diff --git a/packages/essdiffraction/docs/api-reference/index.md b/packages/essdiffraction/docs/api-reference/index.md
index dbc995c8b..c93339e61 100644
--- a/packages/essdiffraction/docs/api-reference/index.md
+++ b/packages/essdiffraction/docs/api-reference/index.md
@@ -1,8 +1,25 @@
 # API Reference
 
+## ESSdiffraction
+
+### Submodules
+
+```{eval-rst}
+.. currentmodule:: ess.diffraction
+
+.. autosummary::
+   :toctree: ../generated/modules
+   :template: module-template.rst
+   :recursive:
+
+   pdf
+   peaks
+```
+
 ## ESSpowder
 
 ### Module Attributes
+
 ```{eval-rst}
 .. currentmodule:: ess.powder
 
@@ -37,9 +54,7 @@
    grouping
    logging
    masking
-   peaks
    smoothing
-   transform
    types
 ```
 
@@ -47,7 +62,6 @@
 
 ### Workflows
 
-
 ```{eval-rst}
 .. currentmodule:: ess.dream
 
@@ -103,7 +117,6 @@
 
 ### Workflows
 
-
 ```{eval-rst}
 .. currentmodule:: ess.beer
 
@@ -144,7 +157,6 @@
 
 ### Workflows
 
-
 ```{eval-rst}
 .. currentmodule:: ess.magic
 
diff --git a/packages/essdiffraction/docs/conf.py b/packages/essdiffraction/docs/conf.py
index 29988706d..f40fa0c77 100644
--- a/packages/essdiffraction/docs/conf.py
+++ b/packages/essdiffraction/docs/conf.py
@@ -249,8 +249,12 @@
 # build already tests if those plots can be made.
 # So we simply disable plots in doctests.
 doctest_global_setup = """
+# Private import because of https://github.com/mctools/ncrystal/issues/361
+from NCrystal._common import set_ncrystal_print_fct
 import numpy as np
 
+set_ncrystal_print_fct(lambda *args, **kwargs: None)
+
 try:
     import scipp as sc
 
diff --git a/packages/essdiffraction/src/ess/diffraction/__init__.py b/packages/essdiffraction/src/ess/diffraction/__init__.py
index d5c25d527..76981bcd7 100644
--- a/packages/essdiffraction/src/ess/diffraction/__init__.py
+++ b/packages/essdiffraction/src/ess/diffraction/__init__.py
@@ -7,9 +7,13 @@
 
 import importlib.metadata
 
+from . import pdf
+
 try:
     __version__ = importlib.metadata.version("essdiffraction")
 except importlib.metadata.PackageNotFoundError:
     __version__ = "0.0.0"
 
 del importlib
+
+__all__ = ["pdf"]
diff --git a/packages/essdiffraction/src/ess/diffraction/pdf.py b/packages/essdiffraction/src/ess/diffraction/pdf.py
new file mode 100644
index 000000000..facda0cf3
--- /dev/null
+++ b/packages/essdiffraction/src/ess/diffraction/pdf.py
@@ -0,0 +1,276 @@
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright (c) 2026 Scipp contributors (https://github.com/scipp)
+
+r"""Pair distribution functions and related functions.
+
+Here, we use the following definitions and naming convention:
+
+.. list-table::
+   :header-rows: 1
+
+   * - Name
+     - Definition
+   * - :func:`Pair correlation function <pair_correlation_function>`
+     - :math:`G(r) = \frac{2}{\pi} \int_o^\infty\, Q (S(Q) - 1) \sin(Qr) \text{d}Q`
+   * - :func:`Pair distribution function <pair_distribution_function>`
+     - :math:`g(r) = 1 + \frac{G(r)}{4 \pi \rho r}`
+   * - :func:`Radial distribution function <radial_distribution_function>`
+     - :math:`\text{RDF}(r) = 4\pi r^2 \rho g(r)`
+   * - :func:`Linearized radial distribution function <linearized_radial_distribution_function>`
+     - :math:`T(r) = \text{RDF}(r) / r`
+   * - :func:`Running coordination number <running_coordination_number>`
+     - :math:`C(r) = \int_0^r\, \text{RDF}(r') \text{d}r'`
+
+Where :math:`\rho` is the atomic density.
