diff --git a/docs/source/error_measures.rst b/docs/source/error_measures.rst
index dc47ced9..6e998949 100644
--- a/docs/source/error_measures.rst
+++ b/docs/source/error_measures.rst
@@ -17,6 +17,8 @@ Example::
 
 .. autoclass:: MeanSquaredError
 
+.. autoclass:: NormalisedRootMeanSquaredError
+
 .. autoclass:: ProbabilityBasedError
 
 .. autoclass:: ProblemErrorMeasure
diff --git a/pints/__init__.py b/pints/__init__.py
index 47292639..d06f37a5 100644
--- a/pints/__init__.py
+++ b/pints/__init__.py
@@ -135,11 +135,12 @@ from ._boundaries import (
 #
 from ._error_measures import (
     ErrorMeasure,
-    ProblemErrorMeasure,
-    ProbabilityBasedError,
-    SumOfErrors,
     MeanSquaredError,
+    NormalisedRootMeanSquaredError,
+    ProbabilityBasedError,
+    ProblemErrorMeasure,
     RootMeanSquaredError,
+    SumOfErrors,
     SumOfSquaresError,
 )
 
diff --git a/pints/_error_measures.py b/pints/_error_measures.py
index dae77adc..56a4ba3d 100644
--- a/pints/_error_measures.py
+++ b/pints/_error_measures.py
@@ -64,6 +64,81 @@ class ProblemErrorMeasure(ErrorMeasure):
         return self._n_parameters
 
 
+class MeanSquaredError(ProblemErrorMeasure):
+    """
+    Calculates the mean square error: ``f = sum( (x[i] - y[i])**2 ) / n``,
+    where ``n`` is the product of the number of times in the time series and
+    the number of outputs of the problem.
+
+    Extends :class:`ProblemErrorMeasure`.
+
+    Parameters
+    ----------
+    problem
+        A :class:`pints.SingleOutputProblem` or
+        :class:`pints.MultiOutputProblem`.
+    weights
+        An optional sequence of (float) weights, exactly one per problem
+        output. If no weights are specified all sums will be weighted equally.
+    """
+    def __init__(self, problem, weights=None):
+        super(MeanSquaredError, self).__init__(problem)
+        self._ninv = 1.0 / np.product(self._values.shape)
+
+        if weights is None:
+            weights = [1] * self._n_outputs
+        elif self._n_outputs != len(weights):
+            raise ValueError(
+                'Number of weights must match number of problem outputs.')
+        # Check weights
+        self._weights = np.asarray([float(w) for w in weights])
+
+    def __call__(self, x):
+        return np.sum(((np.sum((self._problem.evaluate(x) - self._values)**2,
+                               axis=0) * self._weights) * self._ninv),
+                      axis=0)
+
+    def evaluateS1(self, x):
+        """ See :meth:`ErrorMeasure.evaluateS1()`. """
+        y, dy = self._problem.evaluateS1(x)
+        dy = dy.reshape((self._n_times, self._n_outputs, self._n_parameters))
+        r = y - self._values
+        e = self._ninv * np.sum(np.sum(r**2, axis=0) * self._weights, axis=0)
+        de = 2 * self._ninv * np.sum(np.sum((r.T * dy.T), axis=2) *
+                                     self._weights, axis=1)
+        return e, de
+
+
+class NormalisedRootMeanSquaredError(ProblemErrorMeasure):
+    """
+    Calculates a normalised root mean squared error. This is similar to the
+    (unnormalised) :class:`RootMeanSquaredError`, which is defined as
+    ``f = sqrt( sum( (x[i] - y[i])**2 / n) ) / C``
+    with a normalisation constant ``C`` given by
+    ``C = sqrt( sum((y[i])**2 / n) )``.
+
+    Extends :class:`ProblemErrorMeasure`.
+
+    Parameters
+    ----------
+    problem
+        A :class:`pints.SingleOutputProblem`
+    """
+    def __init__(self, problem):
+        super(NormalisedRootMeanSquaredError, self).__init__(problem)
+
+        if not isinstance(problem, pints.SingleOutputProblem):
+            raise ValueError(
+                'This measure is only defined for single output problems.')
+
+        self._ninv = 1.0 / len(self._values)
+        self._norm = 1.0 / np.sqrt(self._ninv * np.sum(self._values**2))
+
+    def __call__(self, x):
+        return self._norm * np.sqrt(self._ninv * np.sum(
+            (self._problem.evaluate(x) - self._values)**2))
+
+
 class ProbabilityBasedError(ErrorMeasure):
     """
     Changes the sign of a :class:`LogPDF` to use it as an error. Minimising
@@ -101,6 +176,32 @@ class ProbabilityBasedError(ErrorMeasure):
         return self._log_pdf.n_parameters()
 
 
+class RootMeanSquaredError(ProblemErrorMeasure):
+    """
+    Calculates a root mean squared error (RMSE):
+    ``f = sqrt( sum( (x[i] - y[i])**2 / n) )``
+
+    Extends :class:`ProblemErrorMeasure`.
+
+    Parameters
+    ----------
+    problem
+        A :class:`pints.SingleOutputProblem`
+    """
+    def __init__(self, problem):
+        super(RootMeanSquaredError, self).__init__(problem)
+
+        if not isinstance(problem, pints.SingleOutputProblem):
+            raise ValueError(
+                'This measure is only defined for single output problems.')
+
+        self._ninv = 1.0 / len(self._values)
+
+    def __call__(self, x):
+        return np.sqrt(self._ninv * np.sum(
+            (self._problem.evaluate(x) - self._values)**2))
+
+
 class SumOfErrors(ErrorMeasure):
     """
     Calculates a sum of :class:`ErrorMeasure` objects, all defined on the same
@@ -201,77 +302,6 @@ class SumOfErrors(ErrorMeasure):
         return self._n_parameters
 
 
-class MeanSquaredError(ProblemErrorMeasure):
-    """
-    Calculates the mean square error: ``f = sum( (x[i] - y[i])**2 ) / n``,
-    where ``n`` is the product of the number of times in the time series and
-    the number of outputs of the problem.
-
-    Extends :class:`ProblemErrorMeasure`.
-
-    Parameters
-    ----------
-    problem
-        A :class:`pints.SingleOutputProblem` or
-        :class:`pints.MultiOutputProblem`.
-    weights
-        An optional sequence of (float) weights, exactly one per problem
-        output. If no weights are specified all sums will be weighted equally.
-    """
-    def __init__(self, problem, weights=None):
-        super(MeanSquaredError, self).__init__(problem)
-        self._ninv = 1.0 / np.product(self._values.shape)
-
-        if weights is None:
-            weights = [1] * self._n_outputs
-        elif self._n_outputs != len(weights):
-            raise ValueError(
-                'Number of weights must match number of problem outputs.')
-        # Check weights
-        self._weights = np.asarray([float(w) for w in weights])
-
-    def __call__(self, x):
-        return np.sum(((np.sum((self._problem.evaluate(x) - self._values)**2,
-                               axis=0) * self._weights) * self._ninv),
-                      axis=0)
-
-    def evaluateS1(self, x):
-        """ See :meth:`ErrorMeasure.evaluateS1()`. """
-        y, dy = self._problem.evaluateS1(x)
-        dy = dy.reshape((self._n_times, self._n_outputs, self._n_parameters))
-        r = y - self._values
-        e = self._ninv * np.sum(np.sum(r**2, axis=0) * self._weights, axis=0)
-        de = 2 * self._ninv * np.sum(np.sum((r.T * dy.T), axis=2) *
-                                     self._weights, axis=1)
-        return e, de
-
-
-class RootMeanSquaredError(ProblemErrorMeasure):
-    """
-    Calculates a root mean squared error (RMSE):
-    ``f = sqrt( sum( (x[i] - y[i])**2 / n) )``
-
-    Extends :class:`ProblemErrorMeasure`.
-
-    Parameters
-    ----------
-    problem
-        A :class:`pints.SingleOutputProblem`
-    """
-    def __init__(self, problem):
-        super(RootMeanSquaredError, self).__init__(problem)
-
-        if not isinstance(problem, pints.SingleOutputProblem):
-            raise ValueError(
-                'This measure is only defined for single output problems.')
-
-        self._ninv = 1.0 / len(self._values)
-
-    def __call__(self, x):
-        return np.sqrt(self._ninv * np.sum(
-            (self._problem.evaluate(x) - self._values)**2))
-
-
 class SumOfSquaresError(ProblemErrorMeasure):
     """
     Calculates a sum-of-squares error: ``f = sum( (x[i] - y[i])**2 )``
diff --git a/pints/_log_likelihoods.py b/pints/_log_likelihoods.py
index 1d8a2f4a..6a3eaa50 100644
--- a/pints/_log_likelihoods.py
+++ b/pints/_log_likelihoods.py
@@ -137,6 +137,66 @@ class ARMA11LogLikelihood(pints.ProblemLogLikelihood):
                       - np.sum(autocorr_error**2, axis=0) / (2 * sigma**2))
 
