Package index • wwinference

All functions

add_pmfs(): Add probability mass functions

add_time_indexing(): Add time indexing to count data

assert_cols_det_unique_row(): Check a set of columns in a data frame uniquely identify data frame rows.

assert_daily_data(): Assert that the vector of dates being passed in contains dates for each day

assert_dates_within_frame(): Assert that the second vector of dates is within the period after the first date in the first set of dats and the maximum date

assert_df_not_empty(): Assert that the dataframe being passed to the function is not empty

assert_elements_non_neg(): Assert that all elements of a vector are non-negative

assert_equivalent_indexing(): Assert that two tibbles of date and time mapping align

assert_int_or_char(): Assert that a vector is either of a vector of integers or a vector of characters

assert_no_dates_after_max(): Check that all dates in dataframe passed in are before a specified date

assert_no_repeated_elements(): Check that there are no repeated elements in the vector

assert_non_missingness(): Assert that there is no missignness in a particular vector

assert_req_count_cols_present(): Check that the input count data contains all the required column names

assert_req_ww_cols_present(): Check that the input wastewater data contains all the required column names

assert_rt_correct_length(): Assert that R(t) specified for generating simulated data is of sufficient length

assert_single_value(): Assert that there is only a single value in a particular column

assert_site_lab_indices_align(): Assert that the specified site and lab indices line up

assert_sufficient_days_of_data(): Assert that the vector of dates spans at least the specified calibration time

assert_ww_site_pops_lt_total(): Assert that the sum of the wastewater site populations don't exceed the total population

autoescape_brackets(): Escape brackets returned in a string for passing to glue

calc_rt(): Back- calculate R(t) from incident infections and the generation interval

compile_model(): Compile a stan model while pointing at the package default include directory (stan) for #include statements

convert_to_logmean(): Get the mean of a Normal distribution for a random variable Y needed to ensure that the distribution of X = exp(Y) (which is Log-Normal) has a specified mean and sd.

convert_to_logsd(): Get the sd of a Normal distribution for a random variable Y needed to ensure that the distribution of X = exp(Y) (which is Log-Normal) has a specified mean and sd.

create_dir(): Create a new directory if one doesn't exist

create_site_lab_map(): Create a mapping of sites, labs, and population sizes in each site

default_covid_gi: COVID-19 post-Omicron generation interval probability mass function

default_covid_inf_to_hosp: COVID-19 time delay distribution from infection to hospital admission

downsample_ww_obs(): Downsample the predicted wastewater concentrations based on the lab site reporting frequency and lab site reporting latencyy

drop_first_and_renormalize(): Drop the first element of a simplex and re-normalize the result to sum to 1.

flag_ww_outliers(): Flag WW outliers

format_hosp_data(): Format the hospital admissions data into a tidy dataframe

format_subpop_hosp_data(): Format the subpopulation-level hospital admissions data into a tidy dataframe

format_ww_data(): Format the wastewater data as a tidy data frame

generate_simulated_data(): Generate simulated data from the underlying model's generative process

get_count_data_sizes(): Get count data integer sizes for stan

get_count_indices(): Get count data indices

get_count_values(): Get count values

get_date_time_spine(): Get date time spine to map to model output

get_draws() get_draws_df() plot(<wwinference_fit_draws>): Postprocess to generate a draws dataframe

get_global_rt(): Function to generate a global weekly R(t) estimate with added noise

get_ind_m(): Get index matrix

get_inits_for_one_chain(): Given a set of prior parameters and stan data, initialize the model near the center of the prior distribution

get_input_count_data_for_stan(): Get the input count data to pass directly to stan

get_input_ww_data_for_stan(): Get the input ww data passed directly to stan

get_lab_site_site_spine(): Get mapping from lab-site to site

get_lab_site_subpop_spine(): Get lab-site subpopulation spine

get_mcmc_options(): Get MCMC options

get_model_diagnostic_flags(): Get a table of diagnostic flags

get_model_spec(): Get model specifications

get_params(): Get parameters for model run

get_plot_forecasted_counts(): Get plot of fit and forecasted counts

get_plot_global_rt(): Get plot of fit, nowcasted, and forecasted "global" R(t)

get_plot_subpop_rt(): Get plot of fit, nowcasted, and forecasted R(t) in each subpopulation

get_plot_ww_conc(): Get plot of fit and forecasted wastewater concentrations

get_pred_obs_conc(): Get the predicted concentrations in each lab site

get_pred_subpop_gen_per_n(): Get the predicted genomes per person in each subpopulation

get_site_subpop_spine(): Get site to subpopulation map

get_stan_data(): Get stan data for ww + hosp model

get_time_varying_daily_ihr(): Get time varying IHR

get_ww_data_sizes(): Get the integer sizes of the wastewater input data

get_ww_indices_and_values(): Get wastewater indices and values for stan

hosp_data: Example hospital admissions dataset

hosp_data_eval: Example hospital admissions dataset for evaluation

indicate_ww_exclusions(): Indicate data that we want to exclude from model fitting

make_hospital_onset_delay_pmf(): Make hospital onset delay pmf

make_incubation_period_pmf(): Make incubation period pmf

make_reporting_delay_pmf(): Make reporting delay pmf

parameter_diagnostics(): Method for printing the CmdStan parameter diagnostics for a wwinference_fit_object

preprocess_count_data(): Pre-process hospital admissions data, converting column names to those that get_stan_data() expects.

preprocess_ww_data(): Pre-process wastewater input data, adding needed indices and flagging potential outliers

simulate_double_censored_pmf(): Simulate daily double censored PMF. From epinowcast: https://package.epinowcast.org/dev/reference/simulate_double_censored_pmf.html #nolint

subpop_hosp_data: Example subpopulation level hospital admissions dataset

subpop_hosp_data_eval: Example subpopulation level retrospective hospital admissions dataset

subpop_inf_process(): Get the subpopulation level incident infections

subpop_rt_process(): Get subpopulation level R(t) estimates assuming time and space independence

summary_diagnostics(): Method for printing the CmdStan summary diagnostics for wwinference_fit_object

throw_type_error(): Throw an informative type error on a user-provided input

to_simplex(): Normalize vector to a simplex

true_global_rt: Global R(t) estimate dataset

validate_count_data(): Validate user-provided count data

validate_data_jointly(): Validate that both count data and wastewater data are coherent and compatible with one another and the the user-specified parameters

validate_paramlist(): Validate a parameter list

validate_pmf(): Validate that the pmf vector being passed to stan is a valid probability mass function. It must sum to 1 and have all non-negative entries.

validate_ww_conc_data(): Validate user-provided wastewater concentration data

ww_data: Example wastewater dataset.

ww_data_eval: Example evaluation wastewater dataset.

wwinference() print(<wwinference_fit>) summary(<wwinference_fit>): Joint inference of count data (e.g. cases/admissions) and wastewater data