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100 changes: 51 additions & 49 deletions README.rst
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Expand Up @@ -51,38 +51,26 @@ Features
model (RLDDM), including a module for estimating the impact of continuous regressors onto RLDDM parameters, and a reinforcement learning
(RL) model. See tutorial for the RLDDM and RL modules here: https://nbviewer.jupyter.org/github/hddm-devs/hddm/blob/master/hddm/examples/demo_RLHDDMtutorial.ipynb and in the paper here: https://rdcu.be/b4q6Z

* HDDM 0.9.0 brings a host of new features.
HDDM includes `likelihood approximation networks`_ via the **HDDMnn**, **HDDMnnRegressor** and **HDDMnnStimCoding** classes.
This allows fitting of a number of variants of sequential sampling models. You can now easily use custom likelihoods
for model fitting. We included a range of new **simulators**, which allow data generation for a host of variants of sequential sampling models.
There are some new out of the box **plots**, in the **hddm.plotting** module. Fast posterior predictives for regression based models.
Some sampler settings are now exposed to the user via a customizable **model_config dictionary**. Lastly you are now able to save and load **HDDMRegression** models with
custom link functions. Please see the **documentation** (under **LAN Extension**) for illustrations on how to use the new features.

* HDDM 0.9.1 improved documentation for LAN models.
Comprehensive tutorial using LAN included. Bugfixes for ``simulator_h_c()`` function.

* HDDM 0.9.2 major overhaul of the plotting functions under hddm.plotting.
Old capabilities are preserved under ``hddm.plotting_old``, but will be deprecated.
The new plotting functions replicate the existing functionality, but improve on various aspects of the plot and provide a more abstracted and extensible interface.
Fixes an error with posterior predictive sampling using hierarchical regression models based on LANs with ``HDDMnnRegressor()``. ``HDDMnnRegressor()`` now issues a
single warning for boundary condition violations instead of flagging all occurences.
* HDDM 0.9.8 adds a **breaking change**.
To accommodate some user requests, **all parameters** that should be estimated for a given model should be made explicitly
in the *include* argument of the calling class (HDDM, HDDMnn, etc.). The remaining parameters can now be set to arbitrary, user defined, defaults.
Check the documentation for a **new tutorial on parameter defaults**.
Moreover, **model plot** is made much more flexible (new tutorial included to showcase some of the options).
Two **tutorials** are added to showcase the capabilities for simulation based inference via **custom likelihoods**.
The legacy models with "vanilla" in their name are globally renamed to instead include "hddm_base".
The simulator backend is now completely outsourced to the `ssms`_ package (severe code simplifications).

* HDDM 0.9.3 Lot's of minor improvements.
Model plots now working for race and lca models with n > 2 choices (use **_plot_func_model_n** as **plot_func** argument in **hddm.plotting.plot_posterior_predictive**).
**model_config** files are simplified and class construction is a bit more robust to lack of specification, improving ease of use with custom models.
Various plots received a bit more styling features.
Better defaults for **simulator_h_c** function in **hddm.simulators.hddm_dataset_generators**.
Posterior predictives now properly take into account the *p_outlier* parameter when generating data from the implicit mixture model. *p_outlier* percent of the data,
now explicitly come from random choices with uniform reaction times.
The documentation is updated to reflect / illustrate the improvements.
* HDDM 0.9.7 adds the **HDDMnnRLRegressor** class, the equivalent to the **HDDMrlRegressor** with support for many more *SSMs* via *LANs*.
Please check the documentation for usage examples.

* HDDM 0.9.4 Bug fixes and one major new functionality.
**HDDMnnRegressor** now allows you to define **indirect regressors**, latent parameters that are driven by their own regression and link to model parameters.
See the documentation for more information on this. **Note** this functionality is experimental for now. Model fitting will work, but extraenous functionality may not,
including posterior predictives for models that include such indirect regressors. Including indirect regressors might demand you to think carefully about the supplied
**model_config**. E.g. in the **race_no_bias_3** model, the usual lower bounds on the **v0, v1 ,v2, v3** parameters are 0. If we allow these parameters to be driven by an
indirect regressor **v**, which is added to the regressions of **v0, v1, v2, v3**, then **v0, v1, v2, v3**
* HDDM 0.9.6 brings a host of new features.
HDDM now includes use of `likelihood approximation networks`_ in conjunction with reinforcement learning models via the **HDDMnnRL** class.
This allows researchers to study not only the across-trial dynamics of learning but the within-trial dynamics of choice processes, using a single model.
This module greatly extends the previous functionality for fitting RL+DDM models (via HDDMrl class) by allowing fitting of a number of variants of sequential sampling models in conjuction with a learning process (RL+SSM models).
We have included a new **simulator**, which allows data generation for a host of variants of sequential sampling models in conjunction with the Rescorla-Wagner update rule on a 2-armed bandit task environment.
There are some new, out-of-the-box **plots** and **utility function** in the **hddm.plotting** and **hddm.utils** modules, respectively, to facilitate posterior visualization and posterior predictive checks.
Lastly you can also save and load **HDDMnnRL** models.
Please see the **documentation** (under **HDDMnnRL Extension**) for illustrations on how to use the new features.

* HDDM 0.9.5 Bug fixes and another new functionality.
**HDDMnnRegressor** now allows you to also define **indirect betas**, latent parameters that can be used in regression models.
Expand All @@ -105,26 +93,40 @@ Features
Note also that the usage of **indirect betas** as well as **indirect regressors** may affect the speed of sampling in general.
Both translate into more computational work at the stage of regression likelihood evaluation.

