version bump

ncn-foreigners · Jan 30, 2025 · 76888f0 · 76888f0
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: nonprobsvy
 Type: Package
 Title: Inference Based on Non-Probability Samples
-Version: 0.1.2
+Version: 0.2.0
 Authors@R:
     c(person(given = "Łukasz",
              family = "Chrostowski",

diff --git a/NEWS.md b/NEWS.md
@@ -1,6 +1,6 @@
 # nonprobsvy News and Updates
 
-# nonprobsvy 0.2
+## nonprobsvy 0.2
 
 ------------------------------------------------------------------------
 
@@ -12,8 +12,8 @@
 -   function `genSimData` removed completely as it is not used anywhere
     in the package.
 -   argument `maxLik_method` renamed to `maxlik_method` in the
-    `control_sel` function.
-
+    `control_sel` function. 
+    
 ### Features
 
 -   two additional datasets have been included: `jvs` (Job Vacancy
@@ -35,13 +35,15 @@
 ### Other
 
 -   more informative error messages added.
+-   documentation improved.
+-   switching completely to snake_case.
 
 ### Documentation
 
 -   annotation has been added that arguments such as `strata`, `subset`
     and `na_action` are not supported for the time being.
 
-# nonprobsvy 0.1.1
+## nonprobsvy 0.1.1
 
 ------------------------------------------------------------------------
 
@@ -84,7 +86,7 @@
 -   added unit tests for variable selection models and mi estimation
     with vector of population totals available
 
-# nonprobsvy 0.1.0
+## nonprobsvy 0.1.0
 
 ------------------------------------------------------------------------
 

diff --git a/R/boot_dr.R b/R/boot_dr.R
@@ -1,4 +1,4 @@
-bootDR <- function(outcome,
+boot_dr <- function(outcome,
                    data,
                    svydesign,
                    SelectionModel,
@@ -52,7 +52,7 @@ bootDR <- function(outcome,
     p <- ncol(X)
     y_rand <- vector(mode = "numeric", length = n_rand)
     y <- c(y_rand, OutcomeModel$y_nons) # outcome variable for joint model
-    var_obj <- bootDR_sel(
+    var_obj <- boot_dr_sel(
       X = X,
       R = R,
       y = y,
@@ -261,7 +261,7 @@ bootDR <- function(outcome,
 #' @importFrom parallel makeCluster
 #' @importFrom parallel stopCluster
 #' @importFrom doParallel registerDoParallel
-bootDR_multicore <- function(outcome,
+boot_dr_multicore <- function(outcome,
                              data,
                              svydesign,
                              SelectionModel,
@@ -316,7 +316,7 @@ bootDR_multicore <- function(outcome,
     p <- ncol(X)
     y_rand <- vector(mode = "numeric", length = n_rand)
     y <- c(y_rand, OutcomeModel$y_nons) # outcome variable for joint model
-    var_obj <- bootDR_sel_multicore(
+    var_obj <- boot_dr_sel_multicore(
       X = X,
       R = R,
       y = y,
@@ -346,9 +346,9 @@ bootDR_multicore <- function(outcome,
     doParallel::registerDoParallel(cl)
     on.exit(parallel::stopCluster(cl))
     parallel::clusterExport(cl = cl, varlist = c(
-      "internal_selection", "internal_outcome", "logit_model_nonprobsvy", "start_fit", "get_method", "controlSel", "theta_h_estimation",
+      "internal_selection", "internal_outcome", "logit_model_nonprobsvy", "start_fit", "get_method", "control_sel", "theta_h_estimation",
       "mle", "mu_hatDR", "probit_model_nonprobsvy", "cloglog_model_nonprobsvy", "glm_nonprobsvy", "nn_nonprobsvy", "pmm_nonprobsvy",
-      "gaussian_nonprobsvy", "poisson_nonprobsvy", "binomial_nonprobsvy", "nonprobMI_fit", "controlOut"
+      "gaussian_nonprobsvy", "poisson_nonprobsvy", "binomial_nonprobsvy", "nonprob_mi_fit", "control_out"
     ))
     if (is.null(pop_totals)) {
       N <- sum(weights_rand)