 
+class CauchyLogLikelihood(pints.ProblemLogLikelihood):
+    r"""
+    Calculates a log-likelihood assuming independent Cauchy-distributed noise
+    at each time point, and adds one parameter: the scale (``sigma``).
+
+    For a noise characterised by ``sigma``, the log-likelihood is of the form:
+
+    .. math::
+        \log{L(\theta, \sigma)} =
+              -N\log \pi - N\log \sigma
+              -\sum_{i=1}^N\log(1 +
+            \frac{x_i - f(\theta)}{\sigma}^2)
+
+    Extends :class:`ProblemLogLikelihood`.
+
+    Parameters
+    ----------
+    problem
+        A :class:`SingleOutputProblem` or :class:`MultiOutputProblem`. For a
+        single-output problem one parameter is added ``sigma``, where
+        ``sigma`` is scale, for a multi-output problem ``n_outputs``
+        parameters are added.
+    """
+
+    def __init__(self, problem):
+        super(CauchyLogLikelihood, self).__init__(problem)
+
+        # Get number of times, number of outputs
+        self._nt = len(self._times)
+        self._no = problem.n_outputs()
+
+        # Add parameters to problem (one for each output)
+        self._n_parameters = problem.n_parameters() + self._no
+
+        # Pre-calculate
+        self._n = len(self._times)
+        self._n_log_pi = self._n * np.log(np.pi)
+
+    def __call__(self, x):
+        # For multiparameter problems the parameters are stored as
+        # (model_params_1, model_params_2, ..., model_params_k,
+        # sigma_1, sigma_2,...)
+        n = self._n
+        m = self._no
+
+        # problem parameters
+        problem_parameters = x[:-m]
+        error = self._values - self._problem.evaluate(problem_parameters)
+
+        # Distribution parameters
+        sigma = np.asarray(x[-m:])
+
+        # Calculate
+        return np.sum(
+            - self._n_log_pi
+            - n * np.log(sigma)
+            - np.sum(np.log(1 + (error / sigma)**2), axis=0)
+        )
+
+
 class GaussianIntegratedUniformLogLikelihood(pints.ProblemLogLikelihood):
     r"""
     Calculates a log-likelihood assuming independent Gaussian-distributed noise
@@ -278,66 +338,6 @@ class GaussianIntegratedUniformLogLikelihood(pints.ProblemLogLikelihood):
         )
 
 
-class CauchyLogLikelihood(pints.ProblemLogLikelihood):
-    r"""
-    Calculates a log-likelihood assuming independent Cauchy-distributed noise
-    at each time point, and adds one parameter: the scale (``sigma``).
-
-    For a noise characterised by ``sigma``, the log-likelihood is of the form:
-
-    .. math::
-        \log{L(\theta, \sigma)} =
-              -N\log \pi - N\log \sigma
-              -\sum_{i=1}^N\log(1 +
-            \frac{x_i - f(\theta)}{\sigma}^2)
-
-    Extends :class:`ProblemLogLikelihood`.
-
-    Parameters
-    ----------
-    problem
-        A :class:`SingleOutputProblem` or :class:`MultiOutputProblem`. For a
-        single-output problem one parameter is added ``sigma``, where
-        ``sigma`` is scale, for a multi-output problem ``n_outputs``
-        parameters are added.
-    """
-
-    def __init__(self, problem):
-        super(CauchyLogLikelihood, self).__init__(problem)
-
-        # Get number of times, number of outputs
-        self._nt = len(self._times)
-        self._no = problem.n_outputs()
-
-        # Add parameters to problem (one for each output)
-        self._n_parameters = problem.n_parameters() + self._no
-
-        # Pre-calculate
-        self._n = len(self._times)
-        self._n_log_pi = self._n * np.log(np.pi)
-
-    def __call__(self, x):
-        # For multiparameter problems the parameters are stored as
-        # (model_params_1, model_params_2, ..., model_params_k,
-        # sigma_1, sigma_2,...)
-        n = self._n
-        m = self._no
-
-        # problem parameters
-        problem_parameters = x[:-m]
-        error = self._values - self._problem.evaluate(problem_parameters)
-
-        # Distribution parameters
-        sigma = np.asarray(x[-m:])
-
-        # Calculate
-        return np.sum(
-            - self._n_log_pi
-            - n * np.log(sigma)
-            - np.sum(np.log(1 + (error / sigma)**2), axis=0)
-        )
-
-
 class GaussianKnownSigmaLogLikelihood(pints.ProblemLogLikelihood):
     r"""
     Calculates a log-likelihood assuming independent Gaussian noise at each

