Emend links to docs.econ-ark.org

Documentation/overview/ARKitecture.md

@@ -38,7 +38,7 @@ Python modules in HARK can generally be categorized into three types: tools, mod
 
 ## HARK
 
-After you [installed](https://docs.econ-ark.org/quick-start.html) and [cloned the repository of](https://github.com/econ-ark/HARK) HARK, you can explore the content of it. In the subfolder HARK, you can find a range of [general purpose tools](#general-purpose-tools), as well as the next subfolder ConsumptionSaving which has [AgentType Classes](#agenttype-class) and [Market Classes](#market-class).
+After you [installed](https://docs.econ-ark.org/guides/quick_start.html) and [cloned the repository of](https://github.com/econ-ark/HARK) HARK, you can explore the content of it. In the subfolder HARK, you can find a range of [general purpose tools](#general-purpose-tools), as well as the next subfolder ConsumptionSaving which has [AgentType Classes](#agenttype-class) and [Market Classes](#market-class).
 
 ### General Purpose Tools
 
@@ -86,7 +86,7 @@ Methods for optimizing an objective function for the purposes of estimating a mo
 
 By default, processes in Python are single-threaded, using only a single CPU core. The **_HARK.parallel_** module provides basic tools for using multiple CPU cores simultaneously, with minimal effort. [^4] In particular, it provides the function **_multiThreadCommands_**, which takes two arguments: a list of **_AgentType_**s and a list of commands as strings; each command should be a method of the **_AgentType_**s. The function simply distributes the **_AgentType_**s across threads on different cores and executes each command in order, returning no output (the **_AgentType_**s themselves are changed by running the commands). Equivalent results would be achieved by simply looping over each type and running each method in the list. Indeed, **_HARK.parallel_** also has a function called **_multiThreadCommandsFake_** that does just that, with identical syntax to **_multiThreadCommands_**; multithreading in HARK can thus be easily turned on and off. [^5] The module also has functions for a parallel implementation of the Nelder-Mead simplex algorithm, as described in Wiswall and Lee (2011). See [here](https://docs.econ-ark.org/reference/tools/parallel.html) for full documentation.
 
-[^4]: **_HARK.parallel_** uses two packages that aren't included in the default distribution of Anaconda: **_joblib_** and **_dill_**; see [here](https://docs.econ-ark.org/quick-start.html#using-hark-with-anaconda) for instructions on how to install them.
+[^4]: **_HARK.parallel_** uses two packages that aren't included in the default distribution of Anaconda: **_joblib_** and **_dill_**; see [here](https://docs.econ-ark.org/guides/quick_start.html#using-hark-with-anaconda) for instructions on how to install them.
 
 [^5]: In the future, **_HARK.parallel_** might be absorbed into **_HARK.core_** and **_HARK.estimation_**, particularly if **_joblib_** and **_dill_** become part of the standard Anaconda distribution.
 

