Maximum a posteriori (MAP) estimation for topic models (i.e., Latent Dirichlet Allocation) in text analysis,
	as described in Taddy (2012) 'On estimation and selection for topic models'.  Previous versions of this code were included as part of the 'textir' package.  If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.

Functions with example data for graphing, pruning, and
        mapping models from hierarchical clustering, and classification
        and regression trees.

Quickly and conveniently create interactive
    visualisations of spatial data with or without background maps.
    Attributes of displayed features are fully queryable via pop-up
    windows. Additional functionality includes methods to visualise true-
    and false-color raster images and bounding boxes.

Policy evaluation using generalized Qini curves: Evaluate data-driven treatment
            targeting rules for one or more treatment arms over different budget
            constraints in experimental or observational settings under unconfoundedness.

R functions for the estimation and eigen-decomposition of multivariate autoregressive models.

Multiplicative AR(1) with Seasonal is a stochastic
  process model built on top of AR(1). The package provides the
  following procedures for MAR(1)S processes: fit, compose, decompose,
  advanced simulate and predict.

Supports visual interpretation of hierarchical composite
    endpoints (HCEs). HCEs are complex constructs used as primary endpoints in
    clinical trials, combining outcomes of different types into ordinal endpoints,
    in which each patient contributes the most clinically important event (one and
    only one) to the analysis. See Karpefors M et al. (2022)
    <doi:10.1177/17407745221134949>.

Recently, multiple marginal variable selection methods have been developed and shown to be effective in Gene-Environment interactions studies. We propose a novel marginal Bayesian variable selection method for Gene-Environment interactions studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo. The core algorithms of the package have been developed in 'C++'.