Release v0.8.0

* Single Rcpp file per model (#157)
* Refactor parts of PNBD dyncov LL in Rcpp (#158 #160)
* Bugfix: Parameter names are removed by some optimx methods (#159)

Co-authored-by: Patrik Schilter <[email protected]>
Co-authored-by: Elliot Shin Oblander <[email protected]>

.github/workflows/Tests.yaml

@@ -50,6 +50,6 @@ jobs:
             print("Not on release branch: Dont run all tests (skip_on_ci())")
           }
 
-          reporter <- RstudioReporter$new() # has to be defined outside capture.output to still print
+          reporter <- RStudioReporter$new() # has to be defined outside capture.output to still print
           capture.output(devtools::test(reporter = reporter, stop_on_failure=TRUE, stop_on_warning=TRUE))
         shell: Rscript {0}

DESCRIPTION

@@ -1,28 +1,38 @@
 Package: CLVTools
 Title: Tools for Customer Lifetime Value Estimation
-Version: 0.7.0
-Date: 2020-08-26
+Version: 0.8.0
+Date: 2021-03-22
 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"),
-    person(given="Patrik",  family="Schilter",    email = "[email protected]",role = "aut"),
+    person(given="Niels",   family="Kuebler",     email = "[email protected]", role = "aut"),
     person(given="Markus",  family="Meierer",     email = "[email protected]", role = "aut"),
-    person(given="Niels",   family="Kuebler",     email = "[email protected]", role = "aut"))
+    person(given="Jeffrey", family="Naef",        email = "[email protected]",role = "aut"),
+    person(given="Elliot",  family="Oblander",    email = "[email protected]",role = "aut"),
+    person(given="Patrik",  family="Schilter",    email = "[email protected]",role = "aut")
+    )
 Depends:
     R (>= 3.5.0),
     methods
-Description: Probabilistic latent customer attrition models (also known as "buy-'til-you-die models") are used to 
-    predict future purchase behavior of customers. This package includes fast and accurate implementations of various 
-    probabilistic latent customer attrition models for non-contractual settings (e.g., retail business) with and 
-    without time-invariant and time-varying covariates. Currently, the package includes the Pareto/NBD model 
-    (Pareto/Negative-Binomial-Distribution), the BG/NBD mode (Beta-Gamma/Negative-Binomial-Distribution) and the GGom/NBD 
-    (Gamma-Gompertz/Negative-Binomial-Distribution) for the purchase and the attrition processes as well as the Gamma/Gamma model 
-    for the spending process. For reference to the Pareto/NBD model, see Schmittlein DC, Morrison DG, Colombo R (1987) <doi:10.1287/mnsc.33.1.1>, 
-    for the BG/NBD model, see Fader PS, Hardie BG, Lee K (2005) <doi:10.1287/mksc.1040.0098> and for the GGom/NBD model see Bemmaor AC, Glady N (2012) 
-    <doi:10.1287/mnsc.1110.1461>. For reference to the Gamma/Gamma model, see Fader PS, Hardie BG, Lee K (2005) <doi:10.1509/jmkr.2005.42.4.415>.
+Description: 
+    A set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV).
+    Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals' attrition, transaction, and spending process. 
+    Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process. 
+    They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior. 
+    Moreover, these models have also been used to predict individuals’ long-term engagement in activities such as playing an online game or 
+    posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in 
+    lifetime values estimates for individual customers.
+    This package includes fast and accurate implementations of various probabilistic models for non-contractual settings 
+    (e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g., 
+    socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying 
+    covariates to control for e.g., seasonal patterns. 
+    Currently, the package includes the following latent attrition models to model individuals' attrition and transaction process: 
+    [1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution), 
+    [2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates), 
+    [3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the 
+    [4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution). 
+    Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals.
 Imports:
     data.table (>= 1.12.0),
-    foreach (>= 1.5.0),
     ggplot2 (>= 3.2.0),
     lubridate (>= 1.7.8), 
     Matrix (>= 1.2-17), 
@@ -32,8 +42,6 @@ Imports:
     utils
 Suggests:
     covr,
-    doFuture, 
-    doParallel,
     future,
     knitr,
     rmarkdown,
@@ -129,8 +137,6 @@ Collate:
     'pnbd_dyncov_CET.R'
     'pnbd_dyncov_DECT.R'
     'pnbd_dyncov_LL.R'
-    'pnbd_dyncov_LL_Bi.R'
-    'pnbd_dyncov_LL_Di.R'
     'pnbd_dyncov_createwalks.R'
     'pnbd_dyncov_expectation.R'
     'pnbd_dyncov_makewalks.R'

NAMESPACE

@@ -47,8 +47,6 @@ import(data.table)
 import(optimx)
 importFrom(MASS,ginv)
 importFrom(Matrix,nearPD)
-importFrom(foreach,"%dopar%")
-importFrom(foreach,foreach)
 importFrom(ggplot2,aes)
 importFrom(ggplot2,element_blank)
 importFrom(ggplot2,element_line)

NEWS.md

@@ -1,3 +1,14 @@
+# CLVTools 0.8.0
+
+### NEW FEATURES
+* Partially refactor the LL of the extended Pareto/NBD in Rcpp with code kindly donated by Elliot Shin Oblander
+* Improved documentation 
+
+### BUG FIXES
+* Optimization methods nlm and nlminb can now be used. Thanks to Elliot Shin Oblander for reporting
+
+
+
 # CLVTools 0.7.0
 
 ### NEW FEATURES