The lava estimation is used to recover signals that is the sum of a sparse signal and a dense signal. The post-lava method corrects the shrinkage bias of lava. For more information on the lava estimation, see Chernozhukov, Hansen, and Liao (2017) <doi:10.1214/16-AOS1434>.

Tools for model specification in the latent variable framework
    (add-on to the 'lava' package). The package contains three main functionalities:
    Wald tests/F-tests with improved control of the type 1 error in small samples,
    adjustment for multiple comparisons when searching for local dependencies,
    and adjustment for multiple comparisons when doing inference for multiple latent variable models.

The lava estimation is a new technique to recover signals that is the sum of a sparse and dense signals. The post-lava method corrects the shrinkage bias of lava. For more information on the lava estimation, see Chernozhukov, Hansen, and Liao (2017) <doi:10.1214/16-AOS1434>.

A variety of models to analyze latent variables based on Bayesian learning: the partially CFA (Chen, Guo, Zhang, & Pan, 2020) <DOI: 10.1037/met0000293>; generalized PCFA; partially confirmatory IRM (Chen, 2020) <DOI: 10.1007/s11336-020-09724-3>; Bayesian regularized EFA <DOI: 10.1080/10705511.2020.1854763>; Fully and partially EFA.

Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness, and measures of relative variability, the package contains new robust tests of symmetry, omnibus and directional tests of normality, and their graphical counterparts such as robust QQ plot, robust trend tests for variances, etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal statistics, economics, and biostatistics.

Performs adjusted inferences based on model objects fitted, using 
    maximum likelihood estimation, by the extreme value analysis packages
    'eva' <https://cran.r-project.org/package=eva>, 
    'evd' <https://cran.r-project.org/package=evd>, 
    'evir' <https://cran.r-project.org/package=evir>, 
    'extRemes' <https://cran.r-project.org/package=extRemes>, 
    'fExtremes' <https://cran.r-project.org/package=fExtremes>, 
    'ismev' <https://cran.r-project.org/package=ismev>, 
    'mev' <https://cran.r-project.org/package=mev>, 
    'POT' <https://cran.r-project.org/package=POT> and
    'texmex' <https://cran.r-project.org/package=texmex>. 
    Adjusted standard errors and an adjusted loglikelihood are provided, using    
    the 'chandwich' package <https://cran.r-project.org/package=chandwich>
    and the object-oriented features of the 'sandwich' package 
    <https://cran.r-project.org/package=sandwich>. The adjustment is based on a 
    robust sandwich estimator of the parameter covariance matrix, based on the 
    methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This 
    can be used for cluster correlated data when interest lies in the 
    parameters of the marginal distributions, or for performing inferences that 
    are robust to certain types of model misspecification.  Univariate extreme 
    value models, including regression models, are supported.