Hidden Markov Model for Return Time-Series Based on Lambda Distribution (ldhmm)
Hidden Markov Model (HMM) based on symmetric lambda distribution framework is implemented for the study of return time-series in the financial market. Major features in the S&P500 index, such as regime identification, volatility clustering, and anti-correlation between return and volatility, can be extracted from HMM cleanly. Univariate symmetric lambda distribution is essentially a location-scale family of power-exponential distribution. Such distribution is suitable for describing highly leptokurtic time series obtained from the financial market. It provides a theoretically solid foundation to explore such data where the normal distribution is not adequate. The HMM implementation follows closely the book: ‘Hidden Markov Models for Time Series’, by Zucchini, MacDonald, Langrock (2016).

Venn Diagrams in 2D and 3D (vennplot)
Calculate and plot Venn diagrams in 2D and 3D.

Sparse Tensors in R (tensorr)
Provides methods to manipulate and store sparse tensors. Tensors are multidimensional generalizations of matrices (two dimensional) and vectors (one dimensional).

Post Vortex Simulation Analysis (vortexR)
Facilitate Post Vortex Simulation Analysis by offering tools to collate multiple Vortex (v10) output files into one R object, and analyse the collated output statistically. Vortex is a software for the development of individual-based model for population dynamic simulation (see <http://…/Vortex10.aspx> ).

XLConnect’ Wrapper (xlsimple)
Provides a simple wrapper for some ‘XLConnect’ functions. ‘XLConnect’ is a package that allows for reading, writing, and manipulating Microsoft Excel files. This package, ‘xlsimple’, adds some documentation and pre-defined formatting to the outputted Excel file. Individual sheets can include a description on the first row to remind user what is in the data set. Auto filters and freeze rows are turned on. A brief readme file is created that provides a summary listing of the created sheets and, where provided, the description.