Fix: Add trailing slash to quantecon-py URLs and upgrade quantecon-book-theme==0.13.2 (jupyterbook link fix)

environment.yml

@@ -7,7 +7,7 @@ dependencies:
   - pip
   - pip:
     - jupyter-book==1.0.4post1
-    - quantecon-book-theme==0.13.1
+    - quantecon-book-theme==0.13.2
     - sphinx-tojupyter==0.4.0
     - sphinxext-rediraffe==0.2.7
     - sphinx-exercise==1.2.0

lectures/finite_markov.md

@@ -212,11 +212,11 @@ One natural way to answer questions about Markov chains is to simulate them.
 
 (To approximate the probability of event $E$, we can simulate many times and count the fraction of times that $E$ occurs).
 
-Nice functionality for simulating Markov chains exists in [QuantEcon.py](https://quantecon.org/quantecon-py).
+Nice functionality for simulating Markov chains exists in [QuantEcon.py](https://quantecon.org/quantecon-py/).
 
 * Efficient, bundled with lots of other useful routines for handling Markov chains.
 
-However, it's also a good exercise to roll our own routines --- let's do that first and then come back to the methods in [QuantEcon.py](https://quantecon.org/quantecon-py).
+However, it's also a good exercise to roll our own routines --- let's do that first and then come back to the methods in [QuantEcon.py](https://quantecon.org/quantecon-py/).
 
 In these exercises, we'll take the state space to be $S = 0,\ldots, n-1$.
 
@@ -231,7 +231,7 @@ The Markov chain is then constructed as discussed above.  To repeat:
 
 To implement this simulation procedure, we need a method for generating draws from a discrete distribution.
 
-For this task, we'll use `random.draw` from [QuantEcon](https://quantecon.org/quantecon-py), which works as follows:
+For this task, we'll use `random.draw` from [QuantEcon](https://quantecon.org/quantecon-py/), which works as follows:
 
 ```{code-cell} python3
 ψ = (0.3, 0.7)           # probabilities over {0, 1}
@@ -294,7 +294,7 @@ always close to 0.25, at least for the `P` matrix above.
 
 ### Using QuantEcon's Routines
 
-As discussed above, [QuantEcon.py](https://quantecon.org/quantecon-py) has routines for handling Markov chains, including simulation.
+As discussed above, [QuantEcon.py](https://quantecon.org/quantecon-py/) has routines for handling Markov chains, including simulation.
 
 Here's an illustration using the same P as the preceding example
 
@@ -306,7 +306,7 @@ X = mc.simulate(ts_length=1_000_000)
 np.mean(X == 0)
 ```
 
-The [QuantEcon.py](https://quantecon.org/quantecon-py) routine is [JIT compiled](https://python-programming.quantecon.org/numba.html#numba-link) and much faster.
+The [QuantEcon.py](https://quantecon.org/quantecon-py/) routine is [JIT compiled](https://python-programming.quantecon.org/numba.html#numba-link) and much faster.
 
 ```{code-cell} ipython
 %time mc_sample_path(P, sample_size=1_000_000) # Our homemade code version
@@ -556,7 +556,7 @@ $$
 It's clear from the graph that this stochastic matrix is irreducible: we can  eventually
 reach any state from any other state.
 
-We can also test this using [QuantEcon.py](https://quantecon.org/quantecon-py)'s MarkovChain class
+We can also test this using [QuantEcon.py](https://quantecon.org/quantecon-py/)'s MarkovChain class
 
 ```{code-cell} python3
 P = [[0.9, 0.1, 0.0],
@@ -775,7 +775,7 @@ One option is to regard solving system {eq}`eq:eqpsifixed`  as an eigenvector pr
 $\psi$ such that $\psi = \psi P$ is a left eigenvector associated
 with the unit eigenvalue $\lambda = 1$.
 
-A stable and sophisticated algorithm specialized for stochastic matrices is implemented in [QuantEcon.py](https://quantecon.org/quantecon-py).
+A stable and sophisticated algorithm specialized for stochastic matrices is implemented in [QuantEcon.py](https://quantecon.org/quantecon-py/).
 
 This is the one we recommend:
 
@@ -1322,7 +1322,7 @@ $$
 
 Tauchen's method {cite}`Tauchen1986` is the most common method for approximating this continuous state process with a finite state Markov chain.
 
-A routine for this already exists in [QuantEcon.py](https://quantecon.org/quantecon-py) but let's write our own version as an exercise.
+A routine for this already exists in [QuantEcon.py](https://quantecon.org/quantecon-py/) but let's write our own version as an exercise.
 
