diff --git a/model/README.md b/model/README.md
index caa7bd8e..8c873ad6 100755
--- a/model/README.md
+++ b/model/README.md
@@ -1,6 +1,6 @@
-# PyRenew: A package for Bayesian renewal modeling with JAX and Numpyro.
+# PyRenew: A Package for Bayesian Renewal Modeling with JAX and Numpyro.
 
-`pyrenew` is a flexible tool for simulation and inference of epidemiological models with an emphasis on renewal models. Built on top of `numpyro`, `pyrenew` provides core components for model building as well as pre-defined models for processing various types of observational processes.
+`pyrenew` is a flexible tool for simulating and statistical inference of epidemiological models, emphasizing renewal models. Built on top of the [`numpyro`](https://num.pyro.ai/) Python library, `pyrenew` provides core components for model building, including pre-defined models for processing various types of observational processes.
 
 ## Installation
 
@@ -12,4 +12,4 @@ pip install git+https://github.com/cdcent/cfa-pyrenew.git
 
 ## Demo
 
-The [`docs`](docs) folder contains quarto documents to get you started. It simulates observed hospitalizations using a simple renewal process model and then fits to it using a No-U-Turn Sampler.
+The [`docs`](docs) folder contains quarto documents to get you started. It simulates observed hospitalizations using a simple renewal process model and then fits it using a No-U-Turn Sampler.
diff --git a/model/docs/getting-started.md b/model/docs/getting-started.md
index 894408a4..884dbac6 100644
--- a/model/docs/getting-started.md
+++ b/model/docs/getting-started.md
@@ -17,7 +17,7 @@ quantities**. Mainly, sampling from a `RandomVariable` involves calling
 the `sample()` method. The benefit of this design is the definition of
 the sample function can be arbitrary, allowing the user to either sample
 from a distribution using `numpyro.sample()`, compute fixed quantities
-(like a mechanistic equation,) or return a fixed value (like a
+(like a mechanistic equation), or return a fixed value (like a
 pre-computed PMF.) For instance, we may be interested in estimating a
 PMF, in which case a `RandomVariable` sampling function may roughly be
 defined as:
@@ -29,7 +29,7 @@ class MyRandVar(RandomVariable):
 ```
 
 Whereas, in some other cases, we may instead use a fixed quantity for
-that variable (like a pre-computed PMF,) where the `RandomVariable`’s
+that variable (like a pre-computed PMF), where the `RandomVariable`’s
 sample function could be defined like:
 
 ``` python
@@ -45,7 +45,7 @@ some quantity needed for other calculations (second case.)
 The `Model` metaclass provides basic functionality for estimating and
 simulation. Like `RandomVariable`, the `Model` metaclass has a
 `sample()` method that defines the model structure. Ultimately, models
-can be nested (or inherited,) providing a straightforward way to add
+can be nested (or inherited), providing a straightforward way to add
 layers of complexity.
 
 ## ‘Hello world’ model
diff --git a/model/docs/getting-started.qmd b/model/docs/getting-started.qmd
index 46dde101..f17bbbca 100644
--- a/model/docs/getting-started.qmd
+++ b/model/docs/getting-started.qmd
@@ -8,7 +8,7 @@ engine: jupyter
 
 ## The fundamentals
 
-`pyrenew`'s core components are the metaclasses `RandomVariable` and `Model`. From the package's perspective, a `RandomVariable` is a quantity models can sample and estimate, **including deterministic quantities**. Mainly, sampling from a `RandomVariable` involves calling the `sample()` method. The benefit of this design is the definition of the sample function can be arbitrary, allowing the user to either sample from a distribution using `numpyro.sample()`, compute fixed quantities (like a mechanistic equation,) or return a fixed value (like a pre-computed PMF.) For instance, we may be interested in estimating a PMF, in which case a `RandomVariable` sampling function may roughly be defined as:
+`pyrenew`'s core components are the metaclasses `RandomVariable` and `Model`. From the package's perspective, a `RandomVariable` is a quantity models can sample and estimate, **including deterministic quantities**. Mainly, sampling from a `RandomVariable` involves calling the `sample()` method. The benefit of this design is the definition of the sample function can be arbitrary, allowing the user to either sample from a distribution using `numpyro.sample()`, compute fixed quantities (like a mechanistic equation), or return a fixed value (like a pre-computed PMF.) For instance, we may be interested in estimating a PMF, in which case a `RandomVariable` sampling function may roughly be defined as:
 
 ```python
 class MyRandVar(RandomVariable):
@@ -16,7 +16,7 @@ class MyRandVar(RandomVariable):
         return numpyro.sample(...)
 ```
 
-Whereas, in some other cases, we may instead use a fixed quantity for that variable (like a pre-computed PMF,) where the `RandomVariable`'s sample function could be defined like:
+Whereas, in some other cases, we may instead use a fixed quantity for that variable (like a pre-computed PMF), where the `RandomVariable`'s sample function could be defined like:
 
 ```python
 class MyRandVar(RandomVariable):
@@ -26,7 +26,7 @@ class MyRandVar(RandomVariable):
 
 This way, when a `Model` samples from `MyRandVar`, it could be either adding random variables to be estimated (first case) or just retrieving some quantity needed for other calculations (second case.)
 
-The `Model` metaclass provides basic functionality for estimating and simulation. Like `RandomVariable`, the `Model` metaclass has a `sample()` method that defines the model structure. Ultimately, models can be nested (or inherited,) providing a straightforward way to add layers of complexity.
+The `Model` metaclass provides basic functionality for estimating and simulation. Like `RandomVariable`, the `Model` metaclass has a `sample()` method that defines the model structure. Ultimately, models can be nested (or inherited), providing a straightforward way to add layers of complexity.
 
 
 ## 'Hello world' model