diff --git a/R/boot_dr_sel.R b/R/boot_dr_sel.R
@@ -1,4 +1,4 @@
-bootDR_sel <- function(X,
+boot_dr_sel <- function(X,
                        R,
                        y,
                        svydesign,
@@ -114,7 +114,7 @@ bootDR_sel <- function(X,
 #' @importFrom parallel makeCluster
 #' @importFrom parallel stopCluster
 #' @importFrom doParallel registerDoParallel
-bootDR_sel_multicore <- function(X,
+boot_dr_sel_multicore <- function(X,
                                  svydesign,
                                  R,
                                  y,
@@ -147,7 +147,7 @@ bootDR_sel_multicore <- function(X,
   doParallel::registerDoParallel(cl)
   on.exit(parallel::stopCluster(cl))
   parallel::clusterExport(cl = cl, varlist = c(
-    "internal_selection", "logit_model_nonprobsvy", "start_fit", "get_method", "controlSel", "mle",
+    "internal_selection", "logit_model_nonprobsvy", "start_fit", "get_method", "control_sel", "mle",
     "probit_model_nonprobsvy", "cloglog_model_nonprobsvy", "mm", "u_theta_beta_dr",
     "mu_hatDR"
   ))

diff --git a/R/boot_ipw.R b/R/boot_ipw.R
@@ -1,4 +1,4 @@
-bootIPW <- function(X_rand,
+boot_ipw <- function(X_rand,
                     X_nons,
                     svydesign,
                     weights,
@@ -183,7 +183,7 @@ bootIPW <- function(X_rand,
 #' @importFrom parallel makeCluster
 #' @importFrom parallel stopCluster
 #' @importFrom doParallel registerDoParallel
-bootIPW_multicore <- function(X_rand,
+boot_ipw_multicore <- function(X_rand,
                               X_nons,
                               svydesign,
                               weights,
@@ -225,7 +225,7 @@ bootIPW_multicore <- function(X_rand,
   doParallel::registerDoParallel(cl)
   on.exit(parallel::stopCluster(cl))
   parallel::clusterExport(cl = cl, varlist = c(
-    "internal_selection", "logit_model_nonprobsvy", "start_fit", "get_method", "controlSel",
+    "internal_selection", "logit_model_nonprobsvy", "start_fit", "get_method", "control_sel",
     "mle", "mu_hatIPW", "probit_model_nonprobsvy", "cloglog_model_nonprobsvy", "theta_h_estimation"
   ))
 

diff --git a/R/boot_mi.R b/R/boot_mi.R
@@ -4,7 +4,7 @@
 #' @importFrom utils setTxtProgressBar
 #' @importFrom utils txtProgressBar
 
-bootMI <- function(X_rand,
+boot_mi <- function(X_rand,
                    X_nons,
                    weights,
                    y,
@@ -125,7 +125,7 @@ bootMI <- function(X_rand,
             X_rand_strap <- X_rand[which(rep_weights[, k] != 0), ]
             weights_rand_strap <- weights_rand_strap_svy[strap_rand_svy]
 
-            model_rand <- nonprobMI_nn(
+            model_rand <- nonprob_mi_nn(
               data = X_nons_strap,
               query = X_rand_strap,
               k = control_outcome$k,
@@ -187,7 +187,7 @@ bootMI <- function(X_rand,
 
             model_rand <- switch(control_outcome$predictive_match,
               { # 1
-                nonprobMI_nn(
+                nonprob_mi_nn(
                   data = y_strap,
                   query = y_rand_strap,
                   k = control_outcome$k,
@@ -196,7 +196,7 @@ bootMI <- function(X_rand,
                 )
               },
               { # 2
-                nonprobMI_nn(
+                nonprob_mi_nn(
                   data = y_nons_strap,
                   query = y_rand_strap,
                   k = control_outcome$k,
@@ -291,7 +291,7 @@ bootMI <- function(X_rand,
             X_nons_strap <- X_nons[strap, , drop = FALSE]
             y_strap <- y[strap]
 
