Provides advanced Bayesian methods to estimate
	     abundance and run-timing from temporally-stratified
	     Petersen mark-recapture experiments. Methods include
	     hierarchical modelling of the capture probabilities
  	     and spline smoothing of the daily run size. Theory
  	     described in Bonner and Schwarz (2011)
         <doi:10.1111/j.1541-0420.2011.01599.x>.

Simulate, estimate and forecast a wide range of regression based dynamic models for bounded time series, covering the most commonly applied models in the literature. The main calculations are done in 'FORTRAN', which translates into very fast algorithms. The main references are 
   Bayer et al. (2017)  <doi:10.1016/j.jhydrol.2017.10.006>, 
   Pumi et al. (2019) <doi:10.1016/j.jspi.2018.10.001>, 
   Pumi et al. (2021) <doi:10.1111/sjos.12439> and 
	 Pumi et al. (2022) <arXiv:2211.02097>.

Large panel data sets are often subject to common trends. However, it can be difficult to determine the exact number of these common factors and analyse their properties.
  The package implements the Barigozzi and Trapani (2022) <doi:10.1080/07350015.2021.1901719> test, which not only provides an efficient way of estimating the number of common factors in large nonstationary panel data sets, but also gives further insights on factor classes. The routine identifies the existence of (i) a factor subject to a linear trend, (ii) the number of zero-mean I(1) and (iii) zero-mean I(0) factors.
  Furthermore, the package includes the Integrated Panel Criteria by Bai (2004) <doi:10.1016/j.jeconom.2003.10.022> that provide a complementary measure for the number of factors.

Functions for data preparation, parameter estimation, scoring, and plotting for the 
    BG/BB (Fader, Hardie, and Shang 2010 <doi:10.1287/mksc.1100.0580>), 
    BG/NBD (Fader, Hardie, and Lee 2005 <doi:10.1287/mksc.1040.0098>) and 
    Pareto/NBD and Gamma/Gamma (Fader, Hardie, and Lee 2005 <doi:10.1509/jmkr.2005.42.4.415>) models.

Provides advanced statistical methods to describe and predict customers'
  purchase behavior in a non-contractual setting. It uses historic transaction records to fit a
  probabilistic model, which then allows to compute quantities of managerial interest on a cohort-
  as well as on a customer level (Customer Lifetime Value, Customer Equity, P(alive), etc.). This
  package complements the BTYD package by providing several additional buy-till-you-die models, that
  have been published in the marketing literature, but whose implementation are complex and non-trivial.
  These models are: NBD [Ehrenberg (1959) <doi:10.2307/2985810>], MBG/NBD [Batislam et al (2007) 
  <doi:10.1016/j.ijresmar.2006.12.005>], (M)BG/CNBD-k [Reutterer et al (2020) 
  <doi:10.1016/j.ijresmar.2020.09.002>], Pareto/NBD (HB) [Abe (2009) <doi:10.1287/mksc.1090.0502>] 
  and Pareto/GGG [Platzer and Reutterer (2016) <doi:10.1287/mksc.2015.0963>].