doc: build readme

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.
@@ -117,7 +117,7 @@ edf
 #> * geo_type  = state
 #> * time_type = day
 #> * as_of     = 2024-01-01
-#>
+#> 
 #> # A tibble: 2,808 × 4
 #>   geo_value time_value cases_cumulative cases_daily
 #> * <chr>     <date>                <dbl>       <dbl>
@@ -141,16 +141,16 @@ edf
 #> * geo_type  = state
 #> * time_type = day
 #> * as_of     = 2024-01-01
-#>
+#> 
 #> # A tibble: 2,808 × 5
 #>   geo_value time_value cases_cumulative cases_daily smoothed_cases_daily
 #> * <chr>     <date>                <dbl>       <dbl>                <dbl>
-#> 1 ca        2020-03-01               19          19                19
-#> 2 ca        2020-03-02               23           4                11.5
+#> 1 ca        2020-03-01               19          19                19   
+#> 2 ca        2020-03-02               23           4                11.5 
 #> 3 ca        2020-03-03               29           6                 9.67
-#> 4 ca        2020-03-04               40          11                10
-#> 5 ca        2020-03-05               50          10                10
-#> 6 ca        2020-03-06               68          18                11.3
+#> 4 ca        2020-03-04               40          11                10   
+#> 5 ca        2020-03-05               50          10                10   
+#> 6 ca        2020-03-06               68          18                11.3 
 #> # ℹ 2,802 more rows
 ```