A replication of key functionality from 'dplyr' and the wider 'tidyverse' using only 'base'.

Population dynamic models underpin a range of analyses and applications in ecology and epidemiology. The various approaches for analysing population dynamics models (MPMs, IPMs, ODEs, POMPs, PVA) each require the model to be defined in a different way. This makes it difficult to combine different modelling approaches and data types to solve a given problem. 'pop' aims to provide a flexible and easy to use common interface for constructing population dynamic models and enabling to them to be fitted and analysed in lots of different ways.

Simulate the dynamic of lion populations using a specific Individual-Based Model (IBM) compiled in C.

Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C, see Chapron et al. (2016) <doi:10.1016/j.ecolmodel.2016.08.012>.

Infers the trends of one or several animal populations over time 
    from series of counts. It does so by accounting for count precision 
    (provided or inferred based on expert knowledge, e.g. guesstimates), 
    smoothing the population rate of increase over time, and accounting for the
    maximum demographic potential of species. Inference is carried out in a 
    Bayesian framework. This work is part of the FRB-CESAB working group
    AfroBioDrivers 
    <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/afrobiodrivers/>.

Construct and analyze projection matrix models from a demography study of marked individuals classified by age or stage. The package covers methods described in Matrix Population Models by Caswell (2001) and Quantitative Conservation Biology by Morris and Doak (2002).

Facilitates population-level analysis of ligand-receptor (LR) interactions 
    using large-scale single-cell transcriptomic data. Identifies significant LR pairs 
    and quantifies their interactions through correlation-based filtering and projection score 
    computations. Designed for large-sample single-cell studies, the package employs 
    statistical modeling, including linear regression, to investigate LR relationships 
    between cell types. It provides a systematic framework for understanding cell-cell 
    communication, uncovering regulatory interactions and signaling mechanisms. 
    Offers tools for LR pair-level, sample-level, and differential interaction analyses, 
    with comprehensive visualization support to aid biological interpretation. 
    The methodology is described in a manuscript currently under review and will be 
    referenced here once published or publicly available.