Functions for reading DCP and CDF.bin files generated by the dChip software.

Calculate and analyze ecological connectivity across the watercourse of river networks using the Dendritic Connectivity Index.

An implementation of Dcifer (Distance for complex infections: fast estimation of relatedness), an identity by descent (IBD) based method to calculate genetic relatedness between polyclonal infections from biallelic and multiallelic data. The package includes functions that format and preprocess the data, implement the method, and visualize the results. 
    Gerlovina et al. (2022) <doi:10.1093/genetics/iyac126>.

Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).

R codes for distance based cell lineage reconstruction. Our methods won both sub-challenges 2 and 3 of the Allen Institute Cell Lineage Reconstruction DREAM Challenge in 2020. 
        References: Gong et al. (2021) <doi:10.1016/j.cels.2021.05.008>, Gong et al. (2022) <doi:10.1186/s12859-022-04633-x>.

Low level functions for implementing
    maximum likelihood estimating procedures for
    complex models using data cloning and Bayesian
    Markov chain Monte Carlo methods
    as described in Solymos 2010 <doi:10.32614/RJ-2010-011>.
    Sequential and parallel MCMC support
    for 'JAGS', 'WinBUGS', 'OpenBUGS', and 'Stan'.

Contains a single function dclust() for divisive hierarchical clustering based on 
    recursive k-means partitioning (k = 2). Useful for clustering large datasets
    where computation of a n x n distance matrix is not feasible (e.g. n > 10,000 records).
    For further information see Steinbach, Karypis and Kumar (2000) <http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf>.

A set of functions for the detection of spatial clusters
        of disease using count data. Bootstrap is used to estimate
        sampling distributions of statistics.

Model-based methods for the detection of disease clusters
  using GLMs, GLMMs and zero-inflated models. These methods are described
  in 'V. Gómez-Rubio et al.' (2019) <doi:10.18637/jss.v090.i14> and
  'V. Gómez-Rubio et al.' (2018) <doi:10.1007/978-3-030-01584-8_1>.