The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC)
    sampling of random variables on a simplex. A Self-Adjusting Logit Transform
    (SALT) proposal is used so that sampling is still efficient even in difficult
    cases, such as those in high dimensions or with parameters that differ by orders
    of magnitude. Special care is also taken to maintain accuracy even when some
    coordinates approach 0 or 1 numerically. Diagnostic and graphic functions are
    included in the package, enabling easy assessment of the convergence and mixing
    of the chain within the constrained space.

Take real or simulated data and salt it with errors commonly 
    found in the wild, such as pseudo-OCR errors, Unicode problems, numeric 
    fields with nonsensical punctuation, bad dates, etc.

Computationally efficient tools for high dimensional predictive
        modeling (regression and classification). SAM is short for sparse 
        additive modeling, and adopts the computationally efficient basis 
        spline technique. We solve  the optimization problems by various 
        computational algorithms including the block coordinate descent 
        algorithm, fast iterative soft-thresholding algorithm, and newton method. 
        The computation is further accelerated by warm-start and active-set tricks.

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.