Design dose escalation using Bayesian logistic regression modeling in Phase I oncology trial.

An interactive document on  the topic of binary logistic regression  analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/BinaryLogisticRegressionModelling/>.

Allows users to easily visualize data from the BLS (United States of America Bureau of Labor Statistics) <https://www.bls.gov>. Currently unemployment data series U1-U6 are available. Not affiliated with the Bureau of Labor Statistics or United States Government.

These functions provide a convenient interface for downloading data from the U.S. Bureau of Labor Statistics <https://www.bls.gov>.  The functions in this package utilize flat files produced by the Bureau of Labor Statistics, which contain full series history.  These files include employment, unemployment, wages, prices, industry and occupational data at a national, state, and sub-state level, depending on the series.  Individual functions are included for those programs which have data available at the state level.  The core functions provide direct access to the Current Employment Statistics (CES) <https://www.bls.gov/ces/>, Local Area Unemployment Statistics (LAUS) <https://www.bls.gov/lau/>, Occupational Employment and Wage Statistics (OEWS) <https://www.bls.gov/oes/> and Alternative Measures of Labor Underutilization (SALT) <https://www.bls.gov/lau/stalt.htm> data produced by the Bureau of Labor Statistics.

Provides a Bayesian latent space 
    model for complex networks, either weighted or unweighted.
    Given an observed input graph, the estimates for the latent coordinates 
    of the nodes are obtained through a Bayesian MCMC algorithm. 
    The overall likelihood of the graph depends on a fundamental probability 
    equation, which is defined so that ties are more likely to exist 
    between nodes whose latent space coordinates are close. 
    The package is mainly based on the model by Hoff, Raftery and Handcock (2002)
    <doi:10.1198/016214502388618906> and contains some extra features 
    (e.g., removal of the Procrustean step, weights implemented as 
    coefficients of the latent distances, 3D plots). 
    The original code related to the above model was retrieved from
    <https://www.stat.washington.edu/people/pdhoff/Code/hoff_raftery_handcock_2002_jasa/>.
    Users can inspect the MCMC simulation, create and customize insightful 
    graphical representations or apply clustering techniques.