Title: R interface to Geochemical Modeling Software
Description: PHREEQC (Version 3) is a geochemical modeling program developed
by the US Geological Survey that is designed to perform a wide variety
of aqueous geochemical calculations, including speciation,
batch-reaction, one-dimensional reactive-transport, and inverse

Title: R Interface to Geochemical Modeling Software
Description: A geochemical modeling program developed by the US Geological
Survey that is designed to perform a wide variety of aqueous geochemical
calculations, including speciation, batch-reaction, one-dimensional

Author: S.R. Charlton [aut, cre], D.L. Parkhurst [aut], and C.A.J. Appelo [aut], with contributions
from D. Gillespie [ctb] for Chipmunk BASIC and S.D. Cohen [ctb], A.C. Hindmarsh [ctb],
R. Serban [ctb], D. Shumaker [ctb], and A.G. Taylor [ctb] for CVODE/SUNDIALS

Title: Panel Data Analysis with Heterogeneous Time Trends
Description: The R-package 'phtt' provides estimation procedures for
panel data with large dimensions n, T, and general forms of
unobservable heterogeneous effects. Particularly, the
estimation procedures are those of Bai (2009) and Kneip,
Sickles, and Song (2012), which complement one another very
well: both models assume the unobservable heterogeneous effects
to have a factor structure. The method of Bai (2009) assumes
that the factors are stationary, whereas the method of Kneip et
al. (2012) allows the factors to be non-stationary.
Additionally, the 'phtt' package provides a wide range of
dimensionality criteria in order to estimate the number of the
unobserved factors simultaneously with the remaining model

Description: The R-package 'phtt' provides estimation procedures for panel data with large dimensions n, T, and general forms of unobservable heterogeneous effects. Particularly, the estimation procedures are those of Bai (2009) and Kneip, Sickles, and Song (2012), which complement one another very well: both models assume the unobservable heterogeneous effects to have a factor structure. The method of Bai (2009) assumes that the factors are stationary, whereas the method of Kneip et al. (2012) allows the factors to be non-stationary. Additionally, the 'phtt' package provides a wide range of dimensionality criteria in order to estimate the number of the unobserved factors simultaneously with the remaining model parameters.