+
+The implementations provided here are defined in terms of each other and must be called
+in the order shown in the table.
+"""  # noqa: E501
+
+import scipp as sc
+
+from ess.reduce.uncertainty import UncertaintyBroadcastMode, broadcast_uncertainties
+
+
+def pair_correlation_function(
+    s: sc.DataArray,
+    r: sc.Variable,
+    *,
+    uncertainty_broadcast_mode: UncertaintyBroadcastMode = UncertaintyBroadcastMode.drop,  # noqa: E501
+    return_covariances: bool = False,
+) -> sc.DataArray | tuple[sc.DataArray, sc.DataArray]:
+    """Compute the pair correlation function from a structure factor.
+
+    Computes the pair correlation function :math:`G(r)` from the overall
+    scattering function :math:`S(Q)`.
+    See `Review: Pair distribution functions from neutron total scattering for the study of local structure in disordered materials <https://www.sciencedirect.com/science/article/pii/S2773183922000374>`_
+    for the definition of :math:`G(r)` or :mod:`ess.diffraction.pdf`.
+    (Note, in the reference, the pair correlation function is denoted as :math:`D(r)`,
+    but since :math:`G(r)` is the more common name, that is what is used here).
+
+    The inputs to the function are:
+
+    * A histogram representing :math:`S(Q)` with :math:`N` bins on a bin-edge grid with
+      :math:`N+1` edges :math:`Q_j` for :math:`j=0\\ldots N`.
+    * The bin-edge grid over :math:`r`. The output histogram representing :math:`G(r)`
+      will be computed on this grid.
+
+    In each output bin, the output is computed as:
+
+    .. math::
+        G_{i+\\frac{1}{2}} &= \\frac{2}{\\pi(r_{i+1}-r_i)} \\int_{r_i}^{r_{i+1}} \\int_{0}^\\infty (S(Q) - 1) Q \\sin(Q r) dQ \\ dr  \\\\
+        &\\approx \\frac{2}{\\pi(r_{i+1}-r_i)} \\sum_{j=0}^{N-1} (S(Q)_{j+\\frac{1}{2}} - 1) (\\cos(\\bar{Q}_{j+\\frac{1}{2}} r_{i})-\\cos(\\bar{Q}_{j+\\frac{1}{2}} r_{i+1})) \\Delta Q_{j+\\frac{1}{2}}
+
+    Note that in the above expression the subscript :math:`_{j+\\frac{1}{2}}` is used
+    to denote quantities belonging to the :math:`j^\\text{th}` bin of a histogram,
+    :math:`\\bar{Q}_{j+\\frac{1}{2}} = \\frac{Q_j + Q_{j+1}}{2}` and
+    :math:`\\Delta Q_{j+\\frac{1}{2}} = Q_{j+1} - Q_{j}`.
+
+    Parameters
+    ----------
+    s:
+        1D DataArray representing :math:`S(Q)` with
+        a bin-edge coordinate called ``'Q'``.
+    r:
+        1D array, bin-edges of output grid.
+    uncertainty_broadcast_mode: UncertaintyBroadcastMode,
+        Choose how uncertainties in S(Q) are broadcast to G(r).
+        Defaults to ``UncertaintyBroadcastMode.drop``.
+    return_covariances:
+        If true the second output of the function will be a 2D array representing
+        the covariance matrix of the entries in the first output.
+
+    Returns
+    -------
+    g: scipp.DataArray
+        1D array representing :math:`G(r)` with a bin-edge coordinate called
+        ``'r'`` that is the provided output grid.
+    cov: scipp.DataArray
+        2D array representing the covariance matrix of the entries in ``g``.
+        Only returned if ``return_covariances=True``.
+
+    See Also
+    --------
+    ess.diffraction.pdf:
+        Module with related functions.