-            model_rand <- nonprobMI_nn(
+            model_rand <- nonprob_mi_nn(
               data = X_nons_strap,
               query = t(pop_totals / N),
               k = control_outcome$k,
@@ -339,7 +339,7 @@ bootMI <- function(X_rand,
 
             model_rand <- switch(control_outcome$predictive_match,
               { # 1
-                nonprobMI_nn(
+                nonprob_mi_nn(
                   data = y_strap,
                   query = y_strap_rand,
                   k = control_outcome$k,
@@ -348,7 +348,7 @@ bootMI <- function(X_rand,
                 )
               },
               { # 2
-                nonprobMI_nn(
+                nonprob_mi_nn(
                   data = y_strap_nons,
                   query = y_strap_rand,
                   k = control_outcome$k,
@@ -410,7 +410,7 @@ bootMI <- function(X_rand,
 #' @importFrom parallel makeCluster
 #' @importFrom parallel stopCluster
 #' @importFrom doParallel registerDoParallel
-bootMI_multicore <- function(X_rand,
+boot_mi_multicore <- function(X_rand,
                              X_nons,
                              weights,
                              y,
@@ -450,8 +450,8 @@ bootMI_multicore <- function(X_rand,
   doParallel::registerDoParallel(cl)
   on.exit(parallel::stopCluster(cl))
   parallel::clusterExport(cl = cl, varlist = c(
-    "internal_selection", "logit_model_nonprobsvy", "start_fit", "get_method", "controlSel",
-    "mle", "mu_hatIPW", "probit_model_nonprobsvy", "cloglog_model_nonprobsvy", "nonprobMI_nn"
+    "internal_selection", "logit_model_nonprobsvy", "start_fit", "get_method", "control_sel",
+    "mle", "mu_hatIPW", "probit_model_nonprobsvy", "cloglog_model_nonprobsvy", "nonprob_mi_nn"
   ))
 
   if (is.null(pop_totals)) {
@@ -508,7 +508,7 @@ bootMI_multicore <- function(X_rand,
           X_rand_strap <- X_rand[strap_rand_svy, , drop = FALSE]
           weights_strap_rand <- weights_rand_strap_svy[strap_rand_svy]
 
-          model_rand <- nonprobMI_nn(
+          model_rand <- nonprob_mi_nn(
             data = X_nons_strap,
             query = X_rand_strap,
             k = control_outcome$k,
@@ -556,7 +556,7 @@ bootMI_multicore <- function(X_rand,
 
           model_rand <- switch(control_outcome$predictive_match,
             { # 1
-              nonprobMI_nn(
+              nonprob_mi_nn(
                 data = y_strap,
                 query = y_rand_strap,
                 k = control_outcome$k,
@@ -565,7 +565,7 @@ bootMI_multicore <- function(X_rand,
               )
             },
             { # 2
-              nonprobMI_nn(
+              nonprob_mi_nn(
                 data = y_nons_strap,
                 query = y_rand_strap,
                 k = control_outcome$k,
@@ -616,7 +616,7 @@ bootMI_multicore <- function(X_rand,
           X_nons_strap <- X_nons[strap, , drop = FALSE]
           y_strap <- y[strap]
 
-          model_rand <- nonprobMI_nn(
+          model_rand <- nonprob_mi_nn(
             data = X_nons_strap,
             query = t(pop_totals / N),
             k = control_outcome$k,
@@ -650,7 +650,7 @@ bootMI_multicore <- function(X_rand,
           y_strap_nons <- family_nonprobsvy$linkinv(eta_nons)
 
