Counts syllables in character vectors for English words.  Imputes syllables as the number of vowel sequences for words not found.

Downloads the latest 'National Transit Database' 
    data, processes it, and returns in a tidy data format.

Implements statistical tools for analyzing, simulating, and computing properties of the New Topp-Leone Kumaraswamy Inverse Exponential (NTLKwIEx) distribution. See Atchadé M, Otodji T, and Djibril A (2024) <doi:10.1063/5.0179458> and Atchadé M, Otodji T, Djibril A, and N'bouké M (2023) <doi:10.1515/phys-2023-0151> for details.

Dealing with neutrosophic data of the form N=D+I(where N is a Neutrosophic number
   ,D is the determinant part of the number and I is  the indeterminacy part)
   using the neutrosophic two way anova test keeps the type I error low.
   This algorithm calculates the fisher statistics when we have a neutrosophic
   data, also tests two hypothesizes, first is to test differences between treatments,
   and second is to test differences between sectors. For more information see
    Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022)
    <https://www.americaspg.com/articleinfo/21/show/1058>.

Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).

Computes the pdf, cdf, quantile function and generating random numbers for neutrosophic distributions. This family have been developed by different authors in the recent years. See Patro and Smarandache (2016) <doi:10.5281/zenodo.571153> and Rao et al (2023) <doi:10.5281/zenodo.7832786>.

Nonparametric test of independence between a pair of spatial objects
    (random fields, point processes) based on random shifts with torus or variance correction. See
    Mrkvička et al. (2021) <doi:10.1016/j.spasta.2020.100430>,
    Dvořák et al. (2022) <doi:10.1111/insr.12503>,
    Dvořák and Mrkvička (2024) <doi:10.1080/10618600.2024.2357626>.