Add score function 'RRMSE'?
Sometime when I compare different output where the absolute magnitude does not really matter, I prefer to have a 'relative' RMSE score, which is simply `RMSE divided by RMSD of observed data to zero`. Maybe worth adding to PINTS?
diff --git a/pints/tests/test_error_measures.py b/pints/tests/test_error_measures.py
index 25511b74..a1e6861a 100755
--- a/pints/tests/test_error_measures.py
+++ b/pints/tests/test_error_measures.py
@@ -256,6 +256,28 @@ class TestErrorMeasures(unittest.TestCase):
         self.assertEqual(dex[0], -1)
         self.assertEqual(dex[1], -6)
 
+    def test_normalised_root_mean_squared_error(self):
+        # Tests :class:`pints.NormalisedRootMeanSquaredError`.
+
+        p = MiniProblem()
+        e = pints.NormalisedRootMeanSquaredError(p)
+        self.assertEqual(e.n_parameters(), 3)
+        float(e([1, 2, 3]))
+        self.assertEqual(e([-1, 2, 3]), 0)
+        self.assertNotEqual(np.all(e([1, 2, 3])), 0)
+        x = [0, 0, 0]
+        y = 1.0
+        self.assertEqual(e(x), y)
+        x = [1, 1, 1]
+        y = np.sqrt((4 + 1 + 4) / 3) / np.sqrt((1 + 4 + 9) / 3)
+        self.assertAlmostEqual(e(x), y)
+
+        p = MultiMiniProblem()
+        self.assertRaisesRegex(
+            ValueError,
+            'This measure is only defined for single output problems.',
+            pints.NormalisedRootMeanSquaredError, p)
+
     def test_probability_based_error(self):
         # Tests :class:`pints.ProbabilityBasedError`.
 
@@ -290,7 +312,10 @@ class TestErrorMeasures(unittest.TestCase):
         self.assertEqual(e(x), y)
 
         p = MultiMiniProblem()
-        self.assertRaises(ValueError, pints.RootMeanSquaredError, p)
+        self.assertRaisesRegex(
+            ValueError,
+            'This measure is only defined for single output problems.',
+            pints.RootMeanSquaredError, p)
 
     def test_sum_of_squares_error_single(self):
         # Tests :class:`pints.MeanSquaredError` with a single output.

