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All functions

assert_columns()
Assert that args in ellipses are columns in df
assert_columns_unique()
Assert that column names are not identical
assert_df()
Assert that df is a data frame
assert_error_correct_avg_trend()
If using error_correct, then check that the columns are either in the formula_vars or average_cols, otherwise produce an error.
assert_function()
Assert that x is a function
assert_group_models()
Assert if group_models TRUE then group_col not NULL
assert_group_sort_col()
Assert sort column for use in average trend functions
assert_h()
Assert that h, for forecasting, is > 0
assert_inla()
Assert if INLA is installed, for use in predict_inla...() functions.
assert_model()
Assert that x is a function
assert_numeric()
Assert numeric value
assert_numeric_cols()
Assert columns in df are numeric
assert_numeric_cols_avg()
Assert columns in df are numeric, for use with average trend functions
assert_string()
Assert that x is a character vector of length n
assert_test_col()
Assert that test_col is of logical type
augury_add_columns()
Adds empty columns to df
calculate_aarr()
Extract AARR from vector of years and prevalence
calculate_sq_ch()
Calculate change error
covariates_df
Default covariates for use in augury functions.
error_correct_fn()
Use mean error to correct predictions
expand_df()
Expand input data to make explicit missing values
expand_df_filter()
Filter expand_df
expand_df_min()
Helper for expand_df_filter() to calculate min for keeping data
filter_model_data()
Filters data for modeling
fit_aarr_model()
Generate prediction from model object
fit_forecast_average_model()
Fit forecast model to averages and apply trend to original data
fit_forecast_model()
Fit forecast model to data
fit_general_average_model()
Fit general model to averages and apply trend to original data
fit_general_model()
Fit general model to data
fit_inla_average_model()
Fit INLA model to averages and apply trend to original data
fit_inla_model()
Fit INLA model to data
fit_lme4_average_model()
Fit mixed model to averages and apply trend to original data
fit_lme4_model()
Fit general model to data
forecast_series()
Forecast data series
get_average_df()
Produces averaged data frame that can then be passed for modelling.
get_forecast_data()
Get data for forecast models
get_formula_avg_cols()
Get variables that need to be averaged from formula.
get_model_data()
Minimizes dataset to data needed for modelling
interpolate_aarr()
Interpolate using AARR from vector of years and prevalence
join_covariates_df()
Join data frame with covariates data frame
map_model_behavior()
Catch instability of INLA
merge_average_df()
Merge average df with predictions with original data frame
merge_prediction()
Merge predicted data into data frame
model_error()
Get modeling error from a data frame
parse_formula()
Asserts formula and extract variables
parse_obs_filter()
Parse obs filter intro string to be evaluated
predict_aarr()
Use annual average rate of reduction (AARR) to predict prevalence
predict_average()
Use averages to impute and forecast data
predict_average_fn()
Impute data using simple averages
predict_forecast()
Use a time series model to infill and project data
predict_forecast_avg_trend()
Use predict_forecast on groups to generate average trend and apply to original data
predict_forecast_data()
Generate prediction from model object
predict_general_data()
Generate prediction from model object
predict_general_mdl()
Use a generic R model to infill and project data
predict_general_mdl_avg_trend()
Use predict_general_mdl on groups to generate average trend and apply to original data
predict_glm()
Use a generalized linear model to infill and project data
predict_glm_avg_trend()
Use predict_glm on groups to generate average trend and apply to original data
predict_glmer()
Use a generalized linear mixed-effects model to infill and project data
predict_glmer_avg_trend()
Use predict_glmer on groups to generate average trend and apply to original data
predict_holt()
Use Holt's linear trend exponential smoothing to forecast data
predict_holt_avg_trend()
Use predict_holt on groups to generate average trend and apply to original data
predict_inla()
Use Bayesian analysis of additive models to infill and project data
predict_inla_avg_trend()
Use predict_inla on groups to generate average trend and apply to original data
predict_inla_data()
Generate prediction from an INLA output object
predict_inla_me()
Use INLA for mixed effects modeling for prediction
predict_inla_ts()
Use INLA for time series prediction
predict_lm()
Use a linear model to infill and project data
predict_lm_avg_trend()
Use predict_lm on groups to generate average trend and apply to original data
predict_lme4()
Use mixed models to infill and project data
predict_lme4_avg_trend()
Use predict_lme4 on groups to generate average trend and apply to original data
predict_lme4_data()
Generate prediction from model object
predict_lmer()
Use a linear mixed-effects model to infill and project data
predict_lmer_avg_trend()
Use predict_lmer on groups to generate average trend and apply to original data
predict_nlmer()
Use a non-linear mixed-effects model to infill and project data
predict_nlmer_avg_trend()
Use predict_nlmer on groups to generate average trend and apply to original data
predict_ses()
Use simple exponential smoothing to forecast data
predict_ses_avg_trend()
Use predict_ses on groups to generate average trend and apply to original data
predict_simple()
Use linear interpolation and flat extrapolation to infill data
predict_simple_fn()
Linearly interpolate data
probit_transform()
Probit transform bounded data in a data frame
probit_vec()
Probit transformation for bounded data
remove_groups()
Remove groups from data frame if grouped
scale_transform()
Scales data in a data frame
scale_vec()
Scale a vector
simple_extrap()
Helper function to do flat extrapolation
temp_fill()
Fills vector backwards and forward, for use prior to applying average trend
trim_series()
Get latest data for forecasting