Dataset containing information about job listings for data science job roles.

An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class.

Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops. 26 datasets are available for case studies in data visualization, statistical inference, modeling, linear regression, data wrangling and machine learning.

Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. It could be applied for non-parametric hypothesis testing and gene set enrichment analysis.

'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.

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).