Option pricing (financial derivatives) techniques mainly following textbook 'Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc. 
  This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015.

Function that implements the Quantum Genetic Algorithm, first proposed by Han and Kim in 2000. This is an R implementation of the 'python' application developed by Lahoz-Beltra  (<https://github.com/ResearchCodesHub/QuantumGeneticAlgorithms>). Each optimization problem is represented as a maximization one, where each solution is a sequence of (qu)bits. Following the quantum paradigm, these qubits are in a superposition state: when measuring them, they collapse in a 0 or 1 state. After measurement, the fitness of the solution is calculated as in usual genetic algorithms. The evolution at each iteration is oriented by the application of two quantum gates to the amplitudes of the qubits: (1) a rotation gate (always); (2) a Pauli-X gate (optionally). The rotation is based on the theta angle values: higher values allow a quicker evolution, and lower values avoid local maxima. The Pauli-X gate is equivalent to the classical mutation operator and determines the swap between alfa and beta amplitudes of a given qubit. The package has been developed in such a way as to permit a complete separation between the engine, and the particular problem subject to combinatorial optimization.

Smooth additive quantile regression models, fitted using
    the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>.
    See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. Differently from
    'quantreg', the smoothing parameters are estimated automatically by marginal
    loss minimization, while the regression coefficients are estimated using either
    PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian
    credible intervals of the estimated effects have approximately the correct
    coverage. The main function is qgam() which is similar to gam() in 'mgcv', but
    fits non-parametric quantile regression models.