fix check fails

Documentation/CHANGELOG.md

@@ -77,7 +77,7 @@ This release drops support for Python 3.8 and 3.9, consistent with SPEC 0, and a
 - Create functions for well-known and repeated calculations in single-function solvers. [1395](https://github.com/econ-ark/HARK/pull/1395)
 - Re-work WealthPortfolioSolver to use approximate EGM method [#1404](https://github.com/econ-ark/HARK/pull/1404)
 - Default parameter dictionaries for AgentType subclasses have been "flattened": all parameters appear in one place for each model, rather than inheriting from parent models' dictionaries. The only exception is submodels *within* a file when only 1 or 2 parameters are added or changed. [#1425](https://github.com/econ-ark/HARK/pull/1425)
-- Fix minor bug in `HARK.distribution.Bernoulli` to allow conversion into `DiscreteDistributionLabeled`. [#1432](https://github.com/econ-ark/HARK/pull/1432)
+- Fix minor bug in `HARK.distributions.Bernoulli` to allow conversion into `DiscreteDistributionLabeled`. [#1432](https://github.com/econ-ark/HARK/pull/1432)
 
 ### 0.14.1
 
@@ -123,7 +123,7 @@ Release Date: February 16, 2023
 - Changes the `DiscreteDistribution` class to allow for arbitrary array-valued random variables. [#1146](https://github.com/econ-ark/HARK/pull/1146)
 - Adds `IndShockRiskyAssetConsumerType` as agent which can invest savings all in safe asset, all in risky asset, a fixed share in risky asset, or optimize its portfolio. [#1107](https://github.com/econ-ark/HARK/issues/1107)
 - Updates all HARK models to allow for age-varying interest rates. [#1150](https://github.com/econ-ark/HARK/pull/1150)
-- Adds `DiscreteDistribution.expected` method which expects vectorized functions and is faster than `HARK.distribution.calc_expectation`. [#1156](https://github.com/econ-ark/HARK/pull/1156)
+- Adds `DiscreteDistribution.expected` method which expects vectorized functions and is faster than `HARK.distributions.calc_expectation`. [#1156](https://github.com/econ-ark/HARK/pull/1156)
 - Adds `DiscreteDistributionXRA` class which extends `DiscreteDistribution` to allow for underlying data to be stored in a `xarray.DataArray` object. [#1156](https://github.com/econ-ark/HARK/pull/1156)
 - Adds keyword argument `labels` to `expected()` when using `DiscreteDistributionXRA` to allow for expressive functions that use labeled xarrays. [#1156](https://github.com/econ-ark/HARK/pull/1156)
 - Adds a wrapper for [`interpolation.py`](https://github.com/EconForge/interpolation.py) for fast multilinear interpolation. [#1151](https://github.com/econ-ark/HARK/pull/1151)
@@ -136,7 +136,7 @@ Release Date: February 16, 2023
 - Updates the `numpy` random generator from `RandomState` to `Generator`. [#1193](https://github.com/econ-ark/HARK/pull/1193)
 - Turns the income and income+return distributions into `DiscreteDistributionLabeled` objects. [#1189](https://github.com/econ-ark/HARK/pull/1189)
 - Creates `UtilityFuncCRRA` which is an object oriented utility function with a coefficient of constant relative risk aversion and includes derivatives and inverses. Also creates `UtilityFuncCobbDouglas`, `UtilityFuncCobbDouglasCRRA`, and `UtilityFuncConstElastSubs`. [#1168](https://github.com/econ-ark/HARK/pull/1168)
-- Reorganizes `HARK.distribution`. All distributions now inherit all features from `scipy.stats`. New `ContinuousFrozenDistribution` and `DiscreteFrozenDistribution` to use `scipy.stats` distributions not yet implemented in HARK. New `Distribution.discretize(N, method = "***")` replaces `Distribution.approx(N)`. New `DiscreteDistribution.limit` attribute describes continuous origin and discretization method. [#1197](https://github.com/econ-ark/HARK/pull/1197).
+- Reorganizes `HARK.distributions`. All distributions now inherit all features from `scipy.stats`. New `ContinuousFrozenDistribution` and `DiscreteFrozenDistribution` to use `scipy.stats` distributions not yet implemented in HARK. New `Distribution.discretize(N, method = "***")` replaces `Distribution.approx(N)`. New `DiscreteDistribution.limit` attribute describes continuous origin and discretization method. [#1197](https://github.com/econ-ark/HARK/pull/1197).
 - Creates new class of _labeled_ models under `ConsLabeledModel` that use xarray for more expressive modeling of underlying mathematical and economics variables. [#1177](https://github.com/econ-ark/HARK/pull/1177)
 
