Estimates the parameters and nonparametric functions of an ARCH-m(X) model with exogenous covariates, estimates the parameters and nonparametric functions of an Additive ARCH-m(X) model with exogenous covariates, estimates the parameters of a GARCH-X model with exogenous covariates, performs hypothesis tests for the covariates returning the p-values, and performs stepwise variable selection on the exogenous covariates, and uses False Discovery Rate p-value corrections to select the exogenous variables.

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.

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.

Flexible and robust estimation and inference of Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models with covariates ('X') based on the results by Francq and Thieu (2019) <doi:10.1017/S0266466617000512>. Coefficients can straightforwardly be set to zero by omission, and quasi maximum likelihood methods ensure estimates are generally consistent and inference valid, even when the standardised innovations are non-normal and/or dependent over time. See Sucarrat (2021) <doi:10.32614/RJ-2021-057> for an overview of the package.

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.

Provides a genetic algorithm framework for regression problems requiring discrete optimization over model spaces with unknown or varying dimension, where gradient-based methods and exhaustive enumeration are impractical. Uses a compact chromosome representation for tasks including spline knot placement and best-subset variable selection, with constraint-preserving crossover and mutation, exact uniform initialization under spacing constraints, steady-state replacement, and optional island-model parallelization from Lu, Lund, and Lee (2010, <doi:10.1214/09-AOAS289>). The computation is built on the 'GA' engine of Scrucca (2017, <doi:10.32614/RJ-2017-008>) and 'changepointGA' engine from Li and Lu (2024, <doi:10.48550/arXiv.2410.15571>). In challenging high-dimensional settings, 'GAReg' enables efficient search and delivers near-optimal solutions when alternative algorithms are not well-justified.