Description: These functions estimate the latent factors of a given matrix, no matter it is high-dimensional or not. It tries to first estimate the number of factors using bi-cross-validation and then estimate the latent factor matrix and the noise variances. For more information about the method, see Art B. Owen and Jingshu Wang 2015 archived article on factor model (<arXiv:1503.03515>).

Title: Extended Empirical Saddlepoint Density Approximation
Description: Tools for fitting the Extended Empirical Saddlepoint (EES) density.
Imports: Rcpp (>= 0.10.4), compiler, stats, graphics, parallel, plyr,

Imports: compiler, stats, graphics, parallel, plyr, doParallel, mvnfast

Title: Extended Empirical Saddlepoint Density Approximations
Author: Matteo Fasiolo and Simon N. Wood
LinkingTo: Rcpp, RcppArmadillo, RcppMLPACK, BH

Description: Tools for fitting the Extended Empirical Saddlepoint (EES) density of Fasiolo et al. (2018) <doi:10.1214/18-EJS1433>.

Title: Indicators of Electoral Systems and Party Systems
Description: It allows to construct two types of indicators used in the study of
Electoral Systems and Party Systems starting from electoral results data.
The Effective Number of Parties (Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>)
and Electoral Volatility in its three versions (Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>,
Packaged: 2018-03-15 17:00:55 UTC; Nicolás Schmidt

Description: It allows structuring electoral data of different size and structure
to calculate various indicators frequently used in the studies of electoral systems and party systems.
Indicators of electoral volatility, electoral disproportionality, party nationalization and the
effective number of parties are included.
Imports: dplyr, tidyr, plyr (>= 1.8.4), readODS (>= 1.6.4), readxl (>=
Packaged: 2021-09-13 17:12:48 UTC; Nicolás Schmidt
Author: Nicolas Schmidt [aut, cre] (<https://orcid.org/0000-0001-5083-5792>)

Authors@R: c(person('Sebastian', 'Bayer', email='sebastian.bayer@uni-konstanz.de', role=c('aut', 'cre')),
Description: Implementations of the expected shortfall backtests of Bayer and Dimitriadis (2020) <doi:10.1093/jjfinec/nbaa013>
as well as other well known backtests from the literature. Can be used to assess the correctness of forecasts of the
expected shortfall risk measure which is e.g. used in the banking and finance industry for quantifying the market risk
of investments. A special feature of the backtests of  Bayer and Dimitriadis (2020) <doi:10.1093/jjfinec/nbaa013>
is that they only require forecasts of  the expected shortfall, which is in striking contrast to all other existing
backtests, making them particularly attractive for practitioners.
Packaged: 2020-06-04 18:53:36 UTC; sebastian