Function reference • estimateR

All functions
`CH_simulated_linelist`	Synthetic linelist of COVID-19 patients
`EST_incidence_data`	Incidence data for COVID-19 in Estonia
`EpiEstim_wrapper`	EpiEstim wrappers arguments
`HK_delay_data`	Delay between date of onset of symptoms of COVID-19 and date of case confirmation in Hong Kong
`HK_incidence_data`	Incidence data for COVID-19 in Hong Kong
`bootstrap_params`	Bootstrapping parameters
`bootstrap_return`	Bootstrapping pipe
`build_delay_distribution()`	Build a discretized probability distribution vector
`combining_observations`	Details on combining observations
`convolve_delays()`	Convolve delay distributions
`dating`	Dating parameters
`deconvolve_incidence()`	Infer infection events dates from delayed observations
`delay_empirical`	Empirical delays parameters
`delay_high`	High-level delay parameters
`distribution`	Distribution
`do_uncertainty_summary()`	Utility for summarising uncertainty
`.add_date_column()`	Add dates column to dataframe.
`.add_noise()`	Add noise to a series of observations.
`.are_valid_argument_values()`	Utility functions for input validity.
`.block_bootstrap()`	Apply block-bootstrapping procedure to module input
`.block_bootstrap_overlap_func()`	Helper function for block-bootstrapping
`.build_list_of_gradually_changing_delays()`	Generate a list of delay distributions with a gradual transition between two input delay distributions
`.check_class()`	Check whether the class of an object is as expected
`.check_class_parameter_name()`	Utility functions for input validity.
`.check_if_integer_value()`	Utility functions for input validity.
`.check_if_integer_vector()`	Utility functions for input validity.
`.check_if_noise()`	Utility functions for input validity.
`.check_if_non_neg_integer_vector()`	Utility functions for input validity.
`.check_if_non_negative_number()`	Utility functions for input validity.
`.check_if_null_or_belongs_to_class()`	Utility functions for input validity.
`.check_if_null_or_integer()`	Utility functions for input validity.
`.check_if_number()`	Utility functions for input validity.
`.check_if_positive_integer()`	Utility functions for input validity.
`.check_if_positive_number()`	Utility functions for input validity.
`.check_is_bootstrap_estimate()`	Utility functions for input validity.
`.check_is_delay_distribution_matrix()`	Utility functions for input validity.
`.check_is_empirical_delay_data()`	Check if input is in the proper empirical delay data format
`.check_is_estimate()`	Utility functions for input validity.
`.check_is_numeric_in_interval()`	Utility functions for input validity.
`.check_is_probability_distr_vector()`	Check that an object represents a probability distribution.
`.compute_Ot()`	Compute the number of delayed observations of infections at the current time step.
`.compute_discretized_infectiousness()`	Compute the discretized infectiousness for a particular time step.
`.convolve_delay_distribution_matrices()`	Convolve two delay distribution matrices
`.convolve_delay_distribution_vector_with_matrix()`	Convolve a delay distribution vector with a delay distribution matrix
`.convolve_delay_distribution_vectors()`	Convolve two discretized probability distribution vectors.
`.convolve_delay_distributions()`	Convolve a list of delay vectors or matrices
`.convolve_two_delay_distributions()`	Convolve two delay vectors or matrices
`.deconvolve_incidence_Richardson_Lucy()`	Deconvolve the incidence input with the Richardson-Lucy (R-L) algorithm
`.delay_distribution_matrix_rmse_compute()` `.delay_distribution_matrix_rmse_compute()`	Utility function that generates delay data, assuming a different delay between event and observation for each individual day. It then generates the delay matrix and computes the RMSE between the parameters of the gamma distributions passed as arguments and the ones recovered from the delay matrix. The shapes and scales of the gamma distributions are specified as parameters, and the number of timesteps is assumed to be equal to the length of these vectors.
`.draw_It()`	Draw the number of infections for a particular time step.
`.estimate_Re_EpiEstim_piecewise_constant()`	Estimate Re with EpiEstim in a piecewise-constant fashion
`.estimate_Re_EpiEstim_sliding_window()`	Estimate Re with EpiEstim using a sliding window
`.generate_delay_data()`	Generate artificial delay data.
`.get_arg_names()`	Get the names of the arguments of a function
`.get_delay_distribution()`	Return probability distribution vector or matrix
`.get_delay_matrix_column()`	Build a specific column of the delay distribution matrix
`.get_delay_matrix_from_delay_distributions()`	Build delay distribution matrix from a single delay or a list of delay distributions