 #### Minor Changes
@@ -147,7 +147,7 @@ Release Date: February 16, 2023
 - Adds `benchmark` utility to profile the performance of `HARK` solvers. [#1131](https://github.com/econ-ark/HARK/pull/1131)
 - Fixes scaling bug in Normal equiprobable approximation method. [1139](https://github.com/econ-ark/HARK/pull/1139)
 - Removes the extra-dimension that was returned by `calc_expectations` in some instances. [#1149](https://github.com/econ-ark/HARK/pull/1149)
-- Adds `HARK.distribution.expected` alias for `DiscreteDistribution.expected`. [#1156](https://github.com/econ-ark/HARK/pull/1156)
+- Adds `HARK.distributions.expected` alias for `DiscreteDistribution.expected`. [#1156](https://github.com/econ-ark/HARK/pull/1156)
 - Renames attributes in `DiscreteDistribution`: `X` to `atoms` and `pmf` to `pmv`. [#1164](https://github.com/econ-ark/HARK/pull/1164), [#1051](https://github.com/econ-ark/HARK/pull/1151), [#1159](https://github.com/econ-ark/HARK/pull/1159).
 - Remove or replace automated tests that depend on brittle simulation results. [#1148](https://github.com/econ-ark/HARK/pull/1148)
 - Updates asset grid constructor from `ConsIndShockModel.py` to allow for linearly-spaced grids when `aXtraNestFac == -1`. [#1172](https://github.com/econ-ark/HARK/pull/1172)

Documentation/overview/ARKitecture.md

@@ -33,6 +33,7 @@ Python modules in HARK can generally be categorized into two types: tools and mo
 
 **Model modules** specify particular economic models, including classes to represent agents in the model (and the ''market structure'' in which they interact) and functions for solving the ''one period problem'' of those models. For example, **ConsIndShockModel.py** concerns consumption-saving models in which agents have CRRA utility over consumption and face idiosyncratic shocks to permanent and transitory income. The module includes classes for representing ''types'' of consumers, along with functions for solving (several flavors of) the one period consumption-saving problem. Model modules generally have **_Model_** in their name, and the classes for representing agents all have **_Type_** at the end of their name (as instances represent a collection of ex ante homogeneous agents who share common model and parameters-- a "type"). For example, **_HARK.ConsumptionSaving.ConsIndShockModel_** includes the class **_IndShockConsumerType_**.
 
+
 ## HARK
 
 After you [installed](https://docs.econ-ark.org/Documentation/guides/installation.html) or [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 subclasses](#agenttype-class) and [Market subclasses](#market-class).
@@ -106,6 +107,7 @@ A discrete time model in our framework is characterized by a sequence of ''perio
 
 An instance of **_AgentType_** also has the attributes named in **_time_vary_** and **_time_inv_**, and may have other attributes that are not included in either (e.g. values not used in the model solution, but instead to construct objects used in the solution). Note that **_time_vary_** may include attributes that are never used by a function in **_solveOnePeriod_**. Most saliently, the attribute **_solution_** is time-varying but is not used to solve individual periods.
 
+
 #### A Universal Solver
 
 When an instance of **_AgentType_** invokes its **_solve_** method, the solution to the agent's problem is stored in the attribute **_solution_**. The solution is computed by recursively solving the sequence of periods defined by the variables listed in **_time_vary_** and **_time_inv_** using the functions in **_solve\_one_period_**. The time-varying inputs are updated each period, including the successive period's solution as **_solution_next_**; the same values of time invariant inputs in **_time_inv_** are passed to the solver in every period. The first call to **_solve\_one\_period_** uses **_solution_terminal_** as **_solution_next_**. In an infinite horizon problem (**_cycles_=0**), the sequence of periods is solved until the solutions of successive cycles have a ''distance'' of less than **_tolerance_**. Usually, the "sequence" of periods in such models is just *one* period long.