Package: CLVTools
Title: Tools for Customer Lifetime Value Estimation
Version: 0.6.0
Date: 2020-06-22
Authors@R: c(
    person(given="Patrick", family="Bachmann",    email = "patrick.bachmann@business.uzh.ch", role = c("cre","aut")),
    person(given="Jeffrey", family="Naef",        email = "naef@stat.math.ethz.ch",role = "aut"),
    person(given="Patrik",  family="Schilter",    email = "patrik.schilter@gmail.com",role = "aut"),
    person(given="Markus",  family="Meierer",     email = "markus.meierer@business.uzh.ch", role = "aut"),
    person(given="Niels",   family="Kuebler",     email = "niels.kuebler@uzh.ch", 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>.
Imports: data.table (>= 1.12.0), foreach (>= 1.5.0), ggplot2 (>=
        3.2.0), lubridate (>= 1.7.8), Matrix (>= 1.2-17), MASS, optimx
        (>= 2019-12.02), stats, utils
Suggests: BTYD, covr, doFuture, doParallel, future, knitr, rmarkdown,
        testthat
License: GPL-3
URL: https://github.com/bachmannpatrick/CLVTools
BugReports: https://github.com/bachmannpatrick/CLVTools/issues
NeedsCompilation: yes
SystemRequirements: C++11
LinkingTo: Rcpp(>= 0.12.12), RcppArmadillo (>= 0.9.500.2.0), RcppGSL
        (>= 0.3.7)
LazyLoad: yes
Encoding: UTF-8
Collate: 'CLVTools.R' 'RcppExports.R' 'all_generics.R'
        'class_clv_time.R' 'class_clv_data.R' 'class_clv_model.R'
        'class_clv_fitted.R' 'class_clv_model_bgnbd.R'
        'class_clv_bgnbd.R' 'class_clv_fitted_staticcov.R'
        'class_clv_data_staticcovariates.R'
        'class_clv_model_bgnbd_staticcov.R'
        'class_clv_bgnbd_staticcov.R'
        'class_clv_data_dynamiccovariates.R'
        'class_clv_fitted_dynamiccov.R'
        'class_clv_model_ggomnbd_nocov.R' 'class_clv_ggomnbd.R'
        'class_clv_model_ggomnbd_staticcov.R'
        'class_clv_ggomnbd_staticcov.R' 'class_clv_model_pnbd.R'
        'class_clv_model_pnbd_staticcov.R'
        'class_clv_model_pnbd_dynamiccov.R' 'class_clv_pnbd.R'
        'class_clv_pnbd_dynamiccov.R' 'class_clv_pnbd_staticcov.R'
        'class_clv_time_date.R' 'class_clv_time_datetime.R'
        'class_clv_time_days.R' 'class_clv_time_hours.R'
        'class_clv_time_weeks.R' 'class_clv_time_years.R'
        'clv_template_controlflow_estimate.R'
        'clv_template_controlflow_predict.R' 'data.R'
        'f_DoExpectation.R' 'f_clvdata_inputchecks.R'
        'f_clvfitted_inputchecks.R' 'f_generics_clvdata.R'
        'f_generics_clvfitted.R' 'f_generics_clvfitted_estimate.R'
        'f_generics_clvfitteddyncov.R'
        'f_generics_clvfittedstaticcov.R'
        'f_generics_clvfittedstaticcov_estimate.R'
        'f_generics_clvpnbddyncov.R' 'f_interface_bgbb.R'
        'f_interface_bgnbd.R' 'f_interface_clvdata.R'
        'f_interface_ggomnbd.R' 'f_interface_pnbd.R'
        'f_interface_setdynamiccovariates.R'
        'f_interface_setstaticcovariates.R' 'f_s3generics_clvdata.R'
        'f_s3generics_clvdata_dynamiccov.R'
        'f_s3generics_clvdata_plot.R'
        'f_s3generics_clvdata_staticcov.R' 'f_s3generics_clvfitted.R'
        'f_s3generics_clvfitted_plot.R'
        'f_s3generics_clvfitted_staticcov.R' 'f_s3generics_clvtime.R'
        'interlayer_callLL.R' 'interlayer_callnextinterlayer.R'
        'interlayer_constraints.R' 'interlayer_correlation.R'
        'interlayer_manager.R' 'interlayer_regularization.R'
        'pnbd_dyncov_ABCD.R' 'pnbd_dyncov_BkSum.R' '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'
        'pnbd_dyncov_palive.R'
RoxygenNote: 7.1.0
VignetteBuilder: knitr
Packaged: 2020-06-24 16:20:49 UTC; patrik
Author: Patrick Bachmann [cre, aut],
  Jeffrey Naef [aut],
  Patrik Schilter [aut],
  Markus Meierer [aut],
  Niels Kuebler [aut]
Maintainer: Patrick Bachmann <patrick.bachmann@business.uzh.ch>
Repository: CRAN
Date/Publication: 2020-06-24 22:10:02 UTC
Built: R 4.1.0; x86_64-w64-mingw32; 2020-08-03 12:29:57 UTC; windows
Archs: i386, x64
