This package provides and implementation of a one step
smoothing newton method for the solution of second order cone
programming problems, originally described by Xiaoni Chi and
Sanyang Liu.

Interactive Document for Working with Cluster Analysis
An interactive document on  the topic of cluster analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/ClusterAnalysis/>.

Tools for Customer Lifetime Value Estimation
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"))
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) 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 reference to the Gamma/Gamma model, see Fader PS, Hardie BG, Lee K (2005) <doi:10.1509/jmkr.2005.42.4.415>.
BTYD, covr, doFuture, doParallel, knitr, rmarkdown, testthat
'CLVTools.R' 'RcppExports.R' 'all_generics.R'
'class_clv_time.R' 'class_clv_data.R'
'class_clv_data_staticcovariates.R'
'class_clv_data_dynamiccovariates.R' 'class_clv_model.R'
'class_clv_fitted.R' 'class_clv_fitted_staticcov.R'
'class_clv_fitted_dynamiccov.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'
Patrick Bachmann [cre, aut],
Jeffrey Naef [aut],
Patrik Schilter [aut],
Markus Meierer [aut],
Niels Kuebler [aut]