Two-Step Lasso (TS-Lasso) and compound minimum methods to recover 
             the abundance of missing peaks in mass spectrum analysis. TS-Lasso is an 
             imputation method that handles various types of missing peaks simultaneously. This package 
             provides the procedure to generate missing peaks (or data) for simulation study, 
             as well as a tool to estimate and visualize the proportion of missing at random.

Interface between the GMT map-making software and R, enabling the
  user to manipulate geographic data within R and call GMT commands to draw and
  annotate maps in postscript format. The gmt package is about interactive data
  analysis, rapidly visualizing subsets and summaries of geographic data, while
  performing statistical analysis in the R console.

The multiple contrast tests for univariate were proposed by Munko, Ditzhaus, Pauly, Smaga, and Zhang (2023) <doi:10.48550/arXiv.2306.15259>. Recently, they were extended to the multivariate functional data in Munko, Ditzhaus, Pauly, and Smaga (2024) <doi:10.48550/arXiv.2406.01242>. These procedures enable us to evaluate the overall hypothesis regarding equality, as well as specific hypotheses defined by contrasts. In particular, we can perform post hoc tests to examine particular comparisons of interest. Different experimental designs are supported, e.g., one-way and multi-way analysis of variance for functional data.

Unconstrained and constrained maximum likelihood estimation of structural and reduced form 
    Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t
    mixture vector autoregressive models, quantile residual tests, graphical diagnostics,
    simulations, forecasting, and estimation of generalized impulse response function and generalized 
    forecast error variance decomposition.
    Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>,
    Savi Virolainen (2025) <doi:10.1080/07350015.2024.2322090>,
    Savi Virolainen (in press) <doi:10.1016/j.ecosta.2025.09.003>.