`.get_delays_over_full_time_units()`	Get delay entries corresponding to a round number of weeks (months...)
`.get_discretized_distribution()`	Discretize a probability distribution.
`.get_distribution_function()`	Get distribution function
`.get_distribution_parms()`	Get relevant parameters from distribution
`.get_dots_as_list()`	Return dots arguments as list.
`.get_infectiousness_profile()`	Compute a discretized infectiousness profile from a serial interval distribution.
`.get_input_length()`	Get length of values vector in a module input object.
`.get_module_input()`	Transform input data into a module input object
`.get_module_output()`	Transform a result output of a module into a module output 'object'
`.get_offset()`	Get offset from module object
`.get_quantiles()`	Obtain quantile values for a distribution
`.get_right_boundary_for_distribution_vector()`	Get the number of steps before a quantile is reached.
`.get_shared_args()`	Get the arguments which apply to a function among a given list of arguments.
`.get_time_steps_quantile()`	Get initial shift for deconvolution step
`.get_values()`	Get values from module object
`.is_numeric_vector()`	Check if object is numeric vector.
`.is_positive_numeric_vector()`	Utility functions for input validity.
`.is_single_delay()`	Is input a single delay object?
`.is_valid_computation_ready_delay_object()`	Utility functions for input validity.
`.is_valid_delay_object()`	Utility functions for input validity.
`.is_valid_delay_single_or_list()`	Utility functions for input validity.
`.is_valid_distribution()`	Check if valid distribution list
`.is_valid_module_input()`	Utility functions for input validity.
`.is_value_in_accepted_values_vector()`	Utility functions for input validity.
`.is_value_valid_time_step()`	Utility functions for input validity.
`.left_augment_delay_distribution()`	Augment a delay distribution by left padding with new columns.
`.make_empty_module_output()`	Make empty module output object
`.prettify_result()`	Prettify results of pipe functions by removing leading and tailing NAs
`.print_vector()`	Utility function to print vectors in a copy-pastable format
`.random_round()`	Round a value to the integer to either the floor or ceiling value, based on a random draw.
`.sample_from_distribution()`	Draw samples from a probability distribution.
`.simplify_output()`	Simplify output object if possible
`.smooth_LOESS()`	LOESS smoothing function
`.summarise_CI_bootstrap()`	Build a confidence interval from bootstrapped values
`.summarise_bagged_mean()`	Compute bagged mean from bootstrapped replicates
`empirical_delay_data_format`	Empirical delay data format
`estimate_Re()`	Estimate the effective reproductive number Re through time from incidence data
`estimate_Re_from_noisy_delayed_incidence()`	Estimate Re from incidence data
`estimate_from_combined_observations()`	Estimate Re from delayed observations of infection events.
`expect_delay_matrix_sums_lte_1()`	Test whether a delay matrix columns sum to less than one.
`get_block_bootstrapped_estimate()`	Estimate Re from incidence and estimate uncertainty with block-bootstrapping
`get_bootstrap_replicate()`	Obtain a bootstrap replicate from incidence data
`get_bootstrapped_estimates_from_combined_observations()`	Estimate Re from incidence and estimate uncertainty by bootstrapping
`get_infections_from_incidence()`	Infer timeseries of infection events from incidence data of delayed observations
`get_matrix_from_empirical_delay_distr()`	Build matrix of delay distributions through time from empirical delay data.
`inner_addition()`	Add values from two module objects
`inner_module`	Inner module option characteristics
`left_addition()`	Add values from two module objects
`leftpad_input()`	Pad values on the left side of input
`make_tibble_from_output()`	Convert module output object into tibble
`merge_outputs()`	Merge multiple module outputs into tibble
`module_methods`	Methods available for each module
`module_objects`	Module object utilities
`module_structure`	Module structure characteristics
`nowcast()`	Correct incidence data for yet-to-be-observed fraction of events
`pipe_params`	Pipe parameters
`simulate`	Simulation parameters
`simulate_combined_observations()`	Simulate a series of observations from a course of infections, combining some partially-delayed and some fully-delayed observations.
`simulate_delayed_observations()`	Simulate a series of delayed observations from a series of infections.
`simulate_infections()`	Simulate a series of infections
`smooth_incidence()`	Smooth noisy incidence data
`summarise_uncertainty()`	Summarise the uncertainty obtained from bootstrapping
`uncertainty`	Uncertainty summary
`universal_params`	Universal parameters
`validation_utility_params`	Utility functions for input validity.

Reference

All functions