Correct bullet lists. That should be it.

Author: MaxBlesch

docs/source/estimation.rst

@@ -46,13 +46,13 @@ The estimation process
 **********************
 
 The estimation process is not directly implemented in ruspy. The package only contains
-the likelihood functions and in case of MPEC also the constraints. For the (minimization)
-maximization of the (negative) loglikelihood function, an external optimization library
-has to be used. Hence ruspy is a so called model package. OpenSourceEconomics offers
-several of these model packages, all for different models. More information can be found
-on our `homepage <https://open-econ.org>`_. The central function to get the criterion
-function, it's derivative and if applicable the constraint is the
-``get_criterion_function`` function. It's source code can be found here:
+the likelihood functions and in case of MPEC also the constraints. For the
+(minimization) maximization of the (negative) loglikelihood function, an external
+optimization library has to be used. Hence ruspy is a so called model package.
+OpenSourceEconomics offers several of these model packages, all for different models.
+More information can be found on our `homepage <https://open-econ.org>`_. The central
+function to get the criterion function, it's derivative and if applicable the constraint
+is the ``get_criterion_function`` function. It's source code can be found here:
 
 .. currentmodule:: ruspy.estimation.criterion_function
 
@@ -86,9 +86,11 @@ following mandatory keys:
 
 - **num_states :** *(int)* The size of the state space as integer.
 
-- **maint_cost_func :** *(string)* The name of the maintenance cost function. See :ref:`maint_func` for details.
+- **maint_cost_func :** *(string)* Name of the cost function. See :ref:`maint_func`
+  for details.
 
-- **cost_scale :** *(float)* The scale for the maintenance costs. See :ref:`scale` for details.
+- **cost_scale :** *(float)* The scale for the maintenance costs. See :ref:`scale`
+  for details.
 
 
 Under the key **method** the method of estimation has to be specified as a *(string)*:
@@ -167,7 +169,7 @@ The dictionary containing the transition estimation results has the following ke
 
 - **x :** *(numpy.array)* Estimated transition probabilities.
 
-- ** trans_count :** *(numpy.array)* Counted state increases for each array index.
+- **trans_count :** *(numpy.array)* Counted state increases for each array index.
 
 
 So far only a pooled estimation of the transitions is possible. Hence, ``ruspy``
@@ -352,8 +354,8 @@ Demonstration
 In the tutorials are two demonstration jupyter notebooks of the cost estimation process.
 The `replication <tutorials/replication/replication.ipynb>`_ notebook allows to easily
 experiment with the methods described here as well as the implied demand function.
-The notebook can also be downloaded from the promotion folder of the
-`repository <https://github.com/OpenSourceEconomics/ruspy/tree/master/promotion/replication>`_.
+The notebook can also be downloaded from the tutorials folder of the
+`repository <https://github.com/OpenSourceEconomics/ruspy/tree/master/docs/source/replication>`_.
 If you have have everything setup, then it should be easy to run it.
 For a more advanced set up have a look at the `replication of Iskhakov et al. (2016)
 <tutorials/replication/replication_iskhakov_et_al_2016.ipynb>`_.

docs/source/model_code.rst

@@ -181,17 +181,18 @@ allow to specify, when the algorithm switches from contraction to Newton-Kantoro
 iterations and general parameters, which let the algorithm stop. So far, there is no
 switching back implemented.
 
-**max_cont_steps :** *(int)* The maximum number of contraction iterations before
-switching to Newton-Kantorovich iterations. Default is 20.
+- **max_cont_steps :** *(int)* The maximum number of contraction iterations before
+  switching to Newton-Kantorovich iterations. Default is 20.
 
-**switch_tol :** *(float)* If this threshold is reached by contraction iterations, then
-the algorithm switches to Newton-Kantorovich iterations. Default is :math:`10^{-3}`.
+- **switch_tol :** *(float)* If this threshold is reached by contraction iterations,
+  then the algorithm switches to Newton-Kantorovich iterations. Default is
+  :math:`10^{-3}`.
 
-**max_newt_kant_steps :** *(int)* The maximum number of Newton-Kantorovich iterations
-before the algorithm stops. Default is 20.
+- **max_newt_kant_steps :** *(int)* The maximum number of Newton-Kantorovich iterations
+  before the algorithm stops. Default is 20.
 
