Setup | R and Positron | Git and GitHub | Air | RStudio keybindings | R packages | Creating our package | License | Git | The DESCRIPTION file | Functions, part 1 | Our first function: no external dependencies | Adding additional arguments | Our second function: with external dependencies | Package data | Exported data | External data from the web | Creating data from code | Raw data | Functions, part 2 | Read in raw data | Using dplyr data masking in packages | Tests | Setting up testthat | Our first real tests | Coverage with covr | Recap: data + functions + tests | README.Rmd | GitHub Actions | pkgdown | Other topics | Vignettes | NEWS | Hex stickers | R Universe | CRAN submission | Docker & pracpac | Additional resources | Session Information

Overview | Analyzing plausibility of forecast data | Prepare observed data | Convert observed data into a signal with to_signal() | Create a seed from the observed data via plane_seed() | Prepare forecast data | Convert forecast data into a signal with to_signal() | Run plane_score() and visualize results | Analyzing plausibility of observed data

Overview | Data preparation | Difference - plane_diff() | Flagged as implausible | Not flagged as implausible | Coverage - plane_cover() | Taper - plane_taper() | Repeat - plane_repeat() | Controlling sensitivity with parameters | Trend - plane_trend() | Shape - plane_shape() | Zero - plane_zero()

Overview | Weighting scheme within plane_score() | Motivations for weighting | Limitations that arise from seed data | Reliability of seed data | Length of seed data | What to do when a flag is raised | Summary

Overview | Usage | Data retrieval | Time series forecasting | Data preparation | Fit a model and forecast | Count regression forecasting | ILI retrieval and prep

Overview | Analysis steps | Example data | Usage | Inputs and outputs | Launching the app

Creating manhattan plots | Creating Q-Q plots

Overview | Pipeline | Setting up the Docker template | Dockerfile | run.sh | pre.R | Building the image | Running the container | Shiny | app.R

Overview | Terminology | Using pracpac | use_docker() | build_image()

Overview | Pedigree parsing and manipulation | Degree Inference | Benchmarking Degree Classification | IBD Segment Analysis