+    """  # noqa: E501
+    q = s.coords['Q']
+    qm = sc.midpoints(q)
+    dq = q[1:] - q[:-1]
+    dr = r[1:] - r[:-1]
+
+    v = sc.cos(qm * r * sc.scalar(1, unit='rad'))
+    v = v[r.dim, :-1] - v[r.dim, 1:]
+
+    ioq = (s - sc.scalar(1.0, unit=s.unit)) * dq
+    mat_ioq = broadcast_uncertainties(ioq, prototype=v, mode=uncertainty_broadcast_mode)
+    c = 2 / sc.constants.pi / dr
+    g = c * (v * mat_ioq).sum(q.dim)
+    g = sc.DataArray(g.data, coords={'r': r})
+    if return_covariances:
+        cov_g = _covariance_of_matrix_vector_product(c * v, ioq)
+        cov_g = sc.DataArray(
+            cov_g, coords={d: r.rename_dims({r.dim: d}) for d in cov_g.dims}
+        )
+        return g, cov_g
+    return g
+
+
+def pair_distribution_function(
+    g: sc.DataArray, *, atomic_density: sc.Variable
+) -> sc.DataArray:
+    r"""Compute the pair distribution function from a pair correlation function.
+
+    Computes the pair distribution function
+
+    .. math::
+
+        g(r) = 1 + \frac{G(r)}{4 \pi \rho r}
+
+    where :math:`G(r)` is the :func:`pair_correlation_function`
+    and :math:`\rho` is the atomic density.
+
+    :math:`g` and :math:`G` are both defined on bin-edges in :math:`r`.
+    The :math:`r` variable in the denominator is computed on the
+    :func:`midpoints <scipp.midpoints>` of ``r``.
+
+    Parameters
+    ----------
+    g:
+        :func:`Pair correlation function <pair_correlation_function>` :math:`G(r)`.
+    atomic_density:
+        The atomic density :math:`\rho` of the material.
+
+    Returns
+    -------
+    g: scipp.DataArray
+        1D array representing :math:`g(r)`.
+
+    See Also
+    --------
+    ess.diffraction.pdf:
+        Module with related functions.
+    """
+    return 1 + g / (4 * sc.constants.pi * atomic_density * sc.midpoints(g.coords['r']))
+
+
+def radial_distribution_function(
+    g: sc.DataArray, *, atomic_density: sc.Variable
+) -> sc.DataArray:
+    r"""Compute the radial distribution function from a pair distribution function.
+
+    Computes the radial distribution function
+
+    .. math::
+
+        \text{RDF}(r) = 4 \pi r^2 \rho g(r)
+
+    where :math:`g(r)` is the :func:`pair_distribution_function`
+    and :math:`\rho` is the atomic density.
+
+    :math:`\text{RDF}` and :math:`g` are both defined on bin-edges in :math:`r`.
+    The :math:`r^2` factor is computed on the
+    :func:`midpoints <scipp.midpoints>` of ``r``.
+
+    Parameters
+    ----------
+    g:
+        :func:`Pair distribution function <pair_distribution_function>` :math:`g(r)`.
+    atomic_density:
+        The atomic density :math:`\rho` of the material.
+
+    Returns
+    -------
+    rdf: scipp.DataArray
+        1D array representing :math:`\text{RDF}(r)`.
+
+    See Also
+    --------
+    ess.diffraction.pdf:
+        Module with related functions.
+    """
+    return 4 * sc.constants.pi * atomic_density * sc.midpoints(g.coords['r']) ** 2 * g
+
+
+def linearized_radial_distribution_function(rdf: sc.DataArray) -> sc.DataArray:
+    r"""Compute the linearized radial distribution function
+    from a radial distribution function.
+
+    Computes the linearized radial distribution function
+
+    .. math::
+
+        \text{T}(r) = \frac{\text{RDF}(r)}{r}
+
+    where :math:`\text{RDF}(r)` is the :func:`radial_distribution_function`.
+
+    :math:`\text{RDF}` and :math:`T` are both defined on bin-edges in :math:`r`.
+    The :math:`r` variable in the denominator is computed on the
+    :func:`midpoints <scipp.midpoints>` of ``r``.
