Carlos A. Silva, Nicholas L. Crookston,
Andrew T. Hudak, Lee A. Vierling
Set of tools for reading, processing and visualizing small set
