Function reference • causalRisk

Estimation functions
`estimate_ipwrisk()`	Estimates the cumulative risk with time-varying predictors of censoring using inverse-probability weighted estimating functions
`estimate_gcomprisk()`	Estimates the cumulative risk of a right-censored outcome using a g-computation estimator.
`estimate_aipwrisk()`	Estimates the cumulative risk of a right-censored outcome using an augmented inverse probability weighted estimator.
`estimate_ipwhr()`	Estimates the hazard ratio of a treatment with time-varying predictors of censoring using inverse-probability weighted estimating functions
`re_estimate()`	A generic function to refit/re-estimate parameters described in a `result` object
`compare_fits()`	compare_fits: a function for computing the difference and 95% confidence interval between fit object point estimates using bootstrap estimates to generate a standard error
Table-making functions
`make_table1()`	Makes a unweighted or IP weighted Table 1
`make_table2()`	Create a table 2 displaying information such as the number of subjects, total amount of observed person-time, number of events, estimates and effect measures of interest, and confidence intervals.
`make_wt_summary_table()`	Summarizes weights from a set of objects that inherit from class `ipw`
`extreme_weights()`	Creates a table of the most extreme weights from an `ipw` object
Plotting functions
`plot(<hr>)` `plot(<cumcount>)` `plot(<cumrisk>)`	Plot a panel of (possibly overlayed) estimates of interest or effect measures and associated confidence intervals
`hist(<ipw>)`	Plots a panel of propensity score histograms
`forest_plot(<cumrisk>)`	Produces a forest plot of cumulative risks or measures of effect
`forest_plot(<hr>)`	Forest plot method for classes inheriting `hr`
Model specification functions
`specify_models()`	Specifies the data structure and models for a data set
`identify_outcome()`	Helper function for identifying an outcome variable for a `model_specification` object
`identify_censoring()`	Helper function for identifying a censoring variable for a `model_specification` object
`identify_treatment()`	Helper function for identifying a treatmement for a `model_specification` object
`identify_competing_risk()`	Helper function for adding a competing risk feature to a `model_specification` object
`identify_subject()`	Helper function for adding a subject identifier feature to a `model_specification` object
`identify_interval()`	Helper function for identifying start and stop times for observations in a longitudinal data structure
`identify_missing()`	Helper function for activating missing data weighting for a `model_specification` object
Functions to modify model specification in a result object
`update_treatment()`	Updates the treatment information in a `result` object for later re-estimation
`update_censoring()`	Updates the censoring information in a `result` object for later re-estimation
`update_outcome()`	Updates the outcome information in a `result` object for later re-estimation
`update_missing()`	Updates the missingness information in a `result` object for later re-estimation
`subgroup()`	Updates a `result` object with a subgroup for later re-estimation.
Miscellaneous functions used internally
`StatStepribbon`	ggplot2 geom to create stairstep confidence bands for cumulative risk estimates
`boot_vector_create()`	Function that creates a vector of bootstrap resampling weights
`check_arguments()`	Checks standard arguments to estimation functions
`compare_protocols()`	Estimating cumulative risk across a list of treatment protocols
`compute_cumcount_ci()`	Computes confidence intervals for cumulative count estimates
`compute_cumcount_effect_ci()`	Computes confidence intervals for functions of cumulative count estimates
`compute_cumrisk_ci()`	Computes confidence intervals for cumulative risk estimates
`compute_cumrisk_effect_ci()`	Computes confidence intervals for functions of cumulative risk estimates
`create_censoring_indicators()`	Builds a matrix of censoring indicators for a set of variables.
`create_censoring_indicators_timevar_old()`	Builds a matrix of censoring indicators for a set of variables.
`cumriskdif_ci()`	Computes cumulative risk differences and confidence intervals
`cumriskrr_ci()`	Computes cumulative risk differences and confidence intervals
`estimate_aipwrisk_efficient()`	Estimates the cumulative risk of a right-censored outcome using
`estimate_censor_coxph()`	estimate_censor_coxph: a function to estimate a censoring probability using coxph
`estimate_censor_cph()`	estimate_censor_cph: a function to estimate a censoring probability using cph
`estimate_censoring_prob()`	estimate_censoring_prob: a function to estimate a probability a patient will be uncensored
`estimate_ipwcount()`	Estimates the cumulative count with time-varying predictors of censoring using inverse-probability weighted estimating functions
`estimate_ipweights()`	Estimates the inverse_probability weights when no outcome is specified
`estimate_missing_glmnet()`	estimate_missing_glmnet: a function to estimate a missingness probability using logistic regression
`estimate_missing_prob()`	estimate_missing_prob: a function to estimate a missingness probability
`estimate_outcome_coxph()`	estimate_outcome_coxph: a function to estimate outcome probabilities at times in new data using coxph
`estimate_outcome_cph()`	estimate_outcome_cph: a function to estimate an outcome risk using cph function in R
`estimate_ps()`	estimate_ps: a function to estimate a propensity score
`estimate_ps_glm()`	estimate_ps_glm: a function to estimate a propensity score using glm logistic regression
`estimate_ps_glmnet()`	estimate_ps_glmnet: a function to estimate a propensity score using logistic regression
`estimate_rates()`	Estimates crude event rates
`inspect_ipw_weights()`	Extract the weights from a fitted IP weighted risk object
`find_covs()`	find_covs: a function to identify covariates used in a model
`find_empty()`	find_empty: a function to identify factors that only have individuals in a single level
`find_missing()`	find_missing: a function to identify rows with missing data and variables that have missing values
`fit_coxph()`	fit_coxph: a function to fit a coxph model
`forest_plot()`	A generic function to produce of forest plot estimates from `results` objects
`identify_count()`	Helper function for identifying a count variable for a `model_specification` object
`initializer()`	Standard set-up needed by estimating functions
`is.aipw()`	is.aipw: a function for checking whether an object inherits from `aipw`
`is.boot_est()`	is.boot_est: a function for checking whether an object inherits from `boot_est`
`is.cumcount()`	is.cumcount: a function for checking whether an object inherits from `cumcount`
`is.cumrisk()`	is.cumrisk: a function for checking whether an object inherits from `cumrisk`
`is.gcomp()`	is.gcomp: a function for checking whether an object inherits from `gcomp`
`is.ipw()`	is.ipw: a function for checking whether an object inherits from `ipw`
`is.hr()`	is.hr: a function for checking whether an object inherits from `hr`
`is.model_specification()`	is.model_specification: a function for checking whether an object inherits from `model_specification`
`logit_ps_est()`	A function to estimate a propensity score using logistic regression
`named_list()`	Function to return a named list
`plot(<hr>)` `plot(<cumcount>)` `plot(<cumrisk>)`	Plot a panel of (possibly overlayed) estimates of interest or effect measures and associated confidence intervals
`plot_boot_cumrisk()`	Plots a panel of (possibly overlayed) cumulative risk functions or cumulative risk differences and confidence intervals.
`predict(<survfit>)`	Predict probabilities from a `survfit` object
`print(<result>)`	Provides a print method for `result` objects
`re_estimate(<cumcount>)`	Re-estimates a `result` object after elements of the `model_specifaction` have been modified.
`re_estimate(<cumrisk>)`	Re-estimates a `result` object after elements of the `model_specifaction` have been modified.
`re_estimate(<no_outcome>)`	Re-estimates a `no_outcome` object after elements of the `model_specifaction` have been modified.
`sim()`	Iterates a simulation function design to assess bias across different boostrap procedures
`sim_eval()`	Evaluates the result of a simulation study
`sim_func1()`	Simulates a simple data structure with potential confounding from two variables, dependent censoring, and a competing risk
`smd()`	Compute a standardized mean difference for two groups
`stack_cumcount()`	Stack cumulative count curves from each treatment group in a results object (or list of objects) in a matrix
`stack_cumcount_boot()`	Stack bootstrapped cumulative count curves from each treatment group in a results object (or list of objects) in a matrix
`stack_cumrisk()`	Stack cumulative risk curves from each treatment group in a results object (or list of objects) in a matrix
`stack_cumrisk_boot()`	Stack bootstrapped cumulative risk curves from each treatment group in a results object (or list of objects) in a matrix
`table2_helper_cumcount()`	Helper function for make_table2.cumcount
`table2_helper_cumrisk()`	Helper function for make_table2.cumrisk
`trim_ps()`	Function for symmetric trimming predicted probabilites
`update_count()`	Updates the count information in a `result` object for later re-estimation
`update_label()`	Updates the label in a result object
`wt_comp()`	Computes treatment weights
`hr_data()`	Extract hazard ratios, confidence intervals and subgroup labels
Datasets
`actg`	AIDS Clinical Trial Group (ACTG) Study Dataset
`example1`	Simulated example right censored data with confounding and dependant censoring.
`example1_timevar`	Simulated example right censored data with confounding, dependant censoring, and time-varying covariates.
`example2`	Simulated example data for `ipwrisk`
`example3`	Simulated example data for demonstration of the IPMW functionality of `causalRisk`
`example4`	Simulated example data for demonstration of the cumulative count functionality of `causalRisk`
`leukemia`	Leukemia Data Set
`wihs`	WIHS Data Set

Reference

Estimation functions

Table-making functions

Plotting functions

Model specification functions

Functions to modify model specification in a result object

Miscellaneous functions used internally

Datasets