Provides regularized structural equation modeling 
  (regularized SEM) with non-smooth penalty functions (e.g., lasso) building 
  on 'lavaan'. The package is heavily inspired by the 
  ['regsem'](<https://github.com/Rjacobucci/regsem>) and 
  ['lslx'](<https://github.com/psyphh/lslx>) packages.

Functions for vectorised conditional recoding of
    variables. case_when() enables you to vectorise multiple if and else
    statements (like 'CASE WHEN' in 'SQL'). if_else() is a stricter and
    more predictable version of ifelse() in 'base' that preserves
    attributes. These functions are forked from 'dplyr' with all package
    dependencies removed and behave identically to the originals.

Some simple objects and functions to do 
             statistics using linear models and a Bayesian framework.

Handling, processing, and analyzing geographic
    data on species' distributions and environmental variables. 
    Read Vilela & Villalobos (2015) <doi:10.1111/2041-210X.12401> for details.

Letter Values for the course Exploratory Data Analysis at Federal University of Bahia (Brazil). The approach implemented in the package is presented in the textbook of Tukey  (1977) <ISBN: 978-0201076165>.

Provides string similarity calculations inspired by the
    Python 'thefuzz' package. Compare strings by edit distance, similarity
    ratio, best matching substring, ordered token matching and set-based
    token matching. A range of edit distance measures are available thanks
    to the 'stringdist' package.

Implements code to identify lexical competitors in a given list
  of words. We include many of the standard competitor types used in spoken word
  recognition research, such as functions to find cohorts, neighbors, and
  rhymes, amongst many others. The package includes documentation for using a
  variety of lexicon files, including those with form codes made up of multiple
  letters (i.e., phoneme codes) and also basic orthographies. Importantly, the
  code makes use of multiple CPU cores and vectorization when possible, making
  it extremely fast and able to handle large lexicons. Additionally, the package
  contains documentation for users to easily write new functions, allowing
  researchers to examine other relationships within a lexicon. 
  Preprint: <https://osf.io/preprints/psyarxiv/8dyru/>. Open access: <doi:10.3758/s13428-021-01667-6>. 
  Citation: Li, Z., Crinnion, A.M. & Magnuson, J.S. (2021). 
  <doi:10.3758/s13428-021-01667-6>.