Description: Provides functions to estimate model parameters and forecast future volatilities using the Unified GARCH-Ito [Kim and Wang (2016) <doi:10.1016/j.jeconom.2016.05.003>] and Realized GARCH-Ito [Song et. al. (2020) <doi:10.1016/j.jeconom.2020.07.007>] models. Optimization is done using augmented Lagrange multiplier method.
Packaged: 2020-09-05 07:43:14 UTC; cindysong

Title: Estimating a GARCHSK Model and GJRSK Model
person("Kei","Nakagawa",email="kei.nak.0315@gmail.com",role=c("aut","cre"),comment = c(ORCID = "0000-0001-5046-8128"))
Description: Functions for estimating a GARCHSK model and GJRSK model based on a publication by Leon et,al (2005)<doi:10.1016/j.qref.2004.12.020> and Nakagawa and Uchiyama (2020)<doi:10.3390/math8111990>. These are a GARCH-type model allowing for time-varying volatility, skewness and kurtosis.
Author: Kei Nakagawa [aut, cre] (<https://orcid.org/0000-0001-5046-8128>)

Title: Gene and Region Counting of Mutations ("GARCOM")
role = c("aut", "cre","cph"),email = "ss5505@cumc.columbia.edu"),person("Giuseppe","Tosto", role =c("aut","cph")))
Description: Gene and Region Counting of Mutations (GARCOM) package computes mutation (or alleles) counts per gene per individuals based on gene annotation or genomic base pair boundaries. It comes with features to accept data formats in plink(.raw) and VCF. It provides users flexibility to extract and filter individuals, mutations and genes of interest.
Imports: data.table (>= 1.12.8), stats, vcfR (>= 1.12.0)

Title: Generalized Autoregressive Score Models
Author: Leopoldo Catania [aut,cre], Kris Boudt [ctb], David Ardia [ctb]
Description: Simulate, Estimate and Forecast using univariate and multivariate GAS models.
Depends: R (>= 2.10), Rsolnp, methods, MASS, xts, numDeriv, zoo

Description: Simulate, estimate and forecast using univariate and multivariate GAS models.

Imports: Rcpp (>= 0.12.2), Rsolnp, MASS, xts, numDeriv, zoo

Imports: Rcpp (>= 0.12.2), Rsolnp, MASS, xts, numDeriv, zoo,

Description: Simulate, estimate and forecast using univariate and multivariate GAS models
as described in Ardia et al. (2016) <https://ssrn.com/abstract=2825380>.