Implements a two-stage estimation approach for Cox 
    regression using five-parameter M-spline functions to model the baseline hazard. It allows
    for flexible hazard shapes and model selection based on log-likelihood criteria as described in
    Teranishi et al.(2025).
    In addition, the package provides functions for constructing and evaluating B-spline copulas 
    based on five M-spline or I-spline basis functions, allowing users to flexibly model and 
    compute bivariate dependence structures. Both the copula function and its density can be evaluated.
    Furthermore, the package supports computation of dependence measures such as Kendall's tau and 
    Spearman's rho, derived analytically from the copula parameters.

Fit additive mixed meta-analysis (AMMA) models, extending
  the 'mixmeta' package <https://cran.r-project.org/package=mixmeta> 
  to allow for spline-based meta-regression. Functions combine features of 
  'mgcv' <https://cran.r-project.org/package=mgcv> for building spline 
  components and 'mixmeta' for estimating general mixed-effects meta-analysis 
  models.

Creates 'ggplot2'-based visualizations of smooth effects from GAM (Generalized Additive Models)
    fitted with 'mgcv' and spline effects from GLM (Generalized Linear Models). Supports survey-weighted
    models ('svyglm', 'svycoxph') from the 'survey' package, interaction terms, and provides hazard ratio
    plots with histograms for survival analysis.
    Wood (2017, ISBN:9781498728331) provides comprehensive methodology for generalized additive models.

Constructs basis functions of B-splines, M-splines,
    I-splines, convex splines (C-splines), periodic splines,
    natural cubic splines, generalized Bernstein polynomials,
    their derivatives, and integrals (except C-splines)
    by closed-form recursive formulas.
    It also contains a C++ head-only library integrated with Rcpp.
    See Wang and Yan (2021) <doi:10.6339/21-JDS1020> for details.

Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu and Lambert (1999) <doi:10.1080/10618600.1999.10474847>. This method allows trees and forests to be built while considering either level and shape or only shape of response trajectories.