Package index • epicurves

Compartmental models under the Gaussian approximation

Any mass-action compartmental model, incidence or prevalence observations, time-varying rates.

ga_model(): Define a mass-action compartmental model
ga_event(): Define an event of a mass-action compartmental model
ga_incidence() ga_prevalence(): Observation streams for the Gaussian-approximation likelihood
ga_loglik(): Gaussian-approximation log-likelihood of a compartmental model
ga_simulate(): Exact simulation of a compartmental model and its surveillance counts
ga_fit(): Maximum likelihood for a compartmental model under the Gaussian approximation
ga_profile(): Profile log-likelihood for one parameter of a ga_fit
ga_bootstrap(): Parametric bootstrap for a ga_fit
confint(<ga_fit>): Wald confidence intervals for a ga_fit
confint(<ga_boot>): Bootstrap confidence intervals for a ga_boot
predict(<ga_fit>): One-step-ahead predictions from a ga_fit
plot(<ga_fit>): Plot the one-step-ahead predictions of a ga_fit

sird_ga_simulate_inc(): Simulate daily reported cases and deaths from the SIRD jump process
sird_ga_loglik_inc(): SIRD-GA log-likelihood for daily reported cases and/or deaths
sird_ga_fit_inc(): Maximum likelihood for SIRD-GA from daily cases and/or deaths
sird_ga_fit_tv(): SIRD-GA with piecewise-constant transmission, fitted to cases and deaths
sird_ga_profile(): Profile log-likelihood of one SIRD-GA parameter
sird_ga_loglik_prev(): Fast Kalman log-likelihood for prevalence observations

sird_drift(): Deterministic SIRD drift vector
sird_jacobian(): Jacobian of the SIRD drift vector
sird_diffusion(): Diffusion covariance matrix of the SIRD jump process
sird_sigma(): Diffusion "square-root" factor of the SIRD jump process
sird_R0(): Basic reproduction number of the SIRD model
sird_ode_solve(): Solve the deterministic normalised SIRD ODE system
sird_gillespie(): Exact stochastic simulation of the SIRD jump process (Gillespie algorithm)
sird_ga_simulate_obs(): Simulate partially-observed SIRD-GA surveillance data
sird_ga_transition(): Solve the SIRD-GA state-space transition quantities
sird_ga_Ak(): Resolvent matrix \(A_{k-1} = \Phi(t_k, t_{k-1})\) of interval k
sird_ga_Fk(): Drift increment \(F_k\) of interval k
sird_ga_Qk(): State-noise covariance \(Q_k\) of interval k
sird_ga_loglik(): Kalman-filter log-likelihood of the SIRD-GA state-space model
sird_ga_fit(): Maximum (approximate) likelihood estimation for the SIRD-GA model
sird_ga_fit_multistart(): Multi-start SIRD-GA fitting, with a practical-identifiability summary
sird_ga_bootstrap(): Parametric bootstrap confidence intervals for the SIRD-GA MLE

logistic_curve(): Logistic growth curve
gompertz_curve(): Gompertz growth curve
richards_curve(): Richards growth curve
growth_incidence(): Daily incidence implied by a growth curve
fit_growth_incidence(): Fit one growth curve to daily counts by maximum likelihood
fit_ensemble_incidence(): Ensemble of growth curves fitted to daily counts
predict_band(): Forecast band of a growth-incidence fit

bic_segment(): Offline segmentation of daily counts by penalised likelihood
edetect_segment(): Online segmentation of an epidemic curve with e-detectors

fit_growth_model(): Fit a single growth-curve model by nonlinear least squares
ensemble_weights(): Inverse-criterion ensemble weights
fit_ensemble(): Fit the full ensemble of growth-curve models to one data window
predict(<ensemble_fit>): Predict the ensemble curve at arbitrary times
ensemble_bootstrap_ci(): Bootstrap confidence band for an ensemble fit
ensemble_bootstrap_params(): Bootstrap confidence intervals for each growth model's parameters
split_mse(): Mean squared error of a split at a single candidate partition point
split_mse_single(): Best single-model split MSE at a candidate partition point (fast proxy)
find_partition_point(): Grid search for a single structural change (partition) point
permutation_test_partition(): Permutation test for a single candidate structural change point
fit_piecewise_ensemble(): Sequential piecewise ensemble model
predict(<piecewise_fit>): Predict from a piecewise ensemble fit
smooth_series(): Centered moving-average smoother

plot_ensemble_fit(): Plot an ensemble fit against the observed data
plot_partition_profile(): Plot an MSE (or AIC) profile from a partition-point grid search
plot_piecewise_fit(): Plot a fitted piecewise ensemble curve, with confirmed partition points
plot_sird_ga_fit(): Plot observed vs. fitted SIRD-GA incidence, with an optional bootstrap band