-**threshold :** *(float)* If this threshold is reached by Newton-Kantorovich iterations,
-then the algorithm stops. Default is :math:`10^{-12}`.
+- **threshold :** *(float)* If this threshold is reached by Newton-Kantorovich
+  iterations, then the algorithm stops. Default is :math:`10^{-12}`.
 
 .. _ev:
 
@@ -292,23 +293,22 @@ This dictionairy provides all the necessary information about how the demand
 function is supposed to look like and how precisely it is supposed to be calculated.
 It has to hold the following keys:
 
-**RC_lower_bound :** *(float)* The lowest replacement cost for which the demand
-is supposed to be calculated.
+- **RC_lower_bound :** *(float)* The lowest replacement cost for which the demand is
+  supposed to be calculated.
 
-**RC_upper_bound :** *(float)* The highest replacement cost for which the demand
-is supposed to be calculated.
+- **RC_upper_bound :** *(float)* The highest replacement cost for which the demand is
+  supposed to be calculated.
 
-**demand_evaluations :** *(int)* The grid size of the replacement cost between
-RC_lower_bound and RC_upper_bound for which the demand level shall be calculated.
+- **demand_evaluations :** *(int)* The grid size of the replacement cost between
+  RC_lower_bound and RC_upper_bound for which the demand level shall be calculated.
 
-**tolerance :** *(float)* The stopping tolerance for the fixed point calculation
-needed to obtain each demand level.
+- **tolerance :** *(float)* The stopping tolerance for the fixed point calculation
+  needed to obtain each demand level.
 
-**num_periods :** *(int)* Number of months :math:`T` for which the expected demand
-is derived. Consequently, set it to 12 if you want to get the annual expected demand.
+- **num_periods :** *(int)* Number of months :math:`T` for which the expected demand
+  is derived. Consequently, set it to 12 if you want to get the annual expected demand.
 
-**num_buses :** *(int)* Number of buses :math:`M` for which the demand is
-calculated.
+- **num_buses :** *(int)* Number of buses :math:`M` for which the demand is calculated.
 
 
 .. _demand_params:
@@ -342,5 +342,4 @@ which indicates whether the fixed point algorithm converged successfully.
 
 
 The use of the ``get_demand`` function is shown in the following `replication
-<https://github.com/OpenSourceEconomics/ruspy/blob/kantevorich/promotion
-/replication/replication.ipynb>`_ notebook.
+<tutorials/replication/replication.ipynb>`_ notebook.

docs/source/simulation.rst

@@ -29,14 +29,15 @@ Simulation initialization dictionary
 
 The initialization dictionary contains the following keys:
 
-**seed :** *(int)* A positive integer setting the random seed for drawing random
-numbers. If none given, some random seed is drawn.
+- **seed :** *(int)* A positive integer setting the random seed for drawing random
+  numbers. If none given, some random seed is drawn.
 
-**discount_factor :** *(float)* See :ref:`disc_fac` for more details.
+- **discount_factor :** *(float)* See :ref:`disc_fac` for more details.
 
-**buses :** *(int)* The number of buses to be simulated.
+- **buses :** *(int)* The number of buses to be simulated.
+
+- **periods :** *(int)* The number of periods to be simulated.
 
-**periods :** *(int)* The number of periods to be simulated.
 
 
 The simulation process
@@ -82,8 +83,7 @@ data is a subset of the returned Dataframe.
 Demonstration
 -------------
 
-The `simulation <tutorials/simulation/simulation_convergence.ipynb>`_ notebook allows to easily
-experiment with the estimation methods described here. The notebook can be downloaded
-from the `repository <https://github.com/OpenSourceEconomics/ruspy/blob/master/promotion
-/simulation/simulation_convergence.ipynb>`_. If you have have everything
-setup, then it should be easy to run it.
+The `simulation notebook <tutorials/simulation/simulation_convergence.ipynb>`_ allows to
+easily experiment with the estimation methods described here. The notebook can be
+downloaded from the `simulation folder <tutorials/simulation>`_ in the tutorials of
+ruspy. If you have have everything setup, then it should be easy to run it.