+
+    Parameters
+    ----------
+    rdf:
+        :func:`Radial distribution function <radial_distribution_function>`
+        :math:`\text{RDF}(r)`.
+
+    Returns
+    -------
+    t: scipp.DataArray
+        1D array representing :math:`T(r)`.
+
+    See Also
+    --------
+    ess.diffraction.pdf:
+        Module with related functions.
+    """
+    return rdf / sc.midpoints(rdf.coords['r'])
+
+
+def running_coordination_number(rdf: sc.DataArray) -> sc.DataArray:
+    r"""Compute the running coordination number from a radial distribution function.
+
+    Computes the running coordination number
+
+    .. math::
+
+        C(r) = \int_0^r\, \text{RDF}(r') \text{d}r'
+
+    where :math:`\text{RDF}(r)` is the :func:`radial_distribution_function`.
+
+    Parameters
+    ----------
+    rdf:
+        :func:`Radial distribution function <radial_distribution_function>`
+        :math:`\text{RDF}(r)`.
+
+    Returns
+    -------
+    c: scipp.DataArray
+        1D array representing :math:`C(r)`.
+
+    See Also
+    --------
+    ess.diffraction.pdf:
+        Module with related functions.
+    """
+    r = rdf.coords['r']
+    if not sc.issorted(r, dim=r.dim, order='ascending'):
+        raise ValueError('The bin-edges in `r` must be sorted in ascending order.')
+
+    return sc.cumsum(rdf * (r[1:] - r[:-1]))
+
+
+def _covariance_of_matrix_vector_product(
+    A: sc.typing.VariableLike, v: sc.typing.VariableLike
+) -> sc.Variable:
+    if A.variances is not None:
+        raise ValueError('The expression is not valid if the matrix has variances.')
+    v = sc.variances(v)  # type: ignore[arg-type] # faulty annotation in variances()?
+    if A.dims[1] != v.dim:
+        A = A.transpose()
+    cov = (sc.sqrt(v) * A).values
+    cov = cov @ cov.T
+    return sc.array(
+        dims=[A.dims[0], A.dims[0] + '_2'], values=cov, unit=v.unit * A.unit**2
+    )
diff --git a/packages/essdiffraction/src/ess/diffraction/peaks.py b/packages/essdiffraction/src/ess/diffraction/peaks.py
index b3bf2bf96..77753b178 100644
--- a/packages/essdiffraction/src/ess/diffraction/peaks.py
+++ b/packages/essdiffraction/src/ess/diffraction/peaks.py
@@ -19,7 +19,7 @@ def dspacing_peaks_from_cif(cif, intensity_threshold=None, **kwargs) -> sc.DataA
         (https://www.crystallography.net/cod/) or the Materials Project
         (https://www.materialsproject.org/).
 
-        For more details, see :py:`ncrystal.cifutils.CIFSource`.
+        For more details, see ``ncrystal.cifutils.CIFSource``.
 
     intensity_threshold:
         The minimum intensity of the peaks to return.
diff --git a/packages/essdiffraction/src/ess/powder/__init__.py b/packages/essdiffraction/src/ess/powder/__init__.py
index 619854710..faa956396 100644
--- a/packages/essdiffraction/src/ess/powder/__init__.py
+++ b/packages/essdiffraction/src/ess/powder/__init__.py
@@ -14,7 +14,6 @@
     grouping,
     masking,
     smoothing,
-    transform,
     workflow,
 )
 from .correction import RunNormalization
@@ -50,7 +49,6 @@
     "masking",
     "providers",
     "smoothing",
-    "transform",
     "with_pixel_mask_filenames",
     "workflow",
 ]
diff --git a/packages/essdiffraction/src/ess/powder/transform.py b/packages/essdiffraction/src/ess/powder/transform.py
deleted file mode 100644
index 3207c5e86..000000000
--- a/packages/essdiffraction/src/ess/powder/transform.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# SPDX-License-Identifier: BSD-3-Clause
-# Copyright (c) 2023 Scipp contributors (https://github.com/scipp)
-"""Signal transformation algorithms for powder diffraction."""
