
     "Chain",

From bc8166e245744f0e0c58c73814edc27a8cb636e7 Mon Sep 17 00:00:00 2001
From: "Tim Maier (Ubuntu Desktop)" <tmaier01@students.uni-mainz.de>
Date: Sun, 20 Nov 2022 18:51:17 +0100
Subject: [PATCH 11/13] Tests for uni- and multivariate `SumTo1, Ordered` 1.
 Old `transforms.ordered` and `transforms.sum_to_1` instantiations    were
 added again for backwards compatibility. 2. Tests for multivariate and
 univariate usage of `SumTo1` and `Ordered`    transformations were added.

---
 pymc/distributions/transforms.py           | 13 ++++
 pymc/tests/distributions/test_transform.py | 90 +++++++++++++++++++---
 2 files changed, 93 insertions(+), 10 deletions(-)

diff --git a/pymc/distributions/transforms.py b/pymc/distributions/transforms.py
index 394a5d0dd6..9852d0f3f2 100644
--- a/pymc/distributions/transforms.py
+++ b/pymc/distributions/transforms.py
@@ -37,9 +37,11 @@
     "logodds",
     "Interval",
     "log_exp_m1",
+    "ordered",
     "univariate_ordered",
     "multivariate_ordered",
     "log",
+    "sum_to_1",
     "univariate_sum_to_1",
     "multivariate_sum_to_1",
     "circular",
@@ -364,6 +366,11 @@ def extend_axis_rev(array, axis):
 Instantiation of :class:`pymc.distributions.transforms.Ordered`
 for use in the ``transform`` argument of a multivariate random variable."""
 
+# backwards compatibility
+ordered = Ordered(ndim_supp=1)
+ordered.__doc__ = """
+Instantiation of :class:`pymc.distributions.transforms.Ordered`
+for use in the ``transform`` argument. """
 
 log = LogTransform()
 log.__doc__ = """
@@ -380,6 +387,12 @@ def extend_axis_rev(array, axis):
 Instantiation of :class:`pymc.distributions.transforms.SumTo1`
 for use in the ``transform`` argument of a multivariate random variable."""
 
+# backwards compatibility
+sum_to_1 = SumTo1(ndim_supp=1)
+sum_to_1.__doc__ = """
+Instantiation of :class:`pymc.distributions.transforms.SumTo1`
+for use in the ``transform`` argument of a random variable."""
+
 circular = CircularTransform()
 circular.__doc__ = """
 Instantiation of :class:`aeppl.transforms.CircularTransform`
diff --git a/pymc/tests/distributions/test_transform.py b/pymc/tests/distributions/test_transform.py
index 59db3f398b..eff0223184 100644
--- a/pymc/tests/distributions/test_transform.py
+++ b/pymc/tests/distributions/test_transform.py
@@ -147,6 +147,9 @@ def test_sum_to_1():
     check_jacobian_det(
         tr.univariate_sum_to_1, Vector(Unit, 2), at.dvector, np.array([0, 0]), lambda x: x[:-1]
     )
+    check_jacobian_det(
+        tr.multivariate_sum_to_1, Vector(Unit, 2), at.dvector, np.array([0, 0]), lambda x: x[:-1]
+    )
 
 
 def test_log():
@@ -250,6 +253,9 @@ def test_ordered():
     check_jacobian_det(
         tr.univariate_ordered, Vector(R, 2), at.dvector, np.array([0, 0]), elemwise=False
     )
+    check_jacobian_det(
+        tr.multivariate_ordered, Vector(R, 2), at.dvector, np.array([0, 0]), elemwise=False
+    )
 
     vals = get_values(tr.univariate_ordered, Vector(R, 3), at.dvector, np.zeros(3))
     close_to_logical(np.diff(vals) >= 0, True, tol)
@@ -617,29 +623,93 @@ def test_discrete_trafo():
         err.match("Transformations for discrete distributions")
 
 
-def test_transforms_ordered():
+def test_2d_univariate_ordered():
     with pm.Model() as model:
-        pm.Normal(
-            "x_univariate",
+        x_1d = pm.Normal(
+            "x_1d",
+            mu=[-3, -1, 1, 2],
+            sigma=1,
+            size=(4,),
+            transform=tr.univariate_ordered,
+        )
+        x_2d = pm.Normal(
+            "x_2d",
             mu=[-3, -1, 1, 2],
             sigma=1,
             size=(10, 4),
             transform=tr.univariate_ordered,
         )
 
-    log_prob = model.point_logps()
-    np.testing.assert_allclose(list(log_prob.values()), np.array([18.69]))
+    log_p = model.compile_logp(sum=False)(
+        {"x_1d_ordered__": np.zeros((4,)), "x_2d_ordered__": np.zeros((10, 4))}
+    )
+    np.testing.assert_allclose(np.tile(log_p[0], (10, 1)), log_p[1])
+
+
+def test_2d_multivariate_ordered():
+    with pm.Model() as model:
+        x_1d = pm.MvNormal(
+            "x_1d",
+            mu=[-1, 1],
+            cov=np.eye(2),
+            initval=[-1, 1],
+            transform=tr.multivariate_ordered,
+        )
+        x_2d = pm.MvNormal(
+            "x_2d",
+            mu=[-1, 1],
+            cov=np.eye(2),
+            size=2,
+            initval=[[-1, 1], [-1, 1]],
+            transform=tr.multivariate_ordered,
+        )
+
+    log_p = model.compile_logp(sum=False)(
+        {"x_1d_ordered__": np.zeros((2,)), "x_2d_ordered__": np.zeros((2, 2))}
+    )
+    np.testing.assert_allclose(log_p[0], log_p[1])
 
