Facilitates access to the data from the Atlas do Estado Brasileiro
    (<https://www.ipea.gov.br/atlasestado/>), maintained by the Instituto de
    Pesquisa Econômica Aplicada (Ipea). It allows users to search for specific
    series, list series or themes, and download data when available.

A powerful tool for automating the early detection of disease
             outbreaks in time series data. 'aeddo' employs advanced statistical 
             methods, including hierarchical models, in an innovative manner to 
             effectively characterize outbreak signals. It is particularly useful 
             for epidemiologists, public health professionals, and researchers 
             seeking to identify and respond to disease outbreaks in a timely 
             fashion. For a detailed reference on hierarchical models, consult 
             Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557.

Package to incorporate change point analysis in ARIMA forecasting.

Implements almost-exact inference for the DerSimonian-Laird
    test statistic in the normal-normal random-effects meta-analysis model,
    as described in Hanada and Sugimoto (2023) <doi:10.1007/s10463-022-00844-4>.
    The method approximates the distribution of the DerSimonian-Laird test
    statistic by combining the distribution of the untruncated
    DerSimonian-Laird estimator of the between-study variance with a
    conditional normal approximation. Methods based on a plug-in
    between-study variance and a corrected heterogeneity measure are provided.

A powerful tool for automating the early detection of seasonal epidemic
             onsets in time series data. It offers the ability to estimate growth rates
             across consecutive time intervals, calculate the sum of cases (SoC) within
             those intervals, and estimate seasonal onsets within user defined seasons.
             With use of a disease-specific threshold it also offers the possibility to
             estimate seasonal onset of epidemics.
             Additionally it offers the ability to estimate burden levels for seasons
             based on historical data. It is aimed towards epidemiologists,
             public health professionals, and researchers seeking to identify and respond
             to seasonal epidemics in a timely fashion.