An R API providing access to a relational database with macroeconomic time series data for South Africa,
 obtained from the South African Reserve Bank (SARB) and Statistics South Africa (STATSSA), and updated on a weekly basis
 via the EconData <https://www.econdata.co.za/> platform and automated scraping of the SARB and STATSSA websites.
 The database is maintained at the Department of Economics at Stellenbosch University.

Health research using data from electronic health records (EHR) has gained
    popularity, but misclassification of EHR-derived disease status and lack of
    representativeness of the study sample can result in substantial bias in
    effect estimates and can impact power and type I error for association
    tests. Here, the assumed target of inference is the relationship between
    binary disease status and predictors modeled using a logistic regression
    model. 'SAMBA' implements several methods for obtaining bias-corrected
    point estimates along with valid standard errors as proposed in Beesley and
    Mukherjee (2020) <doi:10.1101/2019.12.26.19015859>, currently under review.

A collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) <doi:10.1155/2017/7847531>. The methods may be used for further purposes where weighting and generation of new observations is needed.

Implements functions for working with absorbing Markov chains. The
    implementation is based on the framework described in "Toward a unified
    framework for connectivity that disentangles movement and mortality in space
    and time" by Fletcher et al. (2019) <doi:10.1111/ele.13333>, which applies
    them to spatial ecology. This framework incorporates both resistance and 
    absorption with spatial absorbing Markov chains (SAMC) to provide several
    short-term and long-term predictions for metrics related to connectivity in 
    landscapes. Despite the ecological context of the framework, this package
    can be used in any application of absorbing Markov chains.