* HDDM 0.9.6 brings a host of new features.
HDDM now includes use of `likelihood approximation networks`_ in conjunction with reinforcement learning models via the **HDDMnnRL** class.
This allows researchers to study not only the across-trial dynamics of learning but the within-trial dynamics of choice processes, using a single model.
This module greatly extends the previous functionality for fitting RL+DDM models (via HDDMrl class) by allowing fitting of a number of variants of sequential sampling models in conjuction with a learning process (RL+SSM models).
We have included a new **simulator**, which allows data generation for a host of variants of sequential sampling models in conjunction with the Rescorla-Wagner update rule on a 2-armed bandit task environment.
There are some new, out-of-the-box **plots** and **utility function** in the **hddm.plotting** and **hddm.utils** modules, respectively, to facilitate posterior visualization and posterior predictive checks.
Lastly you can also save and load **HDDMnnRL** models.
Please see the **documentation** (under **HDDMnnRL Extension**) for illustrations on how to use the new features.
* HDDM 0.9.4 Bug fixes and one major new functionality.
**HDDMnnRegressor** now allows you to define **indirect regressors**, latent parameters that are driven by their own regression and link to model parameters.
See the documentation for more information on this. **Note** this functionality is experimental for now. Model fitting will work, but extraenous functionality may not,
including posterior predictives for models that include such indirect regressors. Including indirect regressors might demand you to think carefully about the supplied
**model_config**. E.g. in the **race_no_bias_3** model, the usual lower bounds on the **v0, v1 ,v2, v3** parameters are 0. If we allow these parameters to be driven by an
indirect regressor **v**, which is added to the regressions of **v0, v1, v2, v3**, then **v0, v1, v2, v3**

* HDDM 0.9.7 adds the **HDDMnnRLRegressor** class, the equivalent to the **HDDMrlRegressor** with support for many more *SSMs* via *LANs*.
Please check the documentation for usage examples.
* HDDM 0.9.3 Lot's of minor improvements.
Model plots now working for race and lca models with n > 2 choices (use **_plot_func_model_n** as **plot_func** argument in **hddm.plotting.plot_posterior_predictive**).
**model_config** files are simplified and class construction is a bit more robust to lack of specification, improving ease of use with custom models.
Various plots received a bit more styling features.
Better defaults for **simulator_h_c** function in **hddm.simulators.hddm_dataset_generators**.
Posterior predictives now properly take into account the *p_outlier* parameter when generating data from the implicit mixture model. *p_outlier* percent of the data,
now explicitly come from random choices with uniform reaction times.
The documentation is updated to reflect / illustrate the improvements.

* HDDM 0.9.2 major overhaul of the plotting functions under hddm.plotting.
Old capabilities are preserved under ``hddm.plotting_old``, but will be deprecated.
The new plotting functions replicate the existing functionality, but improve on various aspects of the plot and provide a more abstracted and extensible interface.
Fixes an error with posterior predictive sampling using hierarchical regression models based on LANs with ``HDDMnnRegressor()``. ``HDDMnnRegressor()`` now issues a
single warning for boundary condition violations instead of flagging all occurences.


* HDDM 0.9.1 improved documentation for LAN models.
Comprehensive tutorial using LAN included. Bugfixes for ``simulator_h_c()`` function.

* HDDM 0.9.0 brings a host of new features.
HDDM includes `likelihood approximation networks`_ via the **HDDMnn**, **HDDMnnRegressor** and **HDDMnnStimCoding** classes.
This allows fitting of a number of variants of sequential sampling models. You can now easily use custom likelihoods
for model fitting. We included a range of new **simulators**, which allow data generation for a host of variants of sequential sampling models.
There are some new out of the box **plots**, in the **hddm.plotting** module. Fast posterior predictives for regression based models.
Some sampler settings are now exposed to the user via a customizable **model_config dictionary**. Lastly you are now able to save and load **HDDMRegression** models with
custom link functions. Please see the **documentation** (under **LAN Extension**) for illustrations on how to use the new features.

* HDDM 0.9.8 adds a **breaking change**.
To accommodate some user requests, **all parameters** that should be estimated for a given model should be made explicitly
in the *include* argument of the calling class (HDDM, HDDMnn, etc.). The remaining parameters can now be set to arbitrary, user defined, defaults.
Check the documentation for a **new tutorial on parameter defaults**.
Moreover, **model plot** is made much more flexible (new tutorial included to showcase some of the options).
Two **tutorials** are added to showcase the capabilities for simulation based inference via **custom likelihoods**.
The legacy models with "vanilla" in their name are globally renamed to instead include "hddm_base".
The simulator backend is now completely outsourced to the `ssms`_ package (severe code simplifications).


Comparison to other packages
Expand Down Expand Up @@ -168,7 +170,7 @@ Installation
============

For **HDDM >= 0.9.0**, currently in beta release, the most convenient way to install HDDM, is to directly
install via **github**. In a fresh environment (we recommend to use **python 3.7**) type.
install via **github**. In a fresh environment (we recommend to use **python 3.7**) type. **Alternatively**, you can also use docker and pull the latest docker image from `here <https://hub.docker.com/repository/docker/hcp4715/hddm>`_.

We recommend you to open a **conda** environment first.

Expand Down