Provides a Bayesian variable selection approach using continuous spike and slab prior distributions. The prior choices here are motivated by the shrinking and diffusing priors studied in Narisetty & He (2014) <DOI:10.1214/14-AOS1207>.

We perform linear, logistic, and cox regression using the base functions lm(), 
    glm(), and coxph() in the R software and the 'survival' package. Likewise, we 
    can use ols(), lrm() and cph() from the 'rms' package for the same 
    functionality. Each of these two sets of commands has a different focus.
  In many cases, we need to use both sets of commands in the same situation, 
    e.g. we need to filter the full subset model using AIC, and we need to build 
    a visualization graph for the final model. 'base.rms' package can help you to switch 
    between the two sets of commands easily.

Compatibility wrapper to replace the orphaned package. New 
    applications should use base64 encoders from 'jsonlite' or 'openssl' or
    'base64enc'.

This package provides tools for handling base64 encoding. It is more flexible than the orphaned base64 package.

In contrast to RFC3548, the 62nd character ("+") is replaced with
    "-", the 63rd character ("/") is replaced with "_". Furthermore, the encoder
    does not fill the string with trailing "=". The resulting encoded strings
    comply to the regular expression pattern "[A-Za-z0-9_-]" and thus are
    safe to use in URLs or for file names.
    The package also comes with a simple base32 encoder/decoder suited for
    case insensitive file systems.

Provides numerous utilities for acquiring and analyzing
    baseball data from online sources such as 'Baseball Reference' <https://www.baseball-reference.com/>,
    'FanGraphs' <https://www.fangraphs.com/>, and the 'MLB Stats' API <https://www.mlb.com/>.

Provides functions streamlining the data analysis workflow: 
  Outsourcing data import, renaming and type casting to a *.csv.
  Manipulating imputed datasets and fitting models on them. Summarizing models.