Helper functions for creating formatted summary of regression models, writing publication-ready tables to latex files, and running Monte Carlo experiments.

Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.

A spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise.

Fits sparse generalized linear models using an adaptive ridge
    approximation to an L0 penalty. Supported model families include
    Gaussian, logistic, Poisson, gamma, and inverse Gaussian regression.
    The package also provides cross-validation for selecting the penalty
    parameter.

Under an L0 penalty framework, a computationally efficient implementation of change point detection is developed.  By integrating active set algorithms with warm start initialization, the package achieves linear-time complexity for solving change point detection problems. References: Wen et al. (2020) <doi:10.18637/jss.v094.i04>; Zhu et al. (2020)<doi:10.1073/pnas.2014241117>.

Provides smooth approximations to the L0 norm penalty for
    estimating sparse Gaussian graphical models (GGMs). Network estimation
    is performed using the Local Linear Approximation (LLA) framework
    (Fan & Li, 2001 <doi:10.1198/016214501753382273>;
    Zou & Li, 2008 <doi:10.1214/009053607000000802>) with five penalty
    functions: arctangent (Wang & Zhu, 2016 <doi:10.1155/2016/6495417>),
    EXP (Wang, Fan, & Zhu, 2018 <doi:10.1007/s10463-016-0588-3>), Gumbel,
    Log (Candes, Wakin, & Boyd, 2008 <doi:10.1007/s00041-008-9045-x>),
    and Weibull. Adaptive penalty parameters for EXP, Gumbel, and Weibull
    are estimated via maximum likelihood, and model selection uses
    information criteria including AIC, BIC, and EBIC (Extended BIC).
    Simulation functions generate multivariate normal data from GGMs with
    stochastic block model or small-world (Watts-Strogatz) network structures.

Highly optimized toolkit for approximately solving L0-regularized learning problems (a.k.a. best subset selection).
    The algorithms are based on coordinate descent and local combinatorial search.
    For more details, check the paper by Hazimeh and Mazumder (2020) <doi:10.1287/opre.2019.1919>.