Simulation tools to evaluate the long-term effects of salmon management strategies, including a combination of habitat, harvest, and
  habitat actions. The stochastic age-structured operating model accommodates complex life histories, including freshwater survival across 
  early life stages, juvenile survival and fishery exploitation in the marine life stage, partial maturity by age class, and fitness impacts of 
  hatchery programs on natural spawning populations. 'salmonMSE' also provides an age-structured conditioning model to develop operating models 
  fitted to data.

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.

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.