Prepare release v0.7

Cran comments, news.md
docu:typos, roxygen, non-ascii chars, info on cohorts
rcpp: inconsistent inputchecks
Added more information on plotting the expected and actual repeat transactions (setting the stage for adressing #142)
Tests: transaction object copied when adding covariates, enable for multiple optimization methods, verify zero-repeaters

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: CLVTools
 Title: Tools for Customer Lifetime Value Estimation
 Version: 0.7.0
-Date: 2020-06-25
+Date: 2020-08-26
 Authors@R: c(
     person(given="Patrick", family="Bachmann",    email = "[email protected]", role = c("cre","aut")),
     person(given="Jeffrey", family="Naef",        email = "[email protected]",role = "aut"),

NEWS.md

@@ -1,3 +1,16 @@
+# CLVTools 0.7.0
+
+### NEW FEATURES
+* Refactor the Gamma-Gamma (GG) model to predict mean spending per transaction into an independent model
+* The prediction for transaction models can now be combined with separately fit spending models
+* Write the unconditional expectation functions in Rcpp for faster plotting (Pareto/NBD and Beta-Geometric/NBD)
+* Improved documentation and walkthrough
+
+### BUG FIXES
+* Pareto/NBD LogLikelihood: For the case Tcal = t.x and for the case alpha == beta
+* Static or dynamic covariates with syntactically invalid names (spaces, start with numbers, etc) could not be fit
+
+
 # CLVTools 0.6.0
 
 ### NEW FEATURES

R/class_clv_gg.R

@@ -1,4 +1,4 @@
-#' @templateVar name_model_full GammaGamma
+#' @templateVar name_model_full Gamma-Gamma
 #' @templateVar name_class_clvmodel clv.model.gg
 #' @template template_class_clvfittedspendingmodels
 #'

R/clv_template_controlflow_estimate.R

@@ -3,13 +3,6 @@ clv.template.controlflow.estimate <- function(clv.fitted,
                                               optimx.args,
                                               verbose,
                                               ...){
-  # ***TODO: For development purposes only: All additional args have to be named!
-  l.elipsis <- list(...)
-  if(length(l.elipsis)>0){
-    names.ellipsis.args <- names(list(...))
-    stopifnot(!is.null(names.ellipsis.args))
-    stopifnot(all(nchar(names(list(...)))>0))
-  }
 
   # input checks ------------------------------------------------------------------------------------------
   #   checks for model first

R/clv_template_controlflow_predict.R

@@ -1,13 +1,5 @@
 clv.template.controlflow.predict <- function(clv.fitted, verbose, user.newdata, ...){
 
-  # ***TODO: For development purposes only: All additional args have to be named!
-  l.elipsis <- list(...)
-  if(length(l.elipsis)>0){
-    names.ellipsis.args <- names(list(...))
-    stopifnot(!is.null(names.ellipsis.args))
-    stopifnot(all(nchar(names(list(...)))>0))
-  }
-
   # Check if can predict -----------------------------------------------------------------------------------------
   # Cannot predict if there are any NAs in any of the prediction.params
   clv.controlflow.check.prediction.params(clv.fitted = clv.fitted)
@@ -40,7 +32,7 @@ clv.template.controlflow.predict <- function(clv.fitted, verbose, user.newdata,
                                       dt.predictions = dt.predictions, verbose = verbose, ...)
   setkeyv(dt.predictions, "Id")
 
-  # Actuals ---------------------------------------------------------------------------------------------¨
+  # Actuals ---------------------------------------------------------------------------------------------
   has.actuals    <- clv.controlflow.predict.get.has.actuals(clv.fitted, dt.predictions = dt.predictions)
   dt.predictions <- clv.controlflow.predict.add.actuals(clv.fitted = clv.fitted, dt.predictions = dt.predictions,
                                                         has.actuals = has.actuals, verbose = verbose, ...)

R/f_interface_bgnbd.R

@@ -48,7 +48,7 @@ setGeneric("bgnbd", def = function(clv.data, start.params.model=c(), optimx.args
 #' the technical note by Fader and Hardie (2007).
 #'
 #' The likelihood function is the likelihood function associated with the basic model where
-#' alpha, a, and b are replaced with alpha = alpha0*exp(−g1z1), a = a_0*exp(g2z2), and b = b0*exp(g3z2)
+#' alpha, a, and b are replaced with alpha = alpha0*exp(-g1z1), a = a_0*exp(g2z2), and b = b0*exp(g3z2)
 #' while r remains unchanged. Note that in the current implementation, we constrain the covariate parameters
 #' and data for the lifetime process to be equal (g2=g3 and z2=z3).
 #' }