examples/Journeys/Journey-PhD.ipynb

@@ -45,7 +45,7 @@
     "M_{t+1} &=& R (M_{t}-C_{t}) + Y_t, \\\\\n",
     "\\end{eqnarray*}\n",
     "\n",
-    "Finding a distribution of agent assets (consumption, savings) must involve much more advanced numerical tools than in the representative agent setting. This is more demanding task to accomplish and master. Moreover, the knowledge about involved numerical methods is less systematic, and often hard to find. To quote the HARK [Documentation](https://docs.econ-ark.org/introduction.html):\n",
+    "Finding a distribution of agent assets (consumption, savings) must involve much more advanced numerical tools than in the representative agent setting. This is more demanding task to accomplish and master. Moreover, the knowledge about involved numerical methods is less systematic, and often hard to find. To quote the HARK [Documentation](https://docs.econ-ark.org/overview/introduction.html):\n",
     "\n",
     "*\"After months of effort, you may have had the character-improving experience of\n",
     "proudly explaining to your adviser that not only had you grafted two ideas\n",
@@ -424,7 +424,7 @@
     "\n",
     "The Market class was designed to be a general framework for many different macro models. It involves a procedure of aggregating the agents' choices: eg. aggregating consumption and savings (`reap_vars` in the code) and then transforming the aggregated variables (`mill_rule` in the code).\n",
     "\n",
-    "If you would like to get better knowledge about this structure, first take a look at the [Hark documentation](https://docs.econ-ark.org/ARKitecture.html). Next, to understand how the HARK Market class works in less standard setting, look at the [Fashion victim model](https://github.com/econ-ark/DemARK/blob/99948acb7b59cc9a6fb7de758972266fa4b03a06/notebooks/Fashion-Victim-Model.ipynb).\n"
+    "If you would like to get better knowledge about this structure, first take a look at the [Hark documentation](https://docs.econ-ark.org/overview/ARKitecture.html). Next, to understand how the HARK Market class works in less standard setting, look at the [Fashion victim model](https://github.com/econ-ark/DemARK/blob/99948acb7b59cc9a6fb7de758972266fa4b03a06/notebooks/Fashion-Victim-Model.ipynb).\n"
    ]
   },
   {
@@ -441,7 +441,7 @@
     "\n",
     "When you look at the [HARK](https://github.com/econ-ark/HARK) sources, you will find the subdirectory called HARK. Next there is a script called \"core. py\". Surprisingly, you will not find this code in many of the subclasses which you learned during this journey!\n",
     "\n",
-    "The reason for this is that HARK.core.py is a core of the package: a framework  for all models which can be coded in HARK. It contains the general framework of the agent-type classes (AgentType class) and for the market. The exact structure of modules in the HARK core you can find in the [Hark documentation](https://docs.econ-ark.org/ARKitecture.html#general-purpose-tools). Here, you can also find the general structure of the [AgentType](https://docs.econ-ark.org/ARKitecture.html#agenttype-class) and [Market classes](https://docs.econ-ark.org/ARKitecture.html#market-class).\n",
+    "The reason for this is that HARK.core.py is a core of the package: a framework  for all models which can be coded in HARK. It contains the general framework of the agent-type classes (AgentType class) and for the market. The exact structure of modules in the HARK core you can find in the [Hark documentation](https://docs.econ-ark.org/overview/ARKitecture.html#general-purpose-tools). Here, you can also find the general structure of the [AgentType](https://docs.econ-ark.org/overview/ARKitecture.html#agenttype-class) and [Market classes](https://docs.econ-ark.org/overview/ARKitecture.html#market-class).\n",
     "\n",
     "Where are the subclasses which you'v learned during the journey? In HARK, the subclasses are located in the separate directories. For the AgentType subclasses, you need to look at HARK.ConsumptionSaving directory. For example, `PerfForesightConsumerType` and `IndShockConsumerType` can be found in ConsIndShockModel.py. Nevertheless, if you want to understand any of the HARK modules, you must first understand `HARK.core`.\n",
     "\n",
@@ -468,7 +468,7 @@
     "\n",
     "We hope that this section gave you some idea how the HARK library works. However, HARK contains much more than is discussed here. Here is some more guidance on how to continue your journey:\n",
     "\n",
-    "- Before you start make sure that you understand the endogenous grid method, as well as the general framework structure for AgentType and Market from [HARK documentation](https://docs.econ-ark.org/ARKitecture.html#agenttype-class).\n",
+    "- Before you start make sure that you understand the endogenous grid method, as well as the general framework structure for AgentType and Market from [HARK documentation](https://docs.econ-ark.org/overview/ARKitecture.html#agenttype-class).\n",
     "- When working through HARK.core, make sure that you see the connection between the structure in the documentation and the code (check autodoc from the [HARK documentation](https://docs.econ-ark.org/reference/tools/core.html) webpage).\n",
     "- Proceed to the ConsumptionSaving/ConsIndShockModel.py and compare the tutorials with the source code.\n",
     "- Proceed to the ConsumptionSaving/ConsAggShockModel.py and compare the tutorial on the Market class with the source code, check [autodoc](https://docs.econ-ark.org/reference/ConsumptionSaving/ConsAggShockModel.html).\n",

