* Computes 26 Financial Risk Measures for Any Continuous Distribution* (

**Risk**)

Computes 26 financial risk measures for any continuous distribution. The 26 financial risk measures include value at risk, expected shortfall due to Artzner et al. (1999) <DOI:10.1007/s10957-011-9968-2>, tail conditional median due to Kou et al. (2013) <DOI:10.1287/moor.1120.0577>, expectiles due to Newey and Powell (1987) <DOI:10.2307/1911031>, beyond value at risk due to Longin (2001) <DOI:10.3905/jod.2001.319161>, expected proportional shortfall due to Belzunce et al. (2012) <DOI:10.1016/j.insmatheco.2012.05.003>, elementary risk measure due to Ahmadi-Javid (2012) <DOI:10.1007/s10957-011-9968-2>, omega due to Shadwick and Keating (2002), sortino ratio due to Rollinger and Hoffman (2013), kappa due to Kaplan and Knowles (2004), Wang (1998)’s <DOI:10.1080/10920277.1998.10595708> risk measures, Stone (1973)’s <DOI:10.2307/2978638> risk measures, Luce (1980)’s <DOI:10.1007/BF00135033> risk measures, Sarin (1987)’s <DOI:10.1007/BF00126387> risk measures, Bronshtein and Kurelenkova (2009)’s risk measures.

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**Implements Group Fused Multinomial Regression****gfmR**)

Software to implement methodology to preform automatic response category combinations in multinomial logistic regression. There are functions for both cross validation and AIC for model selection. The method provides regression coefficient estimates that may be useful for better understanding the true probability distribution of multinomial logistic regression when category probabilities are similar. These methods are not recommended for a large number of predictor variables.

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**Time Series Cointegrated System****TSCS**)

A set of functions to implement Time Series Cointegrated System (TSCS) spatial interpolation and relevant data visualization.

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**Labelled Data Utility Functions****sjlabelled**)

Collection of functions to work with labelled data to read and write data between R and other statistical software packages like ‘SPSS’, ‘SAS’ or ‘Stata’, and to work with labelled data. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values.

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**Negative Binomial Model-Based Clustering****NB.MClust**)

Model-based clustering of high-dimensional nonnegative data that follow Generalized Negative Binomial distribution. All functions in this package applies to either continuous or integer data. Correlation between variables is allowed, while samples are assumed to be independent.