Ensemble classifiers based upon generalized additive models for binary
classification (De Bock et al. (2010) <DOI:10.1016/j.csda.2009.12.013>). The ensembles
implement Bagging (Breiman (1996) <DOI:10.1023/A:1018054314350>), the Random Subspace Method (Ho (1998) <DOI:10.1109/34.709601>), or
both, and use Hastie and Tibshirani's (1990) generalized additive models (GAMs)
as base classifiers. Once an ensemble classifier has been trained, it can
be used for predictions on new data. A function for cross validation is also
included.

Gene Association Network-based Pathway Analysis
Zhaoyuan Fang, Weidong Tian and Hongbin Ji
This package implements a network-based gene weighting
algorithm for pathways, as well as a gene-weighted gene set
analysis approach for microarray data pathway analysis.

Gene Association Network-Based Pathway Analysis (GANPA)
A network-based gene weighting algorithm for pathway enrichment analysis, using either RNA-seq or microarray data. Zhaoyuan Fang, Weidong Tian and Hongbin Ji (2012) <doi:10.1038/cr.2011.149>.

This is a dataset package for GANPA, which implements a
network-based gene weighting approach to pathway analysis. This
package includes data useful for GANPA, such as a functional
association network, pathways, an expression dataset and
multi-subunit proteins.

Authorize and Request Google Analytics Data
The functions included are used to obtain initial authentication with Google Analytics as well as simple and organized data retrieval from the API. Allows for retrieval from multiple profiles at once.

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.

Estimating a GARCHSK Model and GJRSK Model
c(
person("Kei","Nakagawa",email="kei.nak.0315@gmail.com",role=c("aut","cre"),comment = c(ORCID = "0000-0001-5046-8128"))
)
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.