A collection of string functions designed for writing 
    compact and expressive R code. 'yasp' (Yet Another String Package) is simple,
    fast, dependency-free, and written in pure R. The package provides: a
    coherent set of abbreviations for paste() from package 'base' with a
    variety of defaults, such as p() for "paste" and pcc() for "paste and
    collapse with commas"; wrap(), bracket(), and others for wrapping a
    string in flanking characters; unwrap() for removing pairs of characters
    (at any position in a string); and sentence() for cleaning whitespace
    around punctuation and capitalization appropriate for prose sentences.

Provides functions to manage taxonomy when lineages are
    described with strings and ranks separated with special patterns like
    "|*__" or ";*__".

Test of linearity originally proposed by Yatchew (1997) <doi:10.1016/S0165-1765(97)00218-8> and improved by de Chaisemartin & D'Haultfoeuille (2024) <doi:10.2139/ssrn.4284811> to be robust under heteroskedasticity.

Determines the sum of squares of the (2^n)-1 factorial effects in a 2^n factorial experiment using Yates' algorithm.

Nonparametric estimation of discount functions and yield curves from 
    transaction data of coupon paying bonds. 
    Koo, B., La Vecchia, D., & Linton, O. B. (2021) <doi:10.1016/j.jeconom.2020.04.014> 
    describe an application of this package using the Center for Research in 
    Security Prices (CRSP) Bond Data and document its implementation.

Asks Yes-No questions with variable or custom responses.

Facilitates download of financial data from Yahoo Finance <https://finance.yahoo.com/>, 
 a vast repository of stock price data across multiple financial exchanges. The package offers a local caching system
 and support for parallel computation.

The purpose of this package is to provide methods to interpret multiple
  linear regression and canonical correlation results including beta weights,structure coefficients, 
  validity coefficients, product measures, relative weights, all-possible-subsets regression,
  dominance analysis, commonality analysis, and adjusted effect sizes.