New website and CRAN checks.

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: ReSurv
 Type: Package
-Title: Machine Learning Models for Predicting IBNR Claim Counts
+Title: Machine Learning Models For Predicting Claim Counts
 Version: 1.0.0
 Authors@R:
     c(person(given = "Emil",
@@ -17,9 +17,9 @@ Authors@R:
              email="[email protected]",
              role = c("aut", "cph"),
              comment = c(ORCID = "0000-0001-5846-667X")))
-Description: Prediction of future IBNR frequencies using the feature based development factors introduced in Hiabu, Hofman, Pittarello (2023) <doi:10.48550/arXiv.2312.14549>. 
-             Implementation of Neural Networks (NN), eXtreme Gradient Boosting (XGB), 
-             and Cox model with splines (COX) to optimise the partial log-likelihood of proportional hazard models.
+Description: Prediction of claim counts using the feature based development factors introduced in the manuscript <doi:10.48550/arXiv.2312.14549>. 
+             Implementation of Neural Networks, Extreme Gradient Boosting, 
+             and Cox model with splines to optimise the partial log-likelihood of proportional hazard models.
 URL: https://github.com/edhofman/ReSurv
 BugReports: https://github.com/edhofman/ReSurv/issues
 License: GPL (>= 2)

R/ResurvcvIndividualData.R

@@ -33,40 +33,6 @@
 #' @import tidyverse
 #' @import xgboost
 #'
-#' @examples
-#' ## Not run
-#' input_data <- data_generator(random_seed = 1964)
-#'
-#' individual_data <- IndividualDataPP(input_data,
-#'                                   id="claim_number",
-#'                                   continuous_features=NULL,
-#'                                   categorical_features="claim_type",
-#'                                   accident_period="AP",
-#'                                   calendar_period="RP",
-#'                                   input_time_granularity = "months",
-#'                                   output_time_granularity = "quarters",
-#'                                   years=4,
-#'                                   continuous_features_spline=NULL,
-#'                                   calendar_period_extrapolation=FALSE)
-#'
-#' resurv.cv.xgboost <- ReSurvCV(IndividualDataPP=individual_data,
-#'                               model="XGB",
-#'                               hparameters_grid=list(booster="gbtree",
-#'                               eta=c(.001,.01,.2,.3),
-#'                               max_depth=c(3,6,8),
-#'                               subsample=c(1),
-#'                               alpha=c(0,.2,1),
-#'                               lambda=c(0,.2,1),
-#'                                min_child_weight=c(.5,1)),
-#'                                print_every_n = 1L,
-#'                                nrounds=1L, ##set to one to run quickly
-#'                                verbose=FALSE,
-#'                                verbose.cv=TRUE,
-#'                                early_stopping_rounds = 100L,
-#'                                folds=5L,
-#'                                parallel=TRUE,
-#'                                ncores=2L,
-#'                                random_seed=1L)
 #'
 #'
 #' @references

R/data_generator.R

@@ -21,13 +21,6 @@
 #'
 #' @import SynthETIC
 #'
-#' @examples
-#' ## Generate four years of daily data for scenario Alpha.
-#' input_data <- data_generator(random_seed = 7,
-#'                               scenario='alpha',
-#'                               time_unit = 1/360,
-#'                               years = 4,
-#'                               period_exposure = 200)
 #'
 #'
 #'