Holds functions developed by the University of Ottawa's SAiVE
    (Spatio-temporal Analysis of isotope Variations in the Environment)
    research group with the intention of facilitating the re-use of code,
    foster good code writing practices, and to allow others to benefit
    from the work done by the SAiVE group. Contributions are welcome via
    the 'GitHub' repository <https://github.com/UO-SAiVE/SAiVE> by group members as well as non-members.

A coordinate descent algorithm for computing the solution paths of
    the sparse and coupled sparse asymmetric least squares, including the
    (adaptive) elastic net and Lasso penalized SALES and COSALES regressions.

Functions connecting to the 'Salesforce' Platform APIs (REST, SOAP, 
    Bulk 1.0, Bulk 2.0, Metadata, Reports and Dashboards) 
    <https://trailhead.salesforce.com/en/content/learn/modules/api_basics/api_basics_overview>. 
    "API" is an acronym for "application programming interface". Most all calls 
    from these APIs are supported as they use CSV, XML or JSON data that can be 
    parsed into R data structures. For more details please see the 'Salesforce' 
    API documentation and this package's website 
    <https://stevenmmortimer.github.io/salesforcer/> for more information, 
    documentation, and examples.

The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2022) <doi:10.1080/10618600.2022.2069779>.

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.