pip install -e .[dev,docs]
parse_log_pytest
requirements.txt
pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio pytest
apt-get update apt-get install -y gcc
3.9
requirements.txt
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
alabaster==0.7.16
anyio==4.9.0
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.3.0
asttokens==3.0.0
async-lru==2.0.5
attrs==25.3.0
babel==2.17.0
beautifulsoup4==4.13.3
bleach==6.2.0
certifi==2025.1.31
cffi==1.17.1
charset-normalizer==3.4.1
cma==4.0.0
comm==0.2.2
contourpy==1.3.0
coverage==7.8.0
cycler==0.12.1
debugpy==1.8.13
decorator==5.2.1
defusedxml==0.7.1
docutils==0.21.2
exceptiongroup==1.2.2
execnet==2.1.1
executing==2.2.0
fastjsonschema==2.21.1
flake8==7.2.0
fonttools==4.56.0
fqdn==1.5.1
h11==0.14.0
httpcore==1.0.7
httpx==0.28.1
idna==3.10
imagesize==1.4.1
importlib_metadata==8.6.1
importlib_resources==6.5.2
iniconfig==2.1.0
ipykernel==6.29.5
ipython==8.18.1
ipywidgets==8.1.5
isoduration==20.11.0
jedi==0.19.2
Jinja2==3.1.6
json5==0.10.0
jsonpointer==3.0.0
jsonschema==4.23.0
jsonschema-specifications==2024.10.1
jupyter==1.1.1
jupyter-console==6.6.3
jupyter-events==0.12.0
jupyter-lsp==2.2.5
jupyter_client==8.6.3
jupyter_core==5.7.2
jupyter_server==2.15.0
jupyter_server_terminals==0.5.3
jupyterlab==4.3.6
jupyterlab_pygments==0.3.0
jupyterlab_server==2.27.3
jupyterlab_widgets==3.0.13
kiwisolver==1.4.7
MarkupSafe==3.0.2
matplotlib==3.9.4
matplotlib-inline==0.1.7
mccabe==0.7.0
mistune==3.1.3
nbclient==0.10.2
nbconvert==7.16.6
nbformat==5.10.4
nest-asyncio==1.6.0
notebook==7.3.3
notebook_shim==0.2.4
numpy==2.0.2
overrides==7.7.0
packaging==24.2
pandocfilters==1.5.1
parso==0.8.4
pexpect==4.9.0
pillow==11.1.0
-e git+https://github.com/pints-team/pints.git@df686eee52bfff4c3c03af8fd980bb68215515f0#egg=pints
platformdirs==4.3.7
pluggy==1.5.0
prometheus_client==0.21.1
prompt_toolkit==3.0.50
psutil==7.0.0
ptyprocess==0.7.0
pure_eval==0.2.3
pycodestyle==2.13.0
pycparser==2.22
pyflakes==3.3.1
Pygments==2.19.1
pyparsing==3.2.3
pytest==8.3.5
pytest-asyncio==0.26.0
pytest-cov==6.0.0
pytest-mock==3.14.0
pytest-xdist==3.6.1
python-dateutil==2.9.0.post0
python-json-logger==3.3.0
PyYAML==6.0.2
pyzmq==26.3.0
referencing==0.36.2
requests==2.32.3
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rpds-py==0.24.0
scipy==1.13.1
Send2Trash==1.8.3
six==1.17.0
sniffio==1.3.1
snowballstemmer==2.2.0
soupsieve==2.6
Sphinx==7.4.7
sphinxcontrib-applehelp==2.0.0
sphinxcontrib-devhelp==2.0.0
sphinxcontrib-htmlhelp==2.1.0
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==2.0.0
sphinxcontrib-serializinghtml==2.0.0
stack-data==0.6.3
tabulate==0.9.0
terminado==0.18.1
tinycss2==1.4.0
tomli==2.2.1
tornado==6.4.2
traitlets==5.14.3
types-python-dateutil==2.9.0.20241206
typing_extensions==4.13.0
uri-template==1.3.0
urllib3==2.3.0
wcwidth==0.2.13
webcolors==24.11.1
webencodings==0.5.1
websocket-client==1.8.0
widgetsnbextension==4.0.13
zipp==3.21.0

