Basics of Certificates and Structured Products Valuation
Collection of pricing by duplication methods for popular structured products ("Zertifikate").

Change Point Analysis in Functional Data
Ozan Sonmez, Alexander Aue, Gregory Rice
Change point estimation and detection methods for functional data are implemented using dimension reduction via functional principal component analysis and a fully-functional (norm-based) method. Detecting and dating structural breaks for both dependent and independent functional samples is illustrated along with some basic functional data generating processes.
fda, sde, stats, sandwich, reshape2, lattice

Rmetrics - Dependence Structures with Copulas
R (>= 2.4.0), sn, adapt, methods, timeDate, timeSeries,
fBasics (>= 2100.77)

R (>= 2.4.0), sn, adapt, methods, timeDate, timeSeries,
fBasics (>= 2100.78)

R (>= 2.4.0), sn, methods, timeDate, timeSeries, fBasics (>=
2100.78)

R (>= 2.2.1), methods, fBasics, fCalendar, fSeries, fMultivar, fExtremes

R (>= 2.4.0), sn, adapt, methods, timeDate, timeSeries, fBasics

R (>= 2.4.0), sn, adapt, methods, timeDate, timeSeries,
fBasics

Rmetrics - Bivariate Dependence Structures with Copulae
R (>= 2.15.1), timeDate, timeSeries, fBasics, fMultivar

Provides a  collection of functions to
manage, to investigate and to analyze bivariate financial returns by
Copulae. Included are the families of Archemedean, Elliptical,
Extreme Value, and Empirical Copulae.

Diethelm Wuertz [aut],
Tobias Setz [aut],
Yohan Chalabi [ctb],
Paul Smith [cre]

Dynamic Model Averaging and Dynamic Model Selection for
Continuous Outcomes
doParallel, forecast, foreach, gplots, graphics, grDevices,
iterators, MSwM, parallel, png, stats, utils, xts, zoo
Krzysztof Drachal [aut, cre] (Faculty of Economic Sciences, University of Warsaw, Poland)
It allows to estimate Dynamic Model Averaging, Dynamic Model Selection and Median Probability Model. The original methods (see References) are implemented, as well as, selected further modifications of these methods. In particular the User might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using Google Trends. The code is written in a way which minimises the computational burden (which is quite an obstacle for Dynamic Model Averaging if many variables are used). For example, this package allows for parallel computations under Windows machines. Additionally, the User might reduce a set of models according to a certain algorithm. The package is designed in a way that is hoped to be especially useful in economics and finance. (Research funded by the Polish National Science Centre grant under the contract number DEC-2015/19/N/HS4/00205.)