Man pages for FrankLef/fciR
Data and functions used with Fundamentals of Causal Inference by Babette Brumback
| audit_formula | Audit the Formula Used for Causal Inference |
| backdr_dr | Compute the doubly robust standardized estimates |
| backdr_dr_bad | Doubly robust standardized estimates with misspecified... |
| backdr_exp | Compute Standardized Estimates With Parametric Exposure Model |
| backdr_exp_gee | Compute standardized estimates with parametric exposure model |
| backdr_exp_np | Compute Standardized Averages Using Exposure Modeling, Non... |
| backdr_out | Compute standardized estimates with parametric outcome model |
| backdr_out_np | Standardized estimates via Outcome Modeling, Non-Parametric |
| backdr_out_sat | Standardized estimates via Outcome Modeling, Saturated... |
| backdr_twoparts | Compute standardized estimates with the 2-parts model |
| boot_est | Get estimate and CI using a function as input |
| bootR_run | Bootstrapping Confidence Intervals with Base R |
| boot_run | Bootstrapping Confidence Intervals with Tidyverse |
| calc_prob | Calculate Probabilities |
| calc_prob_cond | Calculate Probabilities Conditional on Other Variables |
| cogdat | Children's Oncology Group from Robins 1989 |
| did_linear | Compute the DiD Estimator with a Linear Model |
| did_logistic | Compute the DiD Estimator with a Lofistic Model |
| did_loglinear | Compute the DiD Estimator with a Loglinear Model |
| did_longer | Convert a data.frame to long format |
| doublewhatifdat | Double What-If study Simulation |
| effect_measures | Calculate the effect measures |
| effect_transf | Convert Dataframe of Effect Measures to Its Inverse |
| effect_transf_proc | Convert Dataframe of Effect Measures to Its Inverse |
| fciR | A package to study with Fundamentals of Causal Inference by... |
| fci_sim_08_01 | Simulation 8.1 |
| fci_tbl_03_02 | Table 3.2 |
| fci_tbl_04_02 | Table 4.2 |
| fci_tbl_05_01 | Table 5.1 |
| fci_tbl_06_01 | Table 6.1 |
| fci_tbl_06_04 | Table 6.4 |
| fci_tbl_06_07 | Table 6.7 |
| fci_tbl_06_09 | Table 6.9 |
| fci_tbl_06_13 | Table 6.3 and 6.14 |
| fci_tbl_07_02 | Table 7.2 |
| fci_tbl_09_01 | Table 9.1 |
| fci_tbl_09_01a | Table 9.1 using qt() instead of 1.96 for CI |
| frontdr_np | Estimate the Effect Using the Front-Door Method |
| ggp_dag | Plot a DAG with ggplot and node names with subscripts |
| ggp_format | Create a list of argumaents used by ggp_x custom functions |
| ggp_measures | Plot of effect measures |
| ggp_measures_groups | Plot of effect measures by group |
| ggp_measures_modif | Create a plot of effect-measures modifications |
| gss | Dataset from 2018 GSS |
| gt2ggp | Convert 'gt_tbl' to 'ggplot' |
| gt_basic | Basic format of table created with 'gt' |
| gt_measures | Create a table of effect measures with their CI |
| gt_measures_colgrp | Create a table of effect-measure modifications with their CI |
| gt_measures_rowgrp | Create a table of effect measures with their CI |
| gt_probs | Create table of probabilities with 'gt' package |
| gt_standdr | Create a table from the result of simulating doubly robust |
| instr_linear | Estimate Effect Using Instrument Variables |
| instr_logistic | Estimate Effect Using Instrument Variables via Logistic Fit |
| instr_loglinear | Estimate Effect Using Instrument Variables via Logarithmic... |
| instr_vars | Compute ITT, CACE and ATT from Instrument Variables |
| jack_ci | Compute the Confidence Interval Estimated with Jacknife |
| jack_est | Estimate of Effect Measure and CI With Jacknife (LOO) |
| jack_run | Estimate of Effect Measure and CI With Jacknife (LOO) |
| mc_beta_effect_measures | Monte Carlo Sim of Effect Measures using the Beta... |
| mc_standdr | Monte Carlo Simulation of Doubly Robust Standardization |
| meas_effect_cond | Compute estimates of the conditional association measures |
| meas_effect_modif | Compute estimates of the association measures for 2 strata |
| meas_effect_uncond | Compute estimates of the unconditional association measures |
| mediation | Estimate Mediation Effect with Parametric Assumptions |
| mediation_calc | Calculate the mediation variables. |
| mediation_NIE | Estimate the Natural Indirect Effect of a Mediator Variable |
| mediation_np | Estimate Non-parametric Mediation Effect |
| mortality | Mortality Rates by Age and Country |
| mortality_long | Mortality Rates by Age and Country in long format. |
| nces | Admissions data from the NCES IPEDS 2018-2019 provisionally. |
| precision_eff | Compute Precision efficiency |
| precision_stats | Compute Stats on Precision Efficiency |
| prob_lmod | Estimate s sampling distribution by Bootstrapping |
| prob_lmod_td | Estimate s sampling distribution by Bootstrapping |
| prop_quant | Stratifying on the Quantiles of the Propensity Score |
| prop_scores | Fit the Propensity Score Model |
| recovery | RECOVERY trial of dexamethasone COVID-10 Collaborative Group |
| sepsis | University of Florida Sepsis and Critical Illness (2017) |
| sepsisb | University of Florida Sepsis and Critical Illness (2017) |
| sim_dag01 | Simulate DAG # 1. Table 5.1. |
| sim_doublewhatif | 'doublewhatifsim' script rewritten |
| sim_intervals | Simulate a sampling distribution |
| standdr_est | Estimates from Doubly Robust Standardization Simulation |
| standdr_sim | Data Simulation for Doubly Robust Standardization |
| standdr_stats | Compute Statistics from 'standdr_sim'. |
| time_msm | Estimate Using Marginal Structural Models |
| time_odtr | Optimal Dynamic Treatment Regime: All steps |
| time_odtr_optA1A2 | Optimal Dynamic Treatment Regime: Step 3 |
| time_odtr_optA2 | Optimal Dynamic Treatment Regime: Step 2 |
| time_odtr_optimal | Optimal Dynamic Treatment Regime: Step 4 |
| time_odtr_prop | Optimal Dynamic Treatment Regime: Step 1 |
| time_snmm | Estimate Using Structural Nested Mean Models |
| whatif2dat | What-If study (Cook et al (2019)) with extended data |
| whatifdat | What-If study (Cook et al (2019)) |