 
-def test_transforms_sumto1():
+def test_2d_univariate_sum_to_1():
     with pm.Model() as model:
-        pm.Normal(
-            "x",
+        x_1d = pm.Normal(
+            "x_1d",
+            mu=[-3, -1, 1, 2],
+            sigma=1,
+            size=(4,),
+            transform=tr.univariate_sum_to_1,
+        )
+        x_2d = pm.Normal(
+            "x_2d",
             mu=[-3, -1, 1, 2],
             sigma=1,
             size=(10, 4),
             transform=tr.univariate_sum_to_1,
         )
 
-    log_prob = model.point_logps()
-    np.testing.assert_allclose(list(log_prob.values()), np.array([-56.76]))
+    log_p = model.compile_logp(sum=False)(
+        {"x_1d_sumto1__": np.zeros(3), "x_2d_sumto1__": np.zeros((10, 3))}
+    )
+    np.testing.assert_allclose(np.tile(log_p[0], (10, 1)), log_p[1])
+
+
+def test_2d_multivariate_sum_to_1():
+    with pm.Model() as model:
+        x_1d = pm.MvNormal(
+            "x_1d",
+            mu=[-1, 1],
+            cov=np.eye(2),
+            transform=tr.multivariate_sum_to_1,
+        )
+        x_2d = pm.MvNormal(
+            "x_2d",
+            mu=[-1, 1],
+            cov=np.eye(2),
+            size=2,
+            transform=tr.multivariate_sum_to_1,
+        )
+
+    log_p = model.compile_logp(sum=False)(
+        {"x_1d_sumto1__": np.zeros(1), "x_2d_sumto1__": np.zeros((2, 1))}
+    )
+    np.testing.assert_allclose(log_p[0], log_p[1])

From 8ad2c03eedfade45e3544ab18fbcff7ab479f9cd Mon Sep 17 00:00:00 2001
From: Tim Maier <tmaier01@students.uni-mainz.de>
Date: Sun, 20 Nov 2022 22:10:31 +0100
Subject: [PATCH 12/13] test `Ordered` and `SumTo1` raise `ValueError`

---
 pymc/tests/distributions/test_transform.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/pymc/tests/distributions/test_transform.py b/pymc/tests/distributions/test_transform.py
index eff0223184..5bed2a9a48 100644
--- a/pymc/tests/distributions/test_transform.py
+++ b/pymc/tests/distributions/test_transform.py
@@ -144,6 +144,9 @@ def test_sum_to_1():
     check_vector_transform(tr.univariate_sum_to_1, Simplex(2))
     check_vector_transform(tr.univariate_sum_to_1, Simplex(4))
 
+    with pytest.raises(ValueError, match=r"\(ndim_supp\) must not exceed 1"):
+        tr.SumTo1(2)
+
     check_jacobian_det(
         tr.univariate_sum_to_1, Vector(Unit, 2), at.dvector, np.array([0, 0]), lambda x: x[:-1]
     )
@@ -250,6 +253,9 @@ def test_circular():
 def test_ordered():
     check_vector_transform(tr.univariate_ordered, SortedVector(6))
 
+    with pytest.raises(ValueError, match=r"\(ndim_supp\) must not exceed 1"):
+        tr.Ordered(2)
+
     check_jacobian_det(
         tr.univariate_ordered, Vector(R, 2), at.dvector, np.array([0, 0]), elemwise=False
     )

From 9daae3536efa065be3ab3d57dfb421d58cd3da36 Mon Sep 17 00:00:00 2001
From: "Tim Maier (Ubuntu Desktop)" <tmaier01@students.uni-mainz.de>
Date: Mon, 21 Nov 2022 10:02:11 +0100
Subject: [PATCH 13/13] improved docs for `SumTo1, Ordered` instantiations

---
 pymc/distributions/transforms.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/pymc/distributions/transforms.py b/pymc/distributions/transforms.py
index 9852d0f3f2..2f3d7cb626 100644
--- a/pymc/distributions/transforms.py
+++ b/pymc/distributions/transforms.py
@@ -370,7 +370,9 @@ def extend_axis_rev(array, axis):
 ordered = Ordered(ndim_supp=1)
 ordered.__doc__ = """
 Instantiation of :class:`pymc.distributions.transforms.Ordered`
-for use in the ``transform`` argument. """
+for use in the ``transform`` argument of a random variable.
+This instantiation is for backwards compatibility only.
+Please use `univariate_ordererd` or `multivariate_ordered` instead."""
 
 log = LogTransform()
 log.__doc__ = """
@@ -391,7 +393,9 @@ def extend_axis_rev(array, axis):
 sum_to_1 = SumTo1(ndim_supp=1)
 sum_to_1.__doc__ = """
 Instantiation of :class:`pymc.distributions.transforms.SumTo1`
-for use in the ``transform`` argument of a random variable."""
+for use in the ``transform`` argument of a random variable.
+This instantiation is for backwards compatibility only.
+Please use `univariate_sum_to_1` or `multivariate_sum_to_1` instead."""
 
 circular = CircularTransform()
 circular.__doc__ = """