Gives users seeking federal lobbying disclosures an easier way to query the API maintained by the Senate federal lobbying disclosures database <https://lda.senate.gov/api/redoc/v1/> to find out how much companies and other entities are spending to lobby Congress and the federal government. It allows for search terms such as keywords, time periods and entity names. It then attempts to clean, or at least flag, filings that could provide incorrect results when seeking to answer the question: How much is being spent on lobbying our Congress and the administration and what issues do they care about?

Automated analysis and modeling of longitudinal 'omics' data (e.g. breath 'metabolomics') using generalized spline mixed effect models. Including automated filtering of noise parameters and determination of breakpoints.

Simulate lobster catch process in a trap fishery. Factors such as lobster density on ocean floor, their movement, trap saturation and bait shrinkage rate can be modeled. Details of the methods for modeling those processes can be found in: Addison and Bell (1997) <doi:10.1071/MF97169>.

A set of tools for inspecting and understanding R data
    structures inspired by str(). Includes ast() for visualizing abstract
    syntax trees, ref() for showing shared references, cst() for showing
    call stack trees, and obj_size() for computing object sizes.

Network analysis usually requires estimating the uncertainty of
    graph statistics. Through this package, we provide tools to bootstrap 
    various networks via local bootstrap procedure. Additionally, it includes 
    functions for generating probability matrices, creating network adjacency 
    matrices from probability matrices, and plotting network structures. 
    The reference will be updated soon.

Implements novel nonparametric approaches to address
    biases and confounding when comparing treatments or exposures in
    observational studies of outcomes. While designed and appropriate for use
    in studies involving medicine and the life sciences, the package can be
    used in other situations involving outcomes with multiple confounders.
    The package implements a family of methods for non-parametric bias correction
    when comparing treatments in observational studies, including survival
    analysis settings, where competing risks and/or censoring may be present.
    The approach extends to bias-corrected personalized predictions of treatment
    outcome differences, and analysis of heterogeneity of treatment effect-sizes
    across patient subgroups. For further details, please see: 
    Lauve NR, Nelson SJ, Young SS, Obenchain RL, Lambert CG. LocalControl:
    An R Package for Comparative Safety and Effectiveness Research.
    Journal of Statistical Software. 2020. p. 1–32. Available from <doi:10.18637/jss.v096.i04>.