docs: remove howtofit/ tree and point tutorials to standalone repo

README.rst

@@ -34,7 +34,7 @@ PyAutoFit: Classy Probabilistic Programming
 `Installation Guide <https://pyautofit.readthedocs.io/en/latest/installation/overview.html>`_ |
 `readthedocs <https://pyautofit.readthedocs.io/en/latest/index.html>`_ |
 `Introduction on Colab <https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.4.13.6/notebooks/overview/overview_1_the_basics.ipynb>`_ |
-`HowToFit <https://pyautofit.readthedocs.io/en/latest/howtofit/howtofit.html>`_
+`HowToFit <https://github.com/PyAutoLabs/HowToFit>`_
 
 **PyAutoFit** is a Python based probabilistic programming language for model fitting and Bayesian inference
 of large datasets.
@@ -57,7 +57,9 @@ The following links are useful for new starters:
 
 - `The introduction Jupyter Notebook on Colab <https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.4.13.6/notebooks/overview/overview_1_the_basics.ipynb>`_, where you can try **PyAutoFit** in a web browser (without installation).
 
-- `The autofit_workspace GitHub repository <https://github.com/Jammy2211/autofit_workspace>`_, which includes example scripts and the `HowToFit Jupyter notebook lectures <https://github.com/Jammy2211/autofit_workspace/tree/main/notebooks/howtofit>`_ which give new users a step-by-step introduction to **PyAutoFit**.
+- `The autofit_workspace GitHub repository <https://github.com/Jammy2211/autofit_workspace>`_, which includes example scripts demonstrating **PyAutoFit**'s features.
+
+- `The standalone HowToFit repository <https://github.com/PyAutoLabs/HowToFit>`_, a series of Jupyter notebook lectures which give new users a step-by-step introduction to **PyAutoFit**.
 
 Support
 -------
@@ -76,7 +78,7 @@ For users less familiar with Bayesian inference and scientific analysis you may
 the **HowToFits** lectures. These teach you the basic principles of Bayesian inference, with the
 content pitched at undergraduate level and above.
 
-A complete overview of the lectures `is provided on the HowToFit readthedocs page <https://pyautofit.readthedocs.io/en/latest/howtofit/howtofit.htmll>`_
+The lectures are available in the `standalone HowToFit repository <https://github.com/PyAutoLabs/HowToFit>`_.
 
 API Overview
 ------------

docs/api/analysis.rst

@@ -11,7 +11,7 @@ It acts as an interface between the data, model and the non-linear search.
 - `readthedocs: example using Analysis object <https://pyautofit.readthedocs.io/en/latest/overview/model_fit.html>`_.
 - `autofit_workspace: simple tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/simple/fit.ipynb>`_
 - `autofit_workspace: complex tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/complex/fit.ipynb>`_
-- `HowToFit: introduction chapter (detailed step-by-step examples) <https://pyautofit.readthedocs.io/en/latest/howtofit/chapter_1_introduction.html>`_
+- `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 --------
 Analysis

docs/api/database.rst

@@ -10,7 +10,7 @@ inspection, analysis and interpretation.
 
 - `readthedocs: example using database functionality <https://pyautofit.readthedocs.io/en/latest/features/database.html>`_
 - `autofit_workspace: tutorial using database <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/features/database.ipynb>`_
-- `HowToFit: database chapter (detailed step-by-step examples) <https://pyautofit.readthedocs.io/en/latest/howtofit/chapter_database.html>`_
+- `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 ----------
 Aggregator

docs/api/model.rst

@@ -14,7 +14,7 @@ It is recommended the `model API cookbooks <https://pyautofit.readthedocs.io/en/
 - `readthedocs: example using Collection object <https://pyautofit.readthedocs.io/en/latest/overview/model_complex.html>`_.
 - `autofit_workspace: simple tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/simple/fit.ipynb>`_
 - `autofit_workspace: complex tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/complex/fit.ipynb>`_
-- `HowToFit: introduction chapter (detailed step-by-step examples) <https://pyautofit.readthedocs.io/en/latest/howtofit/chapter_1_introduction.html>`_
+- `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 ------
 Models

docs/api/plot.rst

@@ -9,7 +9,7 @@ by **PyAutoFit**.
 