-
-import scipp as sc
-
-from ess.reduce.uncertainty import UncertaintyBroadcastMode, broadcast_uncertainties
-
-
-def _covariance_of_matrix_vector_product(A, v):
-    if A.variances is not None:
-        raise ValueError('The expression is not valid if the matrix has variances.')
-    v = sc.variances(v)
-    if A.dims[1] != v.dim:
-        A = A.transpose()
-    cov = (sc.sqrt(v) * A).values
-    cov = cov @ cov.T
-    return sc.array(
-        dims=[A.dims[0], A.dims[0] + '_2'], values=cov, unit=v.unit * A.unit**2
-    )
-
-
-def compute_pdf_from_structure_factor(
-    s: sc.DataArray,
-    r: sc.Variable,
-    *,
-    uncertainty_broadcast_mode=UncertaintyBroadcastMode.drop,
-    return_covariances=False,
-) -> sc.DataArray:
-    '''
-    Compute a pair distribution function from a structure factor.
-
-    Computes the pair distribution function :math:`G(r)` as defined in
-    `Review: Pair distribution functions from neutron total scattering for the study of local structure in disordered materials <https://www.sciencedirect.com/science/article/pii/S2773183922000374>`_ (note, in the reference, the pair distribution function is denoted :math:`D(r)`, but since :math:`G(r)` is the more common name, that is what is used here).
-    from the overall scattering function :math:`S(Q)`.
-
-    The inputs to the algorithm are:
-
-    * A histogram representing :math:`S(Q)` with :math:`N` bins on a bin-edge grid with :math:`N+1` edges :math:`Q_j` for :math:`j=0\\ldots N`.
-    * The bin-edge grid over :math:`r` the output histogram representing :math:`G(r)` will be computed on.
-
-    In each output bin, the output is computed as:
-
-    .. math::
-        G_{i+\\frac{1}{2}} &= \\frac{2}{\\pi(r_{i+1}-r_i)} \\int_{r_i}^{r_{i+1}} \\int_{0}^\\infty (S(Q) - 1) Q \\sin(Q r) dQ \\ dr  \\\\
-        &\\approx \\frac{2}{\\pi(r_{i+1}-r_i)} \\sum_{j=0}^{N-1} (S(Q)_{j+\\frac{1}{2}} - 1) (\\cos(\\bar{Q}_{j+\\frac{1}{2}} r_{i})-\\cos(\\bar{Q}_{j+\\frac{1}{2}} r_{i+1})) \\Delta Q_{j+\\frac{1}{2}}
-
-    Note that in the above expression the subscript :math:`_{j+\\frac{1}{2}}` is used to denote
-    quantities belonging to the :math:`j^\\text{th}` bin of a histogram, :math:`\\bar{Q}_{j+\\frac{1}{2}} = \\frac{Q_j + Q_{j+1}}{2}` and :math:`\\Delta Q_{j+\\frac{1}{2}} = Q_{j+1} - Q_{j}`.
-
-    Parameters
-    ----------
-    s:
-        1D DataArray representing :math:`S(Q)` with a bin-edge coordinate called :math:`Q`
-    r:
-        1D array, bin-edges of output grid
-    uncertainty_broadcast_mode: UncertaintyBroadcastMode,
-        Choose how uncertainties in S(Q) are broadcast to G(r).
-        Defaults to ``UncertaintyBroadcastMode.drop``.
-    return_covariances:
-        bool, if True the second output of the function will be a 2D array representing the covariance
-        matrix of the entries in the first output.
-
-    Returns
-    -------
-    :
-        1D DataArray representing :math:`G(r)` with a bin-edge coordinate called :math:`r` that is the provided output grid, and optionally a 2D DataArray representing the covariances of :math:`G(r)`.
