# Introduction

In most free-living bird populations moult progression and duration in individuals can not be observed fully. Instead snapshot measurements of (re)captured individuals are typically used to infer these parameters on a population level. As an additional complication, recording of moult in the field may take various forms both in terms of the subset of the population that is sampled and whether moult is recorded as a categorical state, or a (semi-)continuous progression.

Underhill & Zucchini (1989; Ibis 130:358) proposed a general modelling framework to accommodate many of these features, implemented in the R package moult (Erni et al. 2013; J Stat Soft 52:8).

moultmcmc implements a Bayesian inference framework for this class of models with the aim of (eventually) allowing the inclusion of hierarchical model structures to accommodate 1) the integration of moult data sets using different modes of recording, 2) individual heterogeneity in moult timing and progression, and 3) hierarchical spatial/temporal effects for multi-site/multi-season data sets.

moultmcmc implements fast inference for these models using Hamiltonian Monte Carlo samplers from Stan. The currently implemented models are described in detail in the vignette ‘Moult data likelihoods’.

# Installation

To install moultmcmc from the github source:

install.packages("remotes")
remotes::install_github("pboesu/moultmcmc")

# Usage

Basic usage is described in the vignette ‘Getting started with moultmcmc’
Please note that moultmcmc is under active development and API changes may still occur.