 - `readthedocs: non-linear search example <https://pyautofit.readthedocs.io/en/latest/overview/non_linear_search.html>`_
 - `autofit_workspace: plot tutorials <https://github.com/Jammy2211/autofit_workspace/tree/release/notebooks/plot>`_
-- `HowToFit: introduction chapter (detailed step-by-step examples) <https://pyautofit.readthedocs.io/en/latest/howtofit/chapter_1_introduction.html>`_
+- `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 --------
 Plotters

docs/api/priors.rst

@@ -12,7 +12,7 @@ The priors of parameters of every component of a mdoel, which is fitted to data,
 - `readthedocs: example using Collection object <https://pyautofit.readthedocs.io/en/latest/overview/model_complex.html>`_.
 - `autofit_workspace: simple tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/simple/fit.ipynb>`_
 - `autofit_workspace: complex tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/complex/fit.ipynb>`_
-- `HowToFit: introduction chapter (detailed step-by-step examples) <https://pyautofit.readthedocs.io/en/latest/howtofit/chapter_1_introduction.html>`_
+- `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 Priors
 ------

docs/api/samples.rst

@@ -12,7 +12,7 @@ For example, for an MCMC model-fit, the ``Samples`` objects contains every sampl
 - `readthedocs: example on using results <https://pyautofit.readthedocs.io/en/latest/overview/result.html>`_.
 - `autofit_workspace: simple results tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/simple/result.ipynb>`_
 - `autofit_workspace: complex result tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/complex/result.ipynb>`_
-- `HowToFit: introduction chapter (detailed step-by-step examples) <https://pyautofit.readthedocs.io/en/latest/howtofit/chapter_1_introduction.html>`_
+- `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 Samples
 -------

docs/api/searches.rst

@@ -12,7 +12,7 @@ Markov Chain Monte Carlo (MCMC) and Maximum Likelihood Estimators (MLE).
 - `readthedocs: example using non-linear searches <https://pyautofit.readthedocs.io/en/latest/overview/non_linear_search.html>`_.
 - `autofit_workspace: simple tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/simple/fit.ipynb>`_
 - `autofit_workspace: complex tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/complex/fit.ipynb>`_
-- `HowToFit: introduction chapter (detailed step-by-step examples) <https://pyautofit.readthedocs.io/en/latest/howtofit/chapter_1_introduction.html>`_
+- `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 Nested Samplers
 ---------------

docs/cookbooks/multiple_datasets.rst

@@ -605,7 +605,7 @@ from large datasets.
 
 **PyAutoFit** has a dedicated feature set for fitting hierarchical and graphical models and interested readers should
 checkout the hierarchical and graphical modeling
-chapter of **HowToFit** (https://pyautofit.readthedocs.io/en/latest/howtofit/chapter_graphical_models.html)
+chapter of **HowToFit** (https://github.com/PyAutoLabs/HowToFit/blob/main/notebooks/chapter_3_graphical_models)
 
 Interpolation
 -------------

docs/features/graphical.rst

@@ -67,7 +67,8 @@ This is what our three Gaussians look like:
   :alt: Alternative text
 
 They are much lower signal-to-noise than the Gaussian's in other examples. Graphical models extract a lot more information
-from lower quantity datasets, something we demonstrate explic in the **HowToFit** lectures on graphical models.
+from lower quantity datasets, something we demonstrate explic in the `HowToFit lectures on graphical models
+<https://github.com/PyAutoLabs/HowToFit/blob/main/notebooks/chapter_3_graphical_models>`_.
 
 For each dataset we now create a corresponding ``Analysis`` class. By associating each dataset with an ``Analysis``
 class we are therefore associating it with a unique ``log_likelihood_function``. If our dataset had many different
@@ -164,9 +165,10 @@ We can now choose a non-linear search and fit the factor graph.
 
 This will fit the N=7 dimension parameter space where every Gaussian has a shared centre!
 
-This is all expanded upon in the **HowToFit** chapter on graphical models, where we will give a more detailed
-description of why this approach to model-fitting extracts a lot more information than fitting each dataset
-one-by-one.
+This is all expanded upon in the `HowToFit chapter on graphical models
+<https://github.com/PyAutoLabs/HowToFit/blob/main/notebooks/chapter_3_graphical_models>`_, where we will give a
+more detailed description of why this approach to model-fitting extracts a lot more information than fitting each
+dataset one-by-one.
 
 Expectation Propagation
 -----------------------

docs/general/workspace.rst

@@ -16,12 +16,11 @@ New users should begin by checking out the following parts of the workspace.
 HowToFit
 --------
 
-The **HowToFit** lecture series are a collection of Jupyter notebooks describing how to build a **PyAutoFit** model
-fitting project and giving illustrations of different statistical methods and techniiques.
+The **HowToFit** lecture series is a collection of Jupyter notebooks describing how to build a **PyAutoFit** model
+fitting project and giving illustrations of different statistical methods and techniques.
 
-Checkout the
-`tutorials section <https://pyautofit.readthedocs.io/en/latest/howtofit/howtofit.html>`_ for a
-full description of the lectures and online examples of every notebook.
+HowToFit now lives in its own standalone repository at `PyAutoLabs/HowToFit <https://github.com/PyAutoLabs/HowToFit>`_.
+Clone or browse the repo for a full description of the lectures and the notebooks for every chapter.
 
 Scripts / Notebooks
 -------------------