 As a first step, we choose
 
@@ -1363,13 +1363,13 @@ The exercise is to write a function `approx_markov(rho, sigma_u, m=3, n=7)` that
 $\{x_0, \ldots, x_{n-1}\} \subset \mathbb R$ and $n \times n$ matrix
 $P$ as described above.
 
-* Even better, write a function that returns an instance of [QuantEcon.py's](https://quantecon.org/quantecon-py) MarkovChain class.
+* Even better, write a function that returns an instance of [QuantEcon.py's](https://quantecon.org/quantecon-py/) MarkovChain class.
 ```
 
 ```{solution} fm_ex3
 :class: dropdown
 
-A solution from the [QuantEcon.py](https://quantecon.org/quantecon-py) library
+A solution from the [QuantEcon.py](https://quantecon.org/quantecon-py/) library
 can be found [here](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/markov/approximation.py).
 
 ```

lectures/kalman.md

@@ -506,11 +506,11 @@ In this case, for any initial choice of $\Sigma_0$ that is both non-negative and
 ```{index} single: Kalman Filter; Programming Implementation
 ```
 
-The class `Kalman` from the [QuantEcon.py](https://quantecon.org/quantecon-py) package implements the Kalman filter
+The class `Kalman` from the [QuantEcon.py](https://quantecon.org/quantecon-py/) package implements the Kalman filter
 
 * Instance data consists of:
     * the moments $(\hat x_t, \Sigma_t)$ of the current prior.
-    * An instance of the [LinearStateSpace](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py) class from [QuantEcon.py](https://quantecon.org/quantecon-py).
+    * An instance of the [LinearStateSpace](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py) class from [QuantEcon.py](https://quantecon.org/quantecon-py/).
 
 The latter represents a linear state space model of the form
 
@@ -530,7 +530,7 @@ $$
 Q := CC' \quad \text{and} \quad R := HH'
 $$
 
-* The class `Kalman` from the [QuantEcon.py](https://quantecon.org/quantecon-py) package has a number of methods, some that we will wait to use until we study more advanced applications in subsequent lectures.
+* The class `Kalman` from the [QuantEcon.py](https://quantecon.org/quantecon-py/) package has a number of methods, some that we will wait to use until we study more advanced applications in subsequent lectures.
 * Methods pertinent for this lecture  are:
     * `prior_to_filtered`, which updates $(\hat x_t, \Sigma_t)$ to $(\hat x_t^F, \Sigma_t^F)$
     * `filtered_to_forecast`, which updates the filtering distribution to the predictive distribution -- which becomes the new prior $(\hat x_{t+1}, \Sigma_{t+1})$

lectures/linear_models.md

@@ -1334,7 +1334,7 @@ Weaker sufficient conditions for convergence  associate eigenvalues equaling or
 ## Code
 
 Our preceding simulations and calculations are based on code in
-the file [lss.py](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py) from the [QuantEcon.py](https://quantecon.org/quantecon-py) package.
+the file [lss.py](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py) from the [QuantEcon.py](https://quantecon.org/quantecon-py/) package.
 
 The code implements a class for handling linear state space models (simulations, calculating moments, etc.).
 

lectures/lqcontrol.md

@@ -555,7 +555,7 @@ for $t = 0, \ldots, T-1$ attains the minimum of {eq}`lq_object` subject to our c
 ## Implementation
 
 We will use code from [lqcontrol.py](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lqcontrol.py)
-in [QuantEcon.py](https://quantecon.org/quantecon-py)
+in [QuantEcon.py](https://quantecon.org/quantecon-py/)
 to solve finite and infinite horizon linear quadratic control problems.
 
 In the module, the various updating, simulation and fixed point methods

lectures/markov_perf.md

@@ -335,7 +335,7 @@ This is the approach we adopt in the next section.
 
 ### Implementation
 
-We use the function [nnash](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lqnash.py) from [QuantEcon.py](https://quantecon.org/quantecon-py) that computes a Markov perfect equilibrium of the infinite horizon linear-quadratic dynamic game in the manner described above.
+We use the function [nnash](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lqnash.py) from [QuantEcon.py](https://quantecon.org/quantecon-py/) that computes a Markov perfect equilibrium of the infinite horizon linear-quadratic dynamic game in the manner described above.
 
 ## Application
 
@@ -439,7 +439,7 @@ From these, we compute the infinite horizon MPE using the preceding code
 
