Copula based Cox proportional hazards models for survival data subject to dependent 
    censoring. This approach does not assume that the parameter defining the copula is known. The 
    dependency parameter is estimated with other finite model parameters by maximizing a Pseudo 
    likelihood function. The cumulative hazard function is estimated via estimating equations 
    derived based on martingale ideas. Available copula functions include Frank, Gumbel and Normal
    copulas. Only Weibull and lognormal models are allowed for the censoring model, even though any
    parametric model that satisfies certain identifiability conditions could be used. Implemented 
    methods are described in the article "Copula based Cox proportional hazards models for dependent
    censoring" by Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>.

Fits a semiparametric spatiotemporal model for data with mixed frequencies,
    specifically where the response variable is observed at a lower frequency than some covariates.
    The estimation uses an iterative backfitting algorithm that combines a non-parametric smoothing spline
    for high-frequency data, parametric estimation for low-frequency and spatial neighborhood effects,
    and an autoregressive error structure. Methodology based on Malabanan, Lansangan, and Barrios (2022)
    <https://scienggj.org/2022/SciEnggJ%202022-vol15-no02-p90-107-Malabanan%20et%20al.pdf>.

Forms likelihood-based confidence intervals
    (LBCIs) for parameters in structural equation modeling,
    introduced in Cheung and Pesigan (2023)
    <doi:10.1080/10705511.2023.2183860>. Currently
    implements the algorithm illustrated by Pek and Wu
    (2018) <doi:10.1037/met0000163>, and supports the robust
    LBCI proposed by Falk (2018)
    <doi:10.1080/10705511.2017.1367254>.

Computes likelihood ratio test (LRT) p-values
  for free parameters in a structural equation model.
  Currently supports models fitted by the 'lavaan' package by
  Rosseel (2012) <doi:10.18637/jss.v048.i02>.