Implements a novel density-based approach for estimating unknown parameters, distribution visualisations and meta-analyses of quantiles and ther functions. 
	A detailed vignettes with example datasets and code to prepare data and analyses is available at <https://bookdown.org/a2delivera/metaquant/>.
	The methods are described in the pre-print by De Livera, Prendergast and Kumaranathunga (2024, <doi:10.48550/arXiv.2411.10971>).

Build spatially and temporally explicit
    process-based species distribution models, that can include an arbitrary
    number of environmental factors, species and processes including metabolic
    constraints and species interactions. The focus of the package is simulating
    populations of one or multiple species in a grid-based landscape and studying
    the meta-population dynamics and emergent patterns that arise from the
    interaction of species under complex environmental conditions. It
    provides functions for common ecological processes such as
    negative exponential, kernel-based dispersal (see
    Nathan et al. (2012) <doi:10.1093/acprof:oso/9780199608898.003.0015>),
    calculation of the environmental suitability based on cardinal values (
    Yin et al. (1995) <doi:10.1016/0168-1923(95)02236-Q>, simplified by
    Yan and Hunt (1999) <doi:10.1006/anbo.1999.0955> see eq: 4), reproduction in
    form of an Ricker model (see Ricker (1954) <doi:10.1139/f54-039> and
    Cabral and Schurr (2010) <doi:10.1111/j.1466-8238.2009.00492.x>),
    as well as metabolic scaling based on the metabolic theory of ecology
    (see Brown et al. (2004) <doi:10.1890/03-9000> and
    Brown, Sibly and Kodric-Brown (2012)
    <doi:10.1002/9781119968535.ch>).

User-friendly package for reporting replicability-analysis methods, affixed to meta-analyses summary. The replicability-analysis output provides an assessment of the investigated intervention, where it offers quantification of effect replicability and assessment of the consistency of findings.
 - Replicability-analysis for fixed-effects and random-effect meta analysis: 
 - r(u)-value;
 - lower bounds on the number of studies with replicated positive and\or negative effect;
 - Allows detecting inconsistency of signals;
 - forest plots with the summary of replicability analysis results;
 - Allows Replicability-analysis with or without the common-effect assumption.