analyzer, POS tagger, Keystroke converter, Hangul automata,

person("Heewon", "Jeon", email = "madjakarta@gmail.com", role = c("aut", "cre")),
Description: POS Tagger and Morphological Analyzer for Korean text based research. It provides tools for corpus linguistics research such as Keystroke converter, Hangul automata, Concordance, and Mutual Information. It also provides a convenient interface for users to apply, edit and add morphological dictionary selectively.

Title: Download Data from Kobotoolbox to R
Authors@R: person("Asitav ", "Sen", email = "hello@asitavsen.com",
Description: Wrapper for 'Kobotoolbox' APIs ver 2 mentioned at <https://support.kobotoolbox.org/api.html>, to download data from 'Kobotoolbox' to R. Small and simple package that adds immense convenience for the data professionals using 'Kobotoolbox'.
Imports: httr, curl, jsonlite, mime, openssl, R6, methods, dplyr
Packaged: 2022-03-29 12:18:27 UTC; scarecrow

Packaged: 2022-05-15 06:28:33 UTC; scarecrow

Packaged: 2022-05-23 13:08:44 UTC; scarecrow

Imports: httr, curl, jsonlite, mime, openssl, R6, methods, dplyr,
Packaged: 2022-10-16 18:41:33 UTC; scarecrow

Packaged: 2022-10-18 22:37:36 UTC; scarecrow

Title: Koul's Minimum Distance Estimation in Linear Regression and
Description: Consider linear regression model and autoregressive model of order p where errors in the linear regression model and innovations in the autoregression model are independent and symmetrically distributed. Hira L. Koul proposed a nonparametric minimum distance estimation method by minimizing L2-type distance between certain weighted residual empirical distribution functions. He also proposed a simpler version of  the loss function by using symmetry of the integrating measure in the distance. This package contains two functions: KoulLrMde() and KoulArMde(). KoulLrMde() and KoulArMde() provide minimum distance estimators for linear regression model and autoregression model, respectively, where both are based on Koul's method. These two functions take much less time for the computation than those based on parametric minimum distance estimation methods.