Plots the coefficients from model objects. This very quickly shows the user the point estimates and confidence intervals for fitted models.

Simulate species occurrence and abundances (counts) along
    gradients.

Simulates the composition of samples of vegetation
        according to gradient-based vegetation theory.  Features a
        flexible algorithm incorporating competition and complex
        multi-gradient interaction.

Contrast analysis for factorial designs provides an
    alternative to the traditional ANOVA approach, offering the distinct
    advantage of testing targeted hypotheses. The foundation of this
    package is primarily rooted in the works of Rosenthal, Rosnow, and
    Rubin (2000, ISBN: 978-0521659802) as well as Sedlmeier and Renkewitz
    (2018, ISBN: 978-3868943214).

A spatially-aware cell clustering algorithm is provided with cluster significance assessment. It comprises four key modules: spatially-aware cell-gene co-embedding, cell clustering, signature gene identification, and cluster significant assessment. More details can be referred to Peng Xie, et al. (2025) <doi:10.1016/j.cell.2025.05.035>.

While individual calibrated radiocarbon dates can span several centuries, combining multiple dates together with any chronological constraints can make a chronology much more robust and precise. This package uses Bayesian methods to enforce the chronological ordering of radiocarbon and other dates, for example for trees with multiple radiocarbon dates spaced at exactly known intervals (e.g., 10 annual rings). For methods see Christen 2003 <doi:10.11141/ia.13.2>. Another example is sites where the relative chronological position of the dates is taken into account - the ages of dates further down a site must be older than those of dates further up (Buck, Kenworthy, Litton and Smith 1991 <doi:10.1017/S0003598X00080534>; Nicholls and Jones 2001 <doi:10.1111/1467-9876.00250>). The paper accompanying this R package is Blaauw et al. 2024 <doi:10.1017/RDC.2024.56>.