 Running the code produces the following output.
 
-One way to see that $F_i$ is indeed optimal for firm $i$ taking $F_2$ as given is to use [QuantEcon.py](https://quantecon.org/quantecon-py)'s LQ class.
+One way to see that $F_i$ is indeed optimal for firm $i$ taking $F_2$ as given is to use [QuantEcon.py](https://quantecon.org/quantecon-py/)'s LQ class.
 
 In particular, let's take F2 as computed above, plug it into {eq}`eq_mpe_p1p` and {eq}`eq_mpe_p1d` to get firm 1's problem and solve it using LQ.
 
@@ -520,7 +520,7 @@ Replicate the {ref}`pair of figures <mpe_vs_monopolist>` showing the comparison
 
 Parameters are as in duopoly_mpe.py and you can use that code to compute MPE policies under duopoly.
 
-The optimal policy in the monopolist case can be computed using [QuantEcon.py](https://quantecon.org/quantecon-py)'s LQ class.
+The optimal policy in the monopolist case can be computed using [QuantEcon.py](https://quantecon.org/quantecon-py/)'s LQ class.
 ```
 
 ```{solution-start} mp_ex1

lectures/perm_income_cons.md

@@ -60,7 +60,7 @@ In this lecture, we'll
 
 * show how the solution to the LQ permanent income model can be obtained using LQ control methods.
 * represent the model as a linear state space system as in {doc}`this lecture <linear_models>`.
-* apply [QuantEcon](https://quantecon.org/quantecon-py)'s [LinearStateSpace](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py) class to characterize statistical features of the consumer's optimal consumption and borrowing plans.
+* apply [QuantEcon](https://quantecon.org/quantecon-py/)'s [LinearStateSpace](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py) class to characterize statistical features of the consumer's optimal consumption and borrowing plans.
 
 We'll then use these characterizations to construct a simple model of cross-section wealth and
 consumption dynamics in the spirit of Truman Bewley {cite}`Bewley86`.
@@ -204,7 +204,7 @@ $$
 
 Here we solve the same model using {doc}`LQ methods <lqcontrol>` based on dynamic programming.
 
-After confirming that answers produced by the two methods agree, we apply [QuantEcon](https://quantecon.org/quantecon-py)'s [LinearStateSpace](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py)
+After confirming that answers produced by the two methods agree, we apply [QuantEcon](https://quantecon.org/quantecon-py/)'s [LinearStateSpace](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py)
 class to illustrate features of the model.
 
 Why solve a model in two distinct ways?

lectures/rational_expectations.md

@@ -575,7 +575,7 @@ Let the firm's belief function $H$ be as given in {eq}`ree_hlom2`.
 
 Formulate the firm's problem as a discounted optimal linear regulator problem, being careful to describe all of the objects needed.
 
-Use the class `LQ` from the [QuantEcon.py](https://quantecon.org/quantecon-py) package to solve the firm's problem for the following parameter values:
+Use the class `LQ` from the [QuantEcon.py](https://quantecon.org/quantecon-py/) package to solve the firm's problem for the following parameter values:
 
 $$
 a_0= 100, a_1= 0.05, \beta = 0.95, \gamma=10, \kappa_0 = 95.5, \kappa_1 = 0.95

lectures/samuelson.md

@@ -1159,7 +1159,7 @@ plt.show()
 
 ## Using the LinearStateSpace class
 
-It turns out that we can use the [QuantEcon.py](https://quantecon.org/quantecon-py)
+It turns out that we can use the [QuantEcon.py](https://quantecon.org/quantecon-py/)
 [LinearStateSpace](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py) class to do
 much of the work that we have done from scratch above.
 
@@ -1410,5 +1410,5 @@ in {cite}`Samuelson1939`.
 We saw that different parameter values led to different output paths, which
 could either be stationary, explosive, or oscillating.
 
-We also were able to represent the model using the [QuantEcon.py](https://quantecon.org/quantecon-py)
+We also were able to represent the model using the [QuantEcon.py](https://quantecon.org/quantecon-py/)
 [LinearStateSpace](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py) class.

lectures/troubleshooting.md

@@ -42,7 +42,7 @@ on how to update Anaconda.
 
 Another option is to simply remove Anaconda and reinstall.
 
-You also need to keep the external code libraries, such as [QuantEcon.py](https://quantecon.org/quantecon-py) up to date.
+You also need to keep the external code libraries, such as [QuantEcon.py](https://quantecon.org/quantecon-py/) up to date.
 
 For this task you can either