We implement functions allowing for mediation analysis to be performed in cases where the mediator is a count variable with excess zeroes. First a function is provided allowing users to perform analysis for zero-inflated count variables using the marginalized zero-inflated Poisson (MZIP) model (Long et al. 2014 <DOI:10.1002/sim.6293>). Using the counterfactual approach to mediation and MZIP we can obtain natural direct and indirect effects for the overall population. Using delta method processes variance estimation can be performed instantaneously. Alternatively, bootstrap standard errors can be used. We also provide functions for cases with exposure-mediator interactions with four-way decomposition of total effect.

Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS.  The n1qn1 routine is useful since it allows prespecification of a Hessian.
       If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the
       'Scilab' optimization documentation located at
       <https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>. This version uses manually modified code from 'f2c' to make this a C only binary.

Provides some functions to get Korean text sample from news articles in
             Naver which is popular news portal service <https://news.naver.com/> in Korea.

Implements methods to perform fast approximate K-nearest neighbor search on input matrix. Algorithm based on the 'N2' implementation of an approximate nearest neighbor search using hierarchical  Navigable Small World (NSW) graphs. The original algorithm is described in "Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs", Y. Malkov and D. Yashunin, <doi:10.1109/TPAMI.2018.2889473>, <arXiv:1603.09320>.

This comprehensive toolkit provide a consistent and 
    extensible framework for working with missing values in vectors. The 
    companion package 'tidyimpute' provides similar functionality for list-like 
    and table-like structures).
    Functions exist for detection, removal, replacement, imputation, 
    recollection, etc. of 'NAs'.