Package: menstrualcycleR 1.1.1

What is PACTS, Scaled Cycle Time, and menstrualcycleR? | Understanding the Challenge | Introducing PACTS | Why Continuous Cycle Modeling? | Anchoring on Ovulation and Menses | Estimating Ovulation Without an Ovulation Biomarker | Optional: imputing the next menses from a confirmed ovulation | Purpose and Use Cases | Data Requirements for Utilizing PACTS and menstrualcycleR | 1. Daily Data with Self-Reported Menses Onset | 2. Ovulation Assessment or Imputation | 3. Cycle Length Inclusion Criteria | 4. Supported Variables and Formats | Installation of the menstrualcycleR package | Using menstrualcycleR | Overview of Package Functions | 1. Cycle Scaling Function: pacts_scaling() | 2. Data Checking Functions | 3. Data Visualization Functions | 4. Launching the Shiny App: launch_app() | Demo dataset | A second dataset, for awkward cases | Applying PACTS | Before and After: Filling Missing Dates Within a Cycle | Summary of Row Counts | Cycle time variables are added in cycle_df_scaled | Checking Data availability | Checking Ovulation Data Availability | Visualizing outcomes across the cycle | cycle_plot() | Note on Rolling Averages | Symptom, Menses-centered | Symptom, Ovulation-centered | Visualizing Individual Participants and Cycles with cycle_plot_individual() | cycle_plot_individual | cycle_plot_individual Output | Cycle_1 summary for ID = 2 | Cycle_1 plot for ID = 2 | Modeling Menstrual Cycle Dynamics with Generalized Additive Mixed Models (GAMMs) | Model Specifications | Running a GAMM | Data Preparation | Interpreting the GAMM Summary Output | Model Family & Link | Model Formula | Parametric Coefficients | Smooth Terms | Model Fit | Visualizing your GAMM across the menstrual cycle | First, we need to compute predicted or model-implied values | Plotting model-implied values | Outstanding Questions | References

Input format | One row per person per day | Menses onset | Ovulation | From a period-tracker export to the PACTS table | Common device exports | Checks before scaling | Choosing a cycle-time variable | Anchor | Confirmed or imputed ovulation | Sensitivity to imputed ovulation | Extended-phase rows | When your data is awkward | A setting recovers rows and data at different rates | leading_ovulation_luteal_days is a placement, not a cap | Some people cannot be recovered, and it is worth knowing which | The case column disappears on fabricated rows, deliberately | What to report | References