Title: LiDAR Data Processing and Visualization
Author: Carlos A. Silva, Nicholas L. Crookston,
Andrew T. Hudak, Lee A. Vierling, Carine Klauberg, Adri<c3><a1>n Cardil
Maintainer: Carlos A. Silva <carlos_engflorestal@outlook.com>
Description: Set of tools for reading, processing and visualizing small set of
LiDAR (Light Detection and Ranging) data for forest inventory applications.

person("Nicholas L.", "Crookston", role = c("aut", "ctb")),
person("Andrew T.", "Hudak", role = c("aut", "ctb")),
person("Lee A.", "Vierling", role = c("aut", "ctb")),
person("Carine", "Klauberg", email = "carine_klauberg@hotmail.com", role = c("aut", "ctb")),
person("Adrian", "Cardil", email = "adriancardil@gmail.com", role = c("aut", "ctb")),
person("Caio", "Hamamura", email = "caiohamamura@gmail.com", role = c("aut", "ctb"), comment = "Maintenance and review"))
Imports: bitops, deldir, geometry, methods, plyr, raster, rgeos, rgl,
Caio Hamamura [aut, ctb] (Maintenance and review)

Description: Set of tools for reading, processing and visualizing small set
of LiDAR (Light Detection and Ranging) data for forest inventory applications.

Description: An interface to LINDO API. Supports Linear, Integer, Quadratic, Conic, General Nonlinear, Global, and Stochastic Programming models. To download the trial version LINDO API, please visit www.lindo.com/rlindo.

Title: Synchrony in Motion Energy Analysis (MEA) Time-Series
Author: Johann R. Kleinbub, Fabian Ramseyer
Maintainer: Johann R. Kleinbub <johann.kleinbub@gmail.com>
Description: A suite of tools useful to read, visualize and export bivariate motion energy time-series. Lagged synchrony between subjects can be analyzed through windowed cross-correlation. Surrogate data generation allows an estimation of pseudosynchrony that helps to estimate the effect size of the observed synchronization. Ramseyer & Tschacher (2011) <doi:10.1037/a0023419>.

Description: A suite of tools useful to read, visualize and export bivariate motion energy time-series. Lagged synchrony between subjects can be analyzed through windowed cross-correlation. Surrogate data generation allows an estimation of pseudosynchrony that helps to estimate the effect size of the observed synchronization. Kleinbub, J. R., & Ramseyer, F. T. (2020). rMEA: An R package to assess nonverbal synchronization in motion energy analysis time-series. Psychotherapy research, 1-14. <doi:10.1080/10503307.2020.1844334>.