The main function calculates confidence intervals (CI) for Mixed Models, utilizing both classical estimators from the lmer() function in the 'lme4' package and robust estimators from the rlmer() function in the 'robustlmm' package, as well as the varComprob() function in the 'robustvarComp' package. Three methods are available: the classical Wald method, the wild bootstrap, and the parametric bootstrap. Bootstrap methods offer flexibility in obtaining lower and upper bounds through percentile or BCa methods. More details are given in Mason, F., Cantoni, E., & Ghisletta, P. (2021) <doi:10.5964/meth.6607> and Mason, F., Cantoni, E., & Ghisletta, P. (2024) <doi:10.1037/met0000643>.

Surrounds the usual sample variance of a univariate numeric sample with a confidence interval for the population variance. This has been done so far only under the assumption that the underlying distribution is normal. Under the hood, this package implements the unique least-variance unbiased estimator of the variance of the sample variance, in a formula that is equivalent to estimating kurtosis and square of the population variance in an unbiased way and combining them according to the classical formula into an estimator of the variance of the sample variance. Both the sample variance and the estimator of its variance are U-statistics. By the theory of U-statistic, the resulting estimator is unique. See Fuchs, Krautenbacher (2016) <doi:10.1080/15598608.2016.1158675> and the references therein for an overview of unbiased estimation of variances of U-statistics.

R's default conflict management system gives the most
    recently loaded package precedence. This can make it hard to detect
    conflicts, particularly when they arise because a package update
    creates ambiguity that did not previously exist. 'conflicted' takes a
    different approach, making every conflict an error and forcing you to
    choose which function to use.

Model soil gas fluxes with the Flux-Gradient Method. It includes
    functions for data handling, a forward and an inverse model for flux
    modeling and methods for calibration and uncertainty estimation. For more
    details see Gartiser et al. (2025a) <doi:10.21105/joss.08094> and Gartiser 
    et al. (2025b) <doi:10.1111/ejss.70126>.