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.

Provides tools to retrieve and summarize taxonomic information and synonymy data for reptile species using data scraped from The Reptile Database website (<https://reptile-database.reptarium.cz/>). Outputs include clean and structured data frames useful for ecological, evolutionary, and conservation research.

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>.

Implements the Leunbach test equating method, following the
    'DIGRAM' software written by Svend Kreiner. Both direct and indirect
    equating are available, with parametric bootstrap standard errors and
    diagnostic statistics including the Goodman-Kruskal gamma test and
    orbit analysis for person fit. See Adroher et al. (2019)
    <doi:10.1186/s12874-019-0768-y> for details of the method.

An (upper) level set of a function is the set of inputs for 
  which the function value is at or above a specified threshold.  (Also 
  called an excursion set).  Applications of level sets include confidence 
  or credible regions for parameters of statistical models, where the function 
  is the likelihood or posterior density; regions where classification rules 
  assign high probability to a given class; and scientific or engineering 
  models where one is interested in input regions for which model output 
  is above a threshold.  This package maps out the boundary of a level 
  set by finding its intersections with collections of 1-dimensional rays, 
  generalizing a proposal by Kim and Lindsay (Statistica Sinica 21:923-948, 
  2011).  Tools are provided to generate rays, find intersections, and 
  visualize results.  The package makes few assumptions about the studied 
  function:  it may be discontinuous, it may have a complicated feasible 
  region, and the target level set may be non-convex or have multiple, 
  disconnected parts.  Vignettes describe package usage and show examples 
  with two to five input space dimensions.