# Serial Interval and Case Reproduction Number Estimation

## Documentation for package ‘mitey’ version 0.2.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

calculate_bootstrap_ci | Calculate Bootstrap Confidence Intervals for R Estimates
calculate_r_estimates | Calculate Reproduction Number Estimates
calculate_si_probability_matrix | Calculate Serial Interval Probability Matrix
calculate_truncation_correction | Calculate Right-Truncation Correction Factors
conv_tri_dist | Convolution of the triangular distribution with the mixture component density (continuous case)
create_day_diff_matrix | Create Day Difference Matrix
f0 | Calculate f0 for Different Components
flower | Calculate flower for Different Components
fupper | Calculate fupper for Different Components
f_gam | Calculate serial interval mixture density assuming underlying gamma distribution
f_norm | Calculate serial interval mixture density assuming underlying normal distribution
generate_case_bootstrap | Generate Bootstrap Sample of Case Incidence
generate_synthetic_epidemic | Generate Synthetic Epidemic Data Using the Renewal Equation
integrate_component | Integrate Serial Interval Component Functions for Likelihood Calculation
integrate_components_wrapper | Compute Serial Interval Component Integrals for All Transmission Routes
plot_si_fit | Visualize Serial Interval Distribution Fit to Outbreak Data
si_estim | Estimate Serial Interval Distribution Using the Vink Method
smooth_estimates | Apply Moving Average Smoothing to R Estimates
wallinga_lipsitch | Estimate Time-Varying Case Reproduction Number Using Wallinga-Lipsitch Method
weighted_var | Calculate Sample Weighted Variance
wt_loglik | Calculate Weighted Negative Log-Likelihood for Gamma Distribution Parameters