Developed for the following tasks. Simulating, computing maximum likelihood 
             estimator, computing the Fisher information matrix, computing goodness-of-fit measures,
             and correcting bias of the ML estimator for a wide range of distributions fitted to units
             placed on progressive type-I interval censoring and progressive type-II censoring plans.  
             The methods of Cox and Snell (1968) <doi:10.1111/j.2517-6161.1968.tb00724.x> and
             bootstrap method for computing the bias-corrected maximum likelihood estimator.

Implementation of bivariate binomial, geometric, and Poisson distributions based on conditional specifications. The package also includes tools for data generation and goodness-of-fit testing for these three distribution families. For methodological details, see Ghosh, Marques, and Chakraborty (2025) <doi:10.1080/03610926.2024.2315294>, Ghosh, Marques, and Chakraborty (2023) <doi:10.1080/03610918.2021.2004419>, and Ghosh, Marques, and Chakraborty (2021) <doi:10.1080/02664763.2020.1793307>.

A collection of functions for structure learning of causal networks and estimation of joint causal effects from observational Gaussian data. Main algorithm consists of a Markov chain Monte Carlo scheme for posterior inference of causal structures, parameters and causal effects between variables.
    References:
    F. Castelletti and A. Mascaro (2021) <doi:10.1007/s10260-021-00579-1>,
    F. Castelletti and A. Mascaro (2022) <doi:10.48550/arXiv.2201.12003>,
    F. Castelletti and A. Mascaro (2026) <doi:10.18637/jss.v116.i05>.

Search, query, and download tabular and
    'geospatial' data from the British Columbia Data Catalogue
    (<https://catalogue.data.gov.bc.ca/>).  Search catalogue data records
    based on keywords, data licence, sector, data format, and B.C.
    government organization. View metadata directly in R, download many
    data formats, and query 'geospatial' data available via the B.C.
    government Web Feature Service ('WFS') using 'dplyr' syntax.