 
-          model_rand <- nonprobMI_nn(
+          model_rand <- nonprob_mi_nn(
             data = y_strap_nons,
             query = y_strap_rand,
             k = control_outcome$k,

diff --git a/R/control_inference.R b/R/control_inference.R
@@ -1,6 +1,6 @@
 #' @title Control parameters for inference
 #'
-#' @description \code{control_inf} constructs a list with all necessary control parameters
+#' @description \code{control_inf} constructs a `list` with all necessary control parameters
 #' for statistical inference.
 #'
 #' @param vars_selection If `TRUE`, then the variables selection model is used.

diff --git a/R/data_manip.R b/R/data_manip.R
@@ -1,5 +1,11 @@
 # create an object with model frames and matrices to preprocess
-model_frame <- function(formula, data, weights = NULL, svydesign = NULL, pop_totals = NULL, pop_size = NULL, flag = TRUE) {
+model_frame <- function(formula,
+                        data,
+                        weights = NULL,
+                        svydesign = NULL,
+                        pop_totals = NULL,
+                        pop_size = NULL,
+                        flag = TRUE) {
   if (!is.null(svydesign)) {
     ##### Model frame for nonprobability sample #####
     model_Frame <- model.frame(formula, data)

diff --git a/R/internals.R b/R/internals.R
@@ -59,7 +59,7 @@ internal_outcome <- function(outcome,
                              family_outcome,
                              start_outcome) {
   # estimation
-  model_nons <- nonprobMI_fit(
+  model_nons <- nonprob_mi_fit(
     outcome = outcome,
     data = data,
     weights = weights,
@@ -201,7 +201,7 @@ mu_hatIPW <- function(y,
   mu_hat
 }
 
-nonprobMI_fit <- function(outcome,
+nonprob_mi_fit <- function(outcome,
                           data,
                           weights,
                           svydesign = NULL,

diff --git a/R/bias_correction_ipw.R → R/ipw_bias_correction.R b/R/bias_correction_ipw.R → R/ipw_bias_correction.R
@@ -15,6 +15,7 @@ mm <- function(X,
                nleqslv_global,
                nleqslv_xscalm,
                boot = FALSE) {
+
   method_selection_function <- paste(method_selection, "_model_nonprobsvy", sep = "")
   method <- get_method(method_selection_function)
   inv_link <- method$make_link_inv

diff --git a/R/nn.R b/R/nn.R
@@ -13,15 +13,15 @@ nn_nonprobsvy <- function(outcome,
                           model_frame = NULL,
                           start_outcome = NULL) { # TODO consider add data standardization before modelling
 
-  model_nons <- nonprobMI_nn(
+  model_nons <- nonprob_mi_nn(
     data = X_nons,
     query = X_nons,
     k = control$k,
     treetype = control$treetype,
     searchtype = control$searchtype
   )
   if (is.null(pop_totals)) {
-    model_rand <- nonprobMI_nn(
+    model_rand <- nonprob_mi_nn(
       data = X_nons,
       query = X_rand,
       k = control$k,
@@ -42,7 +42,7 @@ nn_nonprobsvy <- function(outcome,
       # FUN=\(x) mean(sample_nonprob$short_[x])
     )
   } else {
-    model_rand <- nonprobMI_nn(
+    model_rand <- nonprob_mi_nn(
       data = X_nons,
       query = t(as.matrix(pop_totals / pop_totals[1])),
       k = control$k,
@@ -73,7 +73,7 @@ nn_nonprobsvy <- function(outcome,
 }
 
 
-nonprobMI_nn <- function(data,
+nonprob_mi_nn <- function(data,
                          query,
                          k,
                          treetype,
@@ -126,7 +126,7 @@ nn_exact <- function(pi_ij,
     y_nons_b <- y[boot_samp]
     x_nons_b <- X_nons[boot_samp, , drop = FALSE]
 
-    YY <- nonprobMI_nn(
+    YY <- nonprob_mi_nn(
       data = x_nons_b,
       query = X_rand,
       k = k,