-
-    '''  # noqa: E501
-    q = s.coords['Q']
-    qm = sc.midpoints(q)
-    dq = q[1:] - q[:-1]
-    dr = r[1:] - r[:-1]
-
-    v = sc.cos(qm * r * sc.scalar(1, unit='rad'))
-    v = v[r.dim, :-1] - v[r.dim, 1:]
-
-    ioq = (s - sc.scalar(1.0, unit=s.unit)) * dq
-    mat_ioq = broadcast_uncertainties(ioq, prototype=v, mode=uncertainty_broadcast_mode)
-    c = 2 / sc.constants.pi / dr
-    g = c * (v * mat_ioq).sum(q.dim)
-    g = sc.DataArray(g.data, coords={'r': r})
-    if return_covariances:
-        cov_g = _covariance_of_matrix_vector_product(c * v, ioq)
-        cov_g = sc.DataArray(
-            cov_g, coords={d: r.rename_dims({r.dim: d}) for d in cov_g.dims}
-        )
-        return g, cov_g
-    return g
diff --git a/packages/essdiffraction/tests/diffraction/pdf_test.py b/packages/essdiffraction/tests/diffraction/pdf_test.py
new file mode 100644
index 000000000..9065e984d
--- /dev/null
+++ b/packages/essdiffraction/tests/diffraction/pdf_test.py
@@ -0,0 +1,164 @@
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright (c) 2026 Scipp contributors (https://github.com/scipp)
+import pytest
+import scipp as sc
+import scipp.testing
+from ess.diffraction.pdf import (
+    _covariance_of_matrix_vector_product,
+    linearized_radial_distribution_function,
+    pair_correlation_function,
+    pair_distribution_function,
+    radial_distribution_function,
+    running_coordination_number,
+)
+
+
+def test_pdf_structure_factor_needs_q_coord() -> None:
+    da = sc.DataArray(sc.ones(sizes={'Q': 3}))
+    r = sc.array(dims='r', values=[2, 3, 4, 5.0])
+
+    with pytest.raises(KeyError):
+        pair_correlation_function(
+            da,
+            r,
+        )
+
+
+def test_pair_correlation_function() -> None:
+    da = sc.DataArray(
+        sc.ones(sizes={'Q': 3}),
+        coords={'Q': sc.array(dims='Q', values=[0, 1, 2, 3.0], unit='1/angstrom')},
+    )
+    da.variances = da.data.values.copy()
+    r = sc.array(dims='r', values=[2, 3, 4, 5.0], unit='angstrom')
+
+    G: sc.DataArray = pair_correlation_function(  # type: ignore[assignment]
+        da,
+        r,
+    )
+    assert G.data.unit == '1/angstrom^2'
+    sc.testing.assert_identical(G.coords['r'], r)
+
+
+def test_pair_correlation_function_can_return_covariances() -> None:
+    da = sc.DataArray(
+        sc.ones(sizes={'Q': 3}),
+        coords={'Q': sc.array(dims='Q', values=[0, 1, 2, 3.0], unit='1/angstrom')},
+    )
+    da.variances = da.data.values.copy()
+    r = sc.array(dims='r', values=[2, 3, 4, 5.0], unit='angstrom')
+
+    [G, cov] = pair_correlation_function(da, r, return_covariances=True)
+    assert G.data.unit == '1/angstrom^2'
+    assert cov.data.unit == '1/angstrom^4'
+    assert cov.ndim == 2
+
+
+def test_pair_correlation_function_result_unchanged() -> None:
+    # Note: bogus data
+    da = sc.DataArray(
+        sc.array(dims='Q', values=[1, 2, 4.0]),
+        coords={'Q': sc.array(dims='Q', values=[0, 1, 2, 3.0], unit='1/angstrom')},
+    )
+    r = sc.array(dims='r', values=[2, 3, 4, 5.0], unit='angstrom')
+
+    G: sc.DataArray = pair_correlation_function(  # type: ignore[assignment]
+        da,
+        r,
+    )
+    sc.testing.assert_allclose(
+        G.data,
+        sc.array(dims='r', values=[-0.616322, 1.51907, -3.11757], unit='1/angstrom^2'),
+        rtol=sc.scalar(1e-5),
+    )
+
+
+def test_pair_distribution_function() -> None:
+    da = sc.DataArray(
+        sc.ones(sizes={'Q': 3}),
+        coords={'Q': sc.array(dims='Q', values=[0, 1, 2, 3.0], unit='1/angstrom')},
+    )
+    da.variances = da.data.values.copy()
+    r = sc.array(dims='r', values=[2, 3, 4, 5.0], unit='angstrom')
+    atomic_density = sc.scalar(1.0, unit='1/angstrom^3')
+
+    G: sc.DataArray = pair_correlation_function(da, r)  # type: ignore[assignment]
+    g = pair_distribution_function(G, atomic_density=atomic_density)
+    assert g.data.unit == '1'
+    sc.testing.assert_identical(G.coords['r'], r)
+
+
+def test_radial_distribution_function() -> None:
+    da = sc.DataArray(
+        sc.ones(sizes={'Q': 3}),
+        coords={'Q': sc.array(dims='Q', values=[0, 1, 2, 3.0], unit='1/angstrom')},
+    )
+    da.variances = da.data.values.copy()
+    r = sc.array(dims='r', values=[2, 3, 4, 5.0], unit='angstrom')
+    atomic_density = sc.scalar(1.0, unit='1/angstrom^3')
+
+    G: sc.DataArray = pair_correlation_function(da, r)  # type: ignore[assignment]
+    g = pair_distribution_function(G, atomic_density=atomic_density)
+    rdf = radial_distribution_function(g, atomic_density=atomic_density)
+    assert rdf.data.unit == '1/angstrom'
+    sc.testing.assert_identical(G.coords['r'], r)
+
+
+def test_linearized_radial_distribution_function() -> None:
+    da = sc.DataArray(
+        sc.ones(sizes={'Q': 3}),
+        coords={'Q': sc.array(dims='Q', values=[0, 1, 2, 3.0], unit='1/angstrom')},
+    )
+    da.variances = da.data.values.copy()
+    r = sc.array(dims='r', values=[2, 3, 4, 5.0], unit='angstrom')
+    atomic_density = sc.scalar(1.0, unit='1/angstrom^3')
+
+    G: sc.DataArray = pair_correlation_function(da, r)  # type: ignore[assignment]
+    g = pair_distribution_function(G, atomic_density=atomic_density)
+    rdf = radial_distribution_function(g, atomic_density=atomic_density)
+    T = linearized_radial_distribution_function(rdf)
+    assert T.data.unit == '1/angstrom^2'
+    sc.testing.assert_identical(T.coords['r'], r)
+
+
+def test_running_coordination_number() -> None:
+    da = sc.DataArray(
+        sc.ones(sizes={'Q': 3}),
+        coords={'Q': sc.array(dims='Q', values=[0, 1, 2, 3.0], unit='1/angstrom')},
+    )
+    da.variances = da.data.values.copy()
+    r = sc.array(dims='r', values=[2, 4, 5, 6.0], unit='angstrom')
+    atomic_density = sc.scalar(1.0, unit='1/angstrom^3')
+
+    G: sc.DataArray = pair_correlation_function(da, r)  # type: ignore[assignment]
+    g = pair_distribution_function(G, atomic_density=atomic_density)
+    rdf = radial_distribution_function(g, atomic_density=atomic_density)
+    C = running_coordination_number(rdf)
+    assert C.data.unit == '1'
+    sc.testing.assert_identical(C.coords['r'], r)
+    sc.testing.assert_identical(C[0].value, rdf[0].value * 2)  # *2 from bin size
+    sc.testing.assert_identical(C[1].value, rdf[0].value * 2 + rdf[1].value * 1)
+
+
+def test_matrix_vector_covariance() -> None:
+    A = sc.array(dims='ab', values=[[1, 2], [3, 4]])
+    v = sc.array(dims='b', values=[0, 0.0], variances=[1.0, 2.0])
+    C = _covariance_of_matrix_vector_product(A, v)
+    a00, a01, a10, a11 = A.values.ravel()
+    v0, v1 = v.variances
+    sc.testing.assert_allclose(
+        C,
+        sc.array(
+            dims=['a', 'a_2'],