name: pints
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - ca-certificates=2025.2.25=h06a4308_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.4.4=h6a678d5_1
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - ncurses=6.4=h6a678d5_0
  - openssl=3.0.16=h5eee18b_0
  - pip=25.0=py39h06a4308_0
  - python=3.9.21=he870216_1
  - readline=8.2=h5eee18b_0
  - setuptools=75.8.0=py39h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - tzdata=2025a=h04d1e81_0
  - wheel=0.45.1=py39h06a4308_0
  - xz=5.6.4=h5eee18b_1
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - alabaster==0.7.16
      - anyio==4.9.0
      - argon2-cffi==23.1.0
      - argon2-cffi-bindings==21.2.0
      - arrow==1.3.0
      - asttokens==3.0.0
      - async-lru==2.0.5
      - attrs==25.3.0
      - babel==2.17.0
      - beautifulsoup4==4.13.3
      - bleach==6.2.0
      - certifi==2025.1.31
      - cffi==1.17.1
      - charset-normalizer==3.4.1
      - cma==4.0.0
      - comm==0.2.2
      - contourpy==1.3.0
      - coverage==7.8.0
      - cycler==0.12.1
      - debugpy==1.8.13
      - decorator==5.2.1
      - defusedxml==0.7.1
      - docutils==0.21.2
      - exceptiongroup==1.2.2
      - execnet==2.1.1
      - executing==2.2.0
      - fastjsonschema==2.21.1
      - flake8==7.2.0
      - fonttools==4.56.0
      - fqdn==1.5.1
      - h11==0.14.0
      - httpcore==1.0.7
      - httpx==0.28.1
      - idna==3.10
      - imagesize==1.4.1
      - importlib-metadata==8.6.1
      - importlib-resources==6.5.2
      - iniconfig==2.1.0
      - ipykernel==6.29.5
      - ipython==8.18.1
      - ipywidgets==8.1.5
      - isoduration==20.11.0
      - jedi==0.19.2
      - jinja2==3.1.6
      - json5==0.10.0
      - jsonpointer==3.0.0
      - jsonschema==4.23.0
      - jsonschema-specifications==2024.10.1
      - jupyter==1.1.1
      - jupyter-client==8.6.3
      - jupyter-console==6.6.3
      - jupyter-core==5.7.2
      - jupyter-events==0.12.0
      - jupyter-lsp==2.2.5
      - jupyter-server==2.15.0
      - jupyter-server-terminals==0.5.3
      - jupyterlab==4.3.6
      - jupyterlab-pygments==0.3.0
      - jupyterlab-server==2.27.3
      - jupyterlab-widgets==3.0.13
      - kiwisolver==1.4.7
      - markupsafe==3.0.2
      - matplotlib==3.9.4
      - matplotlib-inline==0.1.7
      - mccabe==0.7.0
      - mistune==3.1.3
      - nbclient==0.10.2
      - nbconvert==7.16.6
      - nbformat==5.10.4
      - nest-asyncio==1.6.0
      - notebook==7.3.3
      - notebook-shim==0.2.4
      - numpy==2.0.2
      - overrides==7.7.0
      - packaging==24.2
      - pandocfilters==1.5.1
      - parso==0.8.4
      - pexpect==4.9.0
      - pillow==11.1.0
      - platformdirs==4.3.7
      - pluggy==1.5.0
      - prometheus-client==0.21.1
      - prompt-toolkit==3.0.50
      - psutil==7.0.0
      - ptyprocess==0.7.0
      - pure-eval==0.2.3
      - pycodestyle==2.13.0
      - pycparser==2.22
      - pyflakes==3.3.1
      - pygments==2.19.1
      - pyparsing==3.2.3
      - pytest==8.3.5
      - pytest-asyncio==0.26.0
      - pytest-cov==6.0.0
      - pytest-mock==3.14.0
      - pytest-xdist==3.6.1
      - python-dateutil==2.9.0.post0
      - python-json-logger==3.3.0
      - pyyaml==6.0.2
      - pyzmq==26.3.0
      - referencing==0.36.2
      - requests==2.32.3
      - rfc3339-validator==0.1.4
      - rfc3986-validator==0.1.1
      - rpds-py==0.24.0
      - scipy==1.13.1
      - send2trash==1.8.3
      - six==1.17.0
      - sniffio==1.3.1
      - snowballstemmer==2.2.0
      - soupsieve==2.6
      - sphinx==7.4.7
      - sphinxcontrib-applehelp==2.0.0
      - sphinxcontrib-devhelp==2.0.0
      - sphinxcontrib-htmlhelp==2.1.0
      - sphinxcontrib-jsmath==1.0.1
      - sphinxcontrib-qthelp==2.0.0
      - sphinxcontrib-serializinghtml==2.0.0
      - stack-data==0.6.3
      - tabulate==0.9.0
      - terminado==0.18.1
      - tinycss2==1.4.0
      - tomli==2.2.1
      - tornado==6.4.2
      - traitlets==5.14.3
      - types-python-dateutil==2.9.0.20241206
      - typing-extensions==4.13.0
      - uri-template==1.3.0
      - urllib3==2.3.0
      - wcwidth==0.2.13
      - webcolors==24.11.1
      - webencodings==0.5.1
      - websocket-client==1.8.0
      - widgetsnbextension==4.0.13
      - zipp==3.21.0
prefix: /opt/conda/envs/pints

pints/tests/test_error_measures.py::TestErrorMeasures::test_normalised_root_mean_squared_error
pints/tests/test_error_measures.py::TestErrorMeasures::test_mean_squared_error_multi pints/tests/test_error_measures.py::TestErrorMeasures::test_mean_squared_error_single pints/tests/test_error_measures.py::TestErrorMeasures::test_mean_squared_error_weighted pints/tests/test_error_measures.py::TestErrorMeasures::test_sum_of_errors
pints/tests/test_error_measures.py::TestErrorMeasures::test_probability_based_error pints/tests/test_error_measures.py::TestErrorMeasures::test_root_mean_squared_error pints/tests/test_error_measures.py::TestErrorMeasures::test_sum_of_squares_error_multi pints/tests/test_error_measures.py::TestErrorMeasures::test_sum_of_squares_error_single pints/tests/test_error_measures.py::TestErrorMeasures::test_sum_of_squares_error_weighted
docs/source/error_measures.rst pints/_log_likelihoods.py pints/_error_measures.py pints/__init__.py
docs/source/error_measures.rst pints/_log_likelihoods.py pints/_error_measures.py pints/__init__.py