An efficient and flexible system to solve sparse mixed model
    equations. Important applications are the use of splines to model spatial or temporal
    trends as described in 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).

Functions related to L-moments: computation of L-moments
  and trimmed L-moments of distributions and data samples; parameter
  estimation; L-moment ratio diagram; plot vs. quantiles of an
  extreme-value distribution.

Extensive functions for Lmoments (LMs) and probability-weighted moments (PWMs),
  distribution parameter estimation, LMs for distributions, LM ratio diagrams, multivariate
  Lcomoments, and asymmetric (asy) trimmed LMs (TLMs). Maximum likelihood and
  maximum product spacings estimation are available. Right-tail and left-tail LM censoring
  by threshold or indicator variable are available. LMs of residual (resid) and reversed
  (rev) residual life are implemented along with 13 quantile operators for reliability analyses.
  Exact analytical bootstrap estimates of order statistics, LMs, and LM var-covars are available.
  Harri-Coble Tau34-squared Normality Test is available. Distributions with L, TL, and added
  (+) support for right-tail censoring (RC) encompass: Asy Exponential (Exp) Power [L],
  Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp
  Poisson [L], Gen Extreme Value [L], Gen Lambda [L, TL], Gen Logistic [L], Gen Normal [L],
  Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L],
  Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L],
  3p log-Normal [L], Pearson Type III [L], Polynomial Density-Quantile 3 and 4 [L],
  Rayleigh [L], Rev-Gumbel [L+RC], Rice [L], Singh Maddala [L], Slash [TL], 3p Student t [L],
  Truncated Exponential [L], Wakeby [L], and Weibull [L].