examples/Journeys/Journey-PhD.py

@@ -92,7 +92,7 @@
 # M_{t+1} &=& R (M_{t}-C_{t}) + Y_t, \\
 # \end{eqnarray*}
 #
-# Finding a distribution of agent assets (consumption, savings) must involve much more advanced numerical tools than in the representative agent setting. This is more demanding task to accomplish and master. Moreover, the knowledge about involved numerical methods is less systematic, and often hard to find. To quote the HARK [Documentation](https://docs.econ-ark.org/introduction.html):
+# Finding a distribution of agent assets (consumption, savings) must involve much more advanced numerical tools than in the representative agent setting. This is more demanding task to accomplish and master. Moreover, the knowledge about involved numerical methods is less systematic, and often hard to find. To quote the HARK [Documentation](https://docs.econ-ark.org/overview/introduction.html):
 #
 # *"After months of effort, you may have had the character-improving experience of
 # proudly explaining to your adviser that not only had you grafted two ideas
@@ -339,7 +339,7 @@
 #
 # The Market class was designed to be a general framework for many different macro models. It involves a procedure of aggregating the agents' choices: eg. aggregating consumption and savings (`reap_vars` in the code) and then transforming the aggregated variables (`mill_rule` in the code).
 #
-# If you would like to get better knowledge about this structure, first take a look at the [Hark documentation](https://docs.econ-ark.org/ARKitecture.html). Next, to understand how the HARK Market class works in less standard setting, look at the [Fashion victim model](../notebooks/Fashion-Victim-Model.ipynb).
+# If you would like to get better knowledge about this structure, first take a look at the [Hark documentation](https://docs.econ-ark.org/overview/ARKitecture.html). Next, to understand how the HARK Market class works in less standard setting, look at the [Fashion victim model](../notebooks/Fashion-Victim-Model.ipynb).
 #
 
 # %% [markdown]
@@ -353,7 +353,7 @@
 #
 # When you look at the [HARK](https://github.com/econ-ark/HARK) sources, you will find the subdirectory called HARK. Next there is a script called "core. py". Surprisingly, you will not find this code in many of the subclasses which you learned during this journey!
 #
-# The reason for this is that HARK.core.py is a core of the package: a framework  for all models which can be coded in HARK. It contains the general framework of the agent-type classes (AgentType class) and for the market. The exact structure of modules in the HARK core you can find in the [Hark documentation](https://docs.econ-ark.org/ARKitecture.html#general-purpose-tools). Here, you can also find the general structure of the [AgentType](https://docs.econ-ark.org/ARKitecture.html#agenttype-class) and [Market classes](https://docs.econ-ark.org/ARKitecture.html#market-class).
+# The reason for this is that HARK.core.py is a core of the package: a framework  for all models which can be coded in HARK. It contains the general framework of the agent-type classes (AgentType class) and for the market. The exact structure of modules in the HARK core you can find in the [Hark documentation](https://docs.econ-ark.org/overview/ARKitecture.html#general-purpose-tools). Here, you can also find the general structure of the [AgentType](https://docs.econ-ark.org/overview/ARKitecture.html#agenttype-class) and [Market classes](https://docs.econ-ark.org/overview/ARKitecture.html#market-class).
 #
 # Where are the subclasses which you'v learned during the journey? In HARK, the subclasses are located in the separate directories. For the AgentType subclasses, you need to look at HARK.ConsumptionSaving directory. For example, `PerfForesightConsumerType` and `IndShockConsumerType` can be found in ConsIndShockModel.py. Nevertheless, if you want to understand any of the HARK modules, you must first understand `HARK.core`.
 #
@@ -380,7 +380,7 @@
 #
 # We hope that this section gave you some idea how the HARK library works. However, HARK contains much more than is discussed here. Here is some more guidance on how to continue your journey:
 #
-# - Before you start make sure that you understand the endogenous grid method, as well as the general framework structure for AgentType and Market from [HARK documentation](https://docs.econ-ark.org/ARKitecture.html#agenttype-class).
+# - Before you start make sure that you understand the endogenous grid method, as well as the general framework structure for AgentType and Market from [HARK documentation](https://docs.econ-ark.org/overview/ARKitecture.html#agenttype-class).
 # - When working through HARK.core, make sure that you see the connection between the structure in the documentation and the code (check autodoc from the [HARK documentation](https://docs.econ-ark.org/reference/tools/core.html) webpage).
 # - Proceed to the ConsumptionSaving/ConsIndShockModel.py and compare the tutorials with the source code.
 # - Proceed to the ConsumptionSaving/ConsAggShockModel.py and compare the tutorial on the Market class with the source code, check [autodoc](https://docs.econ-ark.org/reference/ConsumptionSaving/ConsAggShockModel.html).