'DataSHIELD' is an infrastructure and series of R packages that 
    enables the remote and 'non-disclosive' analysis of sensitive research data.
    This 'DataSHIELD Interface' implementation is for analyzing datasets living
    in the current R session. The purpose of this is primarily for lightweight
    'DataSHIELD' analysis package development.

Density surface modelling of line transect data. A Generalized
    Additive Model-based approach is used to calculate spatially-explicit estimates
    of animal abundance from distance sampling (also presence/absence and strip
    transect) data. Several utility functions are provided for model checking,
    plotting and variance estimation.

Tool collection for common and not so common data science use cases. This includes 
    custom made algorithms for data management as well as value calculations that are hard to find 
    elsewhere because of their specificity but would be a waste to get lost nonetheless. 
    Currently available functionality: find
    sub-graphs in an edge list data.frame, find mode or modes in a vector of values, extract 
    (a) specific regular expression group(s), generate ISO time stamps that play well with 
    file names, or generate URL parameter lists by expanding value combinations.

Performs parametric and non-parametric estimation and simulation of 
    drifting semi-Markov processes. The definition of parametric and non-parametric
    model specifications is also possible. Furthermore, three different types of
    drifting semi-Markov models are considered. These models differ in the number
    of transition matrices and sojourn time distributions used for the computation
    of a number of semi-Markov kernels, which in turn characterize the drifting 
    semi-Markov kernel. For the parametric model estimation and specification, 
    several discrete distributions are considered for the sojourn times: Uniform,
    Poisson, Geometric, Discrete Weibull and Negative Binomial. The non-parametric
    model specification makes no assumptions about the shape of the sojourn time
    distributions. Semi-Markov models are described in:
    Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>.
    Drifting Markov models are described in:
    Vergne, N. (2008) <doi:10.2202/1544-6115.1326>.
    Reliability indicators of Drifting Markov models are described in:
    Barbu, V. S., Vergne, N. (2019) <doi:10.1007/s11009-018-9682-8>. 
    We acknowledge the DATALAB Project
    <https://lmrs-num.math.cnrs.fr/projet-datalab.html> (financed by the
    European Union with the European Regional Development fund (ERDF) and by
    the Normandy Region) and the HSMM-INCA Project (financed by the French
    Agence Nationale de la Recherche (ANR) under grant ANR-21-CE40-0005).