Provides tools for fitting linear mixed models using sparse matrix 
    methods and variance component estimation. Applications include spline-based 
    modeling of spatial and temporal trends using penalized splines (Boer, 2023) 
    <doi:10.1177/1471082X231178591>.

Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers"
	     taught by the section of Biostatistics of the University of Copenhagen.
	     It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...).
	     Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom.
	     Normalized residuals are provided to assess model misspecification.
	     Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients.
	     Predictions can be computed conditional to covariates only or also to outcome values.

Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model.  Arbitrary inter-observation error correlations are supported, with optimized calculations provided for independent-heteroskedastic and stationary dependence structures.

Computes model II simple linear regression using ordinary
 least squares (OLS), major axis (MA), standard major axis (SMA), and
 ranged major axis (RMA).

A complete framework for frequency analysis is provided by 'LMoFit'. It has functions related to the determination of sample L-moments as in Hosking, J.R.M. (1990) <doi:10.1111/j.2517-6161.1990.tb01775.x>, the fitting of various distributions as in Zaghloul et al. (2020) <doi:10.1016/j.advwatres.2020.103720> and Hosking, J.R.M. (2019) <https://CRAN.R-project.org/package=lmom>, besides plotting and manipulating L-space diagrams as in Papalexiou, S.M. & Koutsoyiannis, D. (2016) <doi:10.1016/j.advwatres.2016.05.005> for two-shape parametric distributions on the L-moment ratio diagram. Additionally, the quantile, probability density, and cumulative probability functions of various distributions are provided in a user-friendly manner.