+            values=[
+                [
+                    v0 * a00 * a00 + v1 * a01 * a01,
+                    v0 * a00 * a10 + v1 * a01 * a11,
+                ],
+                [
+                    v0 * a10 * a00 + v1 * a11 * a01,
+                    v0 * a10 * a10 + v1 * a11 * a11,
+                ],
+            ],
+        ),
+    )
diff --git a/packages/essdiffraction/tests/powder/transform_test.py b/packages/essdiffraction/tests/powder/transform_test.py
deleted file mode 100644
index df2fcf3c7..000000000
--- a/packages/essdiffraction/tests/powder/transform_test.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# SPDX-License-Identifier: BSD-3-Clause
-# Copyright (c) 2023 Scipp contributors (https://github.com/scipp)
-import pytest
-import scipp as sc
-import scipp.testing
-from ess.powder.transform import (
-    _covariance_of_matrix_vector_product,
-    compute_pdf_from_structure_factor,
-)
-
-
-def test_pdf_structure_factor_needs_q_coord():
-    da = sc.DataArray(sc.ones(sizes={'Q': 3}))
-    r = sc.array(dims='r', values=[2, 3, 4, 5.0])
-    with pytest.raises(KeyError):
-        compute_pdf_from_structure_factor(
-            da,
-            r,
-        )
-
-
-def test_pdf_structure_factor():
-    da = sc.DataArray(
-        sc.ones(sizes={'Q': 3}),
-        coords={'Q': sc.array(dims='Q', values=[0, 1, 2, 3.0], unit='1/angstrom')},
-    )
-    da.variances = da.data.values.copy()
-    r = sc.array(dims='r', values=[2, 3, 4, 5.0], unit='angstrom')
-    v = compute_pdf_from_structure_factor(
-        da,
-        r,
-    )
-    assert v.data.unit == '1/angstrom^2'
-    sc.testing.assert_identical(v.coords['r'], r)
-
-
-def test_pdf_structure_factor_can_return_covariances():
-    da = sc.DataArray(
-        sc.ones(sizes={'Q': 3}),
-        coords={'Q': sc.array(dims='Q', values=[0, 1, 2, 3.0], unit='1/angstrom')},
-    )
-    da.variances = da.data.values.copy()
-    r = sc.array(dims='r', values=[2, 3, 4, 5.0], unit='angstrom')
-    compute_pdf_from_structure_factor(da, r, return_covariances=True)
-
-
-def test_pdf_structure_factor_result_unchanged():
-    # Note: bogus data
-    da = sc.DataArray(
-        sc.array(dims='Q', values=[1, 2, 4.0]),
-        coords={'Q': sc.array(dims='Q', values=[0, 1, 2, 3.0], unit='1/angstrom')},
-    )
-    r = sc.array(dims='r', values=[2, 3, 4, 5.0], unit='angstrom')
-    v = compute_pdf_from_structure_factor(
-        da,
-        r,
-    )
-    sc.testing.assert_allclose(
-        v.data,
-        sc.array(dims='r', values=[-0.616322, 1.51907, -3.11757], unit='1/angstrom^2'),
-        rtol=sc.scalar(1e-5),
-    )
-
-
-def test_matrix_vector_covariance():
-    A = sc.array(dims='ab', values=[[1, 2], [3, 4]])
-    v = sc.array(dims='b', values=[0, 0.0], variances=[1.0, 2.0])
-    C = _covariance_of_matrix_vector_product(A, v)
-    a00, a01, a10, a11 = A.values.ravel()
-    v0, v1 = v.variances
-    sc.testing.assert_allclose(
-        C,
-        sc.array(
-            dims=['a', 'a_2'],
-            values=[
-                [
-                    v0 * a00 * a00 + v1 * a01 * a01,
-                    v0 * a00 * a10 + v1 * a01 * a11,
-                ],
-                [
-                    v0 * a10 * a00 + v1 * a11 * a01,
-                    v0 * a10 * a10 + v1 * a11 * a11,
-                ],
-            ],
-        ),
-    )