Typo fixed

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: ciccr
 Type: Package
 Title: Causal Inference in Case-Control Studies
-Version: 0.1.0.9000
+Version: 0.2.0
 Authors@R: c(
     person("Sung Jae", "Jun", email = "suj14@psu.edu", role = "aut"),
     person("Sokbae", "Lee", email = "sl3841@columbia.edu", role = c("aut", "cre")))

R/ciccr.R

@@ -2,16 +2,16 @@
 #' @name ciccr
 #' @title ciccr: a package for causal inference in case-control and case-population studies
 #'
-#' @description The ciccr package provides methods for causal infernce in case-control and case-population studies
+#' @description The ciccr package provides methods for causal inference in case-control and case-population studies
 #' under the monotone treatment response (MTR) and monotone treatment selection (MTS) assumptions.
 #'
 #' @section Functions:
 #' The package includes the following:
 #' \itemize{
 #' \item{\code{\link{cicc_plot}}: }{plots upper bounds on relative and attributable risk.}
-#' \item{\code{\link{cicc_RR}}: }{carries out causual inference on relative risk.}
+#' \item{\code{\link{cicc_RR}}: }{carries out causal inference on relative risk.}
 #' \item{\code{\link{avg_RR_logit}}: }{averages the log odds ratio using retrospective logistic regression.}
-#' \item{\code{\link{cicc_AR}}: }{carries out causual inference on attributable risk.}
+#' \item{\code{\link{cicc_AR}}: }{carries out causal inference on attributable risk.}
 #' \item{\code{\link{avg_AR_logit}}: }{averages the upper bound on causal attributable risk using prospective and retrospective logistic regression models.}
 #' \item{\code{\link{ACS_CC}}: }{provides an illustrative case-control sample.}
 #' \item{\code{\link{ACS_CP}}: }{provides an illustrative case-population sample.}

R/data_CC.R

@@ -1,7 +1,7 @@
 #' ACS_CC
 #'
 #' A case-control sample extracted from American Community Survey (ACS) 2018, restricted to white males residing in California with at least a bachelor's degree.
-#' The orginial ACS dataset is not from case-control sampling, but this case-control sample is obtained by the following procedure.
+#' The original ACS dataset is not from case-control sampling, but this case-control sample is obtained by the following procedure.
 #' The case sample is composed of 921 individuals whose income is top-coded.
 #' The control sample of equal size is randomly drawn without replacement from the pool of individuals whose income is not top-coded.
 #' Age is restricted to be between 25 and 70.

R/data_CP.R

@@ -1,7 +1,7 @@
 #' ACS_CP
 #'
 #' A case-population sample extracted from American Community Survey (ACS) 2018, restricted to white males residing in California with at least a bachelor's degree.
-#' The orginial ACS dataset is not from case-population sampling, but this case-population sample is obtained by the following procedure.
+#' The original ACS dataset is not from case-population sampling, but this case-population sample is obtained by the following procedure.
 #' The case sample is composed of 921 individuals whose income is top-coded.
 #' The control sample of equal size is randomly drawn with replacement from all observations and its top-coded status is coded missing.
 #' Age is restricted to be between 25 and 70.

vignettes/ciccr-vignette.Rmd

@@ -131,7 +131,7 @@ Here, the relevant coefficient is 2.06 (`t`) and its two-sided 90% confidence in
 ### Causal Inference on Attributable Risk Using Case-Control Samples 
 
 We now consider attributable risk, that is the absolute difference in probabilities. 
-We carry out causal inference on attributale risk by
+We carry out causal inference on attributable risk by
 
 ```{r}
 results_AR = cicc_AR(y, t, x, sampling = 'cc', no_boot = 100)
@@ -191,7 +191,7 @@ Note that the estimates and upper bounds are constant across the unknown true ca
 
 ### Causal Inference on Attributable Risk Using Case-Population Samples 
 
-We now consider causal inference on attributale risk by
+We now consider causal inference on attributable risk by
 
 ```{r}
 results_AR = cicc_AR(y, t, x, sampling = 'cp', no_boot = 100)