Merge pull request #7 from LucasKook/dev

Documentation and speed of examples

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: tramicp
 Type: Package
-Title: Model-Based Feature Selection for General Response Types
+Title: Model-Based Causal Feature Selection for General Response Types
 Version: 0.0-1
 Authors@R: c(person("Lucas", "Kook", role = c("aut", "cre"),
     comment = c("ORCID" = "0000-0002-7546-7356"),

R/alias.R

@@ -17,7 +17,7 @@
 #' @examples
 #' set.seed(123)
 #' d <- dgp_dicp(mod = "boxcox", n = 300)
-#' BoxCoxICP(Y ~ X2, data = d, env = ~ E)
+#' BoxCoxICP(Y ~ X2, data = d, env = ~ E, type = "wald")
 #'
 BoxCoxICP <- function(formula, data, env, verbose = TRUE, type = "residual",
                       test = "gcm.test", controls = NULL, alpha = 0.05,
@@ -40,13 +40,17 @@ BoxCoxICP <- function(formula, data, env, verbose = TRUE, type = "residual",
 #' @export
 #'
 #' @examples
+#' \donttest{
 #' set.seed(123)
 #' d <- dgp_dicp(mod = "weibull", n = 300)
 #' SurvregICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
 #' ### or
-#' # survregICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
-#' # CoxphICP(Y ~ X2, data = d, env = ~ E)
-#' # coxphICP(Y ~ X2, data = d, env = ~ E)
+#' library("survival")
+#' d$Y <- Surv(d$Y)
+#' survregICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
+#' CoxphICP(Y ~ X2, data = d, env = ~ E)
+#' coxphICP(Y ~ X2, data = d, env = ~ E)
+#' }
 #'
 SurvregICP <- function(formula, data, env, verbose = TRUE, type = "residual",
                        test = "gcm.test", controls = NULL, alpha = 0.05,
@@ -107,9 +111,11 @@ coxphICP <- function(formula, data, env, verbose = TRUE, type = "residual",
 #' @export
 #'
 #' @examples
+#' \donttest{
 #' set.seed(123)
 #' d <- dgp_dicp(mod = "colr", n = 300)
-#' ColrICP(Y ~ X1 + X2 + X3, data = d, env = ~ E, type = "wald", test = "wald")
+#' ColrICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
+#' }
 #'
 ColrICP <- function(formula, data, env, verbose = TRUE, type = "residual",
                     test = "gcm.test", controls = NULL, alpha = 0.05,
@@ -152,9 +158,11 @@ CoxphICP <- function(formula, data, env, verbose = TRUE, type = "residual",
 #' @export
 #'
 #' @examples
+#' \donttest{
 #' set.seed(123)
 #' d <- dgp_dicp(mod = "coxph", n = 300)
 #' LehmannICP(Y ~ X2, data = d, env = ~ E)
+#' }
 #'
 LehmannICP <- function(formula, data, env, verbose = TRUE, type = "residual",
                        test = "gcm.test", controls = NULL, alpha = 0.05,
@@ -177,11 +185,13 @@ LehmannICP <- function(formula, data, env, verbose = TRUE, type = "residual",
 #' @export
 #'
 #' @examples
+#' \donttest{
 #' set.seed(123)
 #' d <- dgp_dicp(mod = "lm", n = 300)
 #' LmICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
 #' ### or
-#' # lmICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
+#' lmICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
+#' }
 #'
 LmICP <- function(formula, data, env, verbose = TRUE, type = "residual",
                   test = "gcm.test", controls = NULL, alpha = 0.05,
@@ -223,11 +233,13 @@ lmICP <- function(formula, data, env, verbose = TRUE, type = "residual",
 #' @export
 #'
 #' @examples
+#' \donttest{
 #' set.seed(123)
 #' d <- dgp_dicp(mod = "polr", n = 300)
 #' PolrICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
 #' ### or
-#' # PolrICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
+#' PolrICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
+#' }
 #'
 PolrICP <- function(formula, data, env, verbose = TRUE, type = "residual",
                     test = "gcm.test", controls = NULL, alpha = 0.05,
@@ -292,9 +304,11 @@ residuals.polr <- function(object, ...) {
 #' @export
 #'
 #' @examples
+#' \donttest{
 #' set.seed(123)
 #' d <- dgp_dicp(mod = "binary", n = 300)
 #' glmICP(Y ~ X1 + X2 + X3, data = d, env = ~ E, family = "binomial")
+#' }
 #'
 glmICP <- function(formula, data, env, verbose = TRUE, type = "residual",
                    test = "gcm.test", controls = NULL, alpha = 0.05,
@@ -327,9 +341,11 @@ glmICP <- function(formula, data, env, verbose = TRUE, type = "residual",
 #' @export
 #'
 #' @examples
+#' \donttest{
 #' set.seed(123)
 #' d <- dgp_dicp(mod = "cotram", n = 300)
 #' cotramICP(Y ~ X2, data = d, env = ~ E)
+#' }
 #'
 cotramICP <- function(formula, data, env, verbose = TRUE, type = "residual",
                       test = "gcm.test", controls = NULL, alpha = 0.05,
@@ -352,9 +368,11 @@ cotramICP <- function(formula, data, env, verbose = TRUE, type = "residual",
 #' @export
 #'
 #' @examples
+#' \donttest{
 #' set.seed(123)
 #' d <- dgp_dicp(mod = "binary", n = 300)
 #' rangerICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
+#' }
 #'
 rangerICP <- function(formula, data, env, verbose = TRUE, type = "residual",
                       test = "gcm.test", controls = NULL, alpha = 0.05,

R/methods.R

@@ -94,7 +94,7 @@ summary.dICP <- function(object, print_all = FALSE, digits = 3, ...) {
 #' @examples
 #' set.seed(123)
 #' d <- dgp_dicp(n = 1e3, mod = "polr")
-#' res <- PolrICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
+#' res <- polrICP(Y ~ X1 + X2 + X3, data = d, env = ~ E, type = "wald")
 #' pvalues(res, which = "predictor")
 #' pvalues(res, which = "set")
 #' pvalues(res, which = "all")

README.md

@@ -14,12 +14,17 @@ described in [the paper](https://doi.org/10.48550/arXiv.2309.12833).
 
 # Installation
 
-The `tramicp` package can be installed via:
+The development version of `tramicp` package can be installed via:
 ```r
 # install.packages("remotes")
 remotes::install_github("LucasKook/tramicp")
 ```
 
+A stable version is available on [CRAN](https://CRAN.R-project.org/package=tramicp):
+```r
+install.packages("tramicp")
+```
+
 # Using package `tramicp`
 
 Consider the following data simulated from a structural causal model: