docs: document (GHA)

Author: dshemetov

README.md

@@ -14,35 +14,35 @@ forecasting.
 
 `{epiprocess}` contains:
 
-  - `epi_df()` and `epi_archive()`, two data frame classes (that work
-    like a `{tibble}` with `{dplyr}` verbs) for working with
-    epidemiological time series data
-      - `epi_df` is for working with a snapshot of data at a single
-        point in time
-      - `epi_archive` is for working with histories of data that changes
-        over time
-      - one of the most common uses of `epi_archive` is for accurate
-        backtesting of forecasting models, see `vignette("backtesting",
-        package="epipredict")`
-  - signal processing tools building on these data structures such as
-      - `epi_slide()` for sliding window operations (aids with feature
-        creation)
-      - `epix_slide()` for sliding window operations on archives (aids
-        with backtesting)
-      - `growth_rate()` for computing growth rates
-      - `detect_outlr()` for outlier detection
-      - `epi_cor()` for computing correlations
+- `epi_df()` and `epi_archive()`, two data frame classes (that work like
+  a `{tibble}` with `{dplyr}` verbs) for working with epidemiological
+  time series data
+  - `epi_df` is for working with a snapshot of data at a single point in
+    time
+  - `epi_archive` is for working with histories of data that changes
+    over time
+  - one of the most common uses of `epi_archive` is for accurate
+    backtesting of forecasting models, see
+    `vignette("backtesting", package="epipredict")`
+- signal processing tools building on these data structures such as
+  - `epi_slide()` for sliding window operations (aids with feature
+    creation)
+  - `epix_slide()` for sliding window operations on archives (aids with
+    backtesting)
+  - `growth_rate()` for computing growth rates
+  - `detect_outlr()` for outlier detection
+  - `epi_cor()` for computing correlations
 
 If you are new to this set of tools, you may be interested learning
 through a book format: [Introduction to Epidemiological
 Forecasting](https://cmu-delphi.github.io/delphi-tooling-book/).
 
 You may also be interested in:
 
-  - `{epidatr}`, for accessing wide range of epidemiological data sets,
-    including COVID-19 data, flu data, and more.
-  - [rtestim](https://github.com/dajmcdon/rtestim), a package for
-    estimating the time-varying reproduction number of an epidemic.
+- `{epidatr}`, for accessing wide range of epidemiological data sets,
+  including COVID-19 data, flu data, and more.
+- [rtestim](https://github.com/dajmcdon/rtestim), a package for
+  estimating the time-varying reproduction number of an epidemic.
 
 This package is provided by the [Delphi group](https://delphi.cmu.edu/)
 at Carnegie Mellon University.