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Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

Author: gvegayon

README.md

@@ -10,7 +10,7 @@ Our methods can also handle outcomes missing at random. Details about
 the study design, assumptions, methodology and implementation can be
 found in the vignettes and paper.
 
-## Installment
+## Installation
 
 Users can install `comprehensivecohort` using the
 <a href="https://cran.r-project.org/package=remotes"
@@ -25,7 +25,7 @@ Or install from CRAN:
 ## Example
 
 The package includes a simulated dataset based on the TOIB study, a
-comprehensive cohort study aiming to determine whether to advice older
+comprehensive cohort study aiming to determine whether to advise older
 adults with chronic knee pain to apply either topical or oral
 non-steroidal anti-inflammatory drugs (NSAIDs) for knee pain management.
 The dataset include 563 observations with outcome $Y$ as Western Ontario

README.qmd

@@ -7,7 +7,7 @@ format: gfm
 The `comprehensivecohort` package provides functions for estimating the comprehensive cohort causal effects (CCCE) in comprehensive cohort studies. We develop a semiparametric sensitivity analysis framework for assessing the impact of unmeasured confounding in the observational arm. Our methods can also handle outcomes missing at random. Details about the study design, assumptions, methodology and implementation can be found in the vignettes and paper. 
 
 
-## Installment
+## Installation
 
 Users can install `comprehensivecohort` using the [`remotes`](https://cran.r-project.org/package=remotes){target="_blank"} R package: 
 
@@ -21,7 +21,7 @@ Or install from CRAN:
 
 ## Example
 
-The package includes a simulated dataset based on the TOIB study, a comprehensive cohort study aiming to determine whether to advice older adults with chronic knee pain to apply either topical or oral non-steroidal anti-inflammatory drugs (NSAIDs) for knee pain management. The dataset include 563 observations with outcome $Y$ as Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score ($0-100$) at 12 months. Some outcome observations might be missing (denoted as NA in $Y$). Column $M$ is a outcome missingness indicator: $1$ if $Y$ is observed, $0$ if $Y$ is missing. Other variables in the dataset include $t$ the treatment indicator ($1$ for topical NSAIDs, $0$ for oral NSAIDs), and $R$ the randomization consent indicator ($1$ for RCT, $0$ for OBS). Rest of the columns are baseline covariates (age, baseline WOMAC pain score, expected pain one year later, chronic pain grade). 
+The package includes a simulated dataset based on the TOIB study, a comprehensive cohort study aiming to determine whether to advise older adults with chronic knee pain to apply either topical or oral non-steroidal anti-inflammatory drugs (NSAIDs) for knee pain management. The dataset include 563 observations with outcome $Y$ as Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score ($0-100$) at 12 months. Some outcome observations might be missing (denoted as NA in $Y$). Column $M$ is a outcome missingness indicator: $1$ if $Y$ is observed, $0$ if $Y$ is missing. Other variables in the dataset include $t$ the treatment indicator ($1$ for topical NSAIDs, $0$ for oral NSAIDs), and $R$ the randomization consent indicator ($1$ for RCT, $0$ for OBS). Rest of the columns are baseline covariates (age, baseline WOMAC pain score, expected pain one year later, chronic pain grade).
 
 ```{r}
 #| error: true

vignettes/Intro.qmd

@@ -129,16 +129,16 @@ The dataset has 563 observations and 8 variables, which include:
 
 ## Application 
 
-The primary function is `est_psi()`, which returns point estimates, estimated variance and $95\%$ Walds confidence interval for $\psi_t(\widetilde{P}; \gamma_t)$, $\psi_{t, 0}(\widetilde{P}; \gamma_t)$ and $\psi_{t, 1}(\widetilde{P})$ under one or more pre-specified $\gamma_t$. 
+The primary function is `est_psi()`, which returns point estimates, estimated variance and $95\%$ Wald confidence interval for $\psi_t(\widetilde{P}; \gamma_t)$, $\psi_{t, 0}(\widetilde{P}; \gamma_t)$ and $\psi_{t, 1}(\widetilde{P})$ under one or more pre-specified $\gamma_t$.
 
 Users need to input data and specify several parameters: 
 
-* Data: <span style="color: blue;">Y</span>, <span style="color: blue;">M</span>, <span style="color: blue;">R</span>, <span style="color: blue;">t</span>, <span style="color: blue;">M</span> (baseline covariates in the form of data frame)
+* Data: <span style="color: blue;">Y</span>, <span style="color: blue;">M</span>, <span style="color: blue;">R</span>, <span style="color: blue;">t</span>, <span style="color: blue;">X</span> (baseline covariates as a data frame)
 * Estimand of interest: <span style="color: blue;">trt</span>=1 if estimating $E[Y(1)]$, $E[Y(1)|R=0]$ and $E[Y(1)|R=1]$; <span style="color: blue;">trt</span>=0 if estimating $E[Y(0)]$, $E[Y(0)|R=0]$ and $E[Y(0)|R=1]$
 * Sensitivity parameters: <span style="color: blue;">gamma</span>, a vector of $\gamma_t$ value. 
-* Single index model settings [@redd2025sensiatrpackageconducting]: <span style="color: blue;">kernel</span>, kernel smoothing, choices of Gaussian and Epanechnikov kernel; <span style="color: blue;">single_index_method</span>, types of constraints in estimation, choices of setting first coefficient to 1, norm of coefficients to 1, and bandwidth to 1; <span style="color: blue;">method</span>, optimization methods, default to `optim`; <span style="color: blue;">use_mave</span>, whether to apply MAVE [@mave_xia_2002; @mave_wang_2008] or cumulative sliced inverse regression method [@slice_zhu_2010] to estimate initial value of the coeffficients, default to TRUE. 
+* Single index model settings [@redd2025sensiatrpackageconducting]: <span style="color: blue;">kernel</span>, kernel smoothing, choices of Gaussian and Epanechnikov kernel; <span style="color: blue;">single_index_method</span>, types of constraints in estimation, choices of setting first coefficient to 1, norm of coefficients to 1, and bandwidth to 1; <span style="color: blue;">method</span>, optimization methods, default to `optim`; <span style="color: blue;">use_mave</span>, whether to apply MAVE [@mave_xia_2002; @mave_wang_2008] or cumulative sliced inverse regression method [@slice_zhu_2010] to estimate initial value of the coefficients, default to TRUE. 
 * Truncation methods: <span style="color: blue;">simple_trunc</span>=TRUE to apply quantile truncation of $\frac{1}{\pi_{t, r}(X)}$ and $\frac{1}{\eta_m(X, r, t)}$, <span style="color: blue;">simple_trunc</span>=FALSE to apply tuning-free Huberization procedure [@tuning_wang_2021] to influence functions. If <span style="color: blue;">simple_trunc</span>=TRUE, specify quantile truncation by setting <span style="color: blue;">quant</span> from 0 to 1. 
-* K-fold sample splitting: <span style="color: blue;">fold</span>=K, a integer. <span style="color: blue;">seed</span>, set.seed(seed). 
+* K-fold sample splitting: <span style="color: blue;">fold</span>=K, an integer. <span style="color: blue;">seed</span>, set.seed(seed).
 
 Here is an example of inferences for $\psi_1(\widetilde{P}; \gamma_1)$, $\psi_{1, 0}(\widetilde{P}; \gamma_1)$ and $\psi_{1, 1}(\widetilde{P})$ under $\gamma_1=0, 0.5$, using 5-fold sample splitting, influence function truncation procedure and specific single index model settings.