R/f_interface_pnbd.R

@@ -23,6 +23,9 @@
 #' The smaller r, the stronger the heterogeneity of the purchase process.\cr
 #' \code{alpha}: scale parameter of the Gamma distribution of the purchase process.
 #'
+#' Based on these parameters, the average purchase rate while customers are active
+#' is r/alpha and the average dropout rate is s/beta.
+#'
 #' Ideally, the starting parameters for r and s represent your best guess
 #' concerning the heterogeneity of customers in their buy and die rate.
 #' If covariates are included into the model additionally parameters for the
@@ -67,14 +70,14 @@
 #' @note
 #' Fitting the Pareto/NBD model with dynamic covariates is for the most part implemented using \code{data.table} and to a smaller part further
 #' parallelized with the \code{foreach} package. Registering a
-#' parallel backend with \code{\link[doFuture]{doFuture}} or \code{\link[doParallel:doParallel-package]{doParallel}} before fitting the
+#' parallel backend with \code{\link[doFuture]{doFuture}} or \code{\link[doParallel]{doParallel}} before fitting the
 #' models allows to take advantage of this. If no parallel backend is set up, the \code{foreach} package gives a friendly reminder that
 #' it is executed sequentially. In case this is desired but no warning should be given, a parallel backend in sequential mode
 #' can be set up, for example package \code{doFuture} with \code{\link[future:plan]{plan("sequential")}}.
 #'
 #' The part executed with \code{foreach} also heavily relies on \code{data.table} which is natively parallelized already. When setting up
 #' the parallel backend, great care should be taken to reduce the overhead from this nested parallelism as otherwise it can \emph{increase} runtime.
-#' See \code{\link[data.table:openmp-utils]{setDTthreads}}, \code{\link[data.table:openmp-utils]{getDTthreads}},
+#' See \code{\link[data.table]{setDTthreads}}, \code{\link[data.table]{getDTthreads}},
 #' and \code{\link[future]{plan}} for information on how to do this.
 #'
 #' The Pareto/NBD model with dynamic covariates can currently not be fit with data that has a temporal resolution
@@ -88,7 +91,7 @@
 #' @template template_clvfittedtransactions_seealso
 #' @seealso \code{\link[CLVTools:SetDynamicCovariates]{SetDynamicCovariates}} to add dynamic covariates on which the \code{pnbd} model can be fit.
 #'
-#' @seealso \code{\link[data.table:openmp-utils]{setDTthreads}}, \code{\link[data.table:openmp-utils]{getDTthreads}},\code{\link[doParallel:registerDoParallel]{registerDoParallel}},\code{\link[doFuture]{registerDoFuture}} for setting up parallel execution.
+#' @seealso \code{\link[data.table]{setDTthreads}}, \code{\link[data.table]{getDTthreads}},\code{\link[doParallel]{registerDoParallel}},\code{\link[doFuture]{registerDoFuture}} for setting up parallel execution.
 #'
 #' @template template_references_pnbd
 #'

R/f_interface_predict_clvfittedtransactions.R

@@ -93,7 +93,7 @@
 #' # Predict future transactions but not spending and CLV
 #' predict(apparel.pnbd, predict.spending = FALSE)
 #'
-#' # Predict spending by fitting a GammaGamma model
+#' # Predict spending by fitting a Gamma-Gamma model
 #' predict(apparel.pnbd, predict.spending = gg)
 #'
 #' # Fit a spending model separately and use it to predict spending

R/f_s3generics_clvfitted.R

@@ -73,7 +73,7 @@ coef.clv.fitted <- function(object, ...){
 #' @description
 #' Returns the variance-covariance matrix of the parameters of the fitted model object.
 #' The variance-covariance matrix is derived from the Hessian that results from the optimization procedure.
-#' First, the Moore–Penrose generalized inverse of the Hessian is used to obtain an estimate of the
+#' First, the Moore-Penrose generalized inverse of the Hessian is used to obtain an estimate of the
 #' variance-covariance matrix.
 #' Next, because some parameters may be transformed for the purpose of restricting their value during
 #' the log-likelihood estimation, the variance estimates are adapted to

R/f_s3generics_clvfittedtransactions_plot.R

@@ -12,7 +12,9 @@
 #'
 #' @description
 #' Plot the actual repeat transactions and overlay it with the repeat transaction as predicted
-#' by the fitted model (unconditional expectation).
+#' by the fitted model. Currently, following previous literature, the in-sample unconditional
+#' expectation is plotted in the holdout period. In the future, we might add the option to also
+#' plot the summed CET for the holdout period as an alternative evaluation metric.
 #'
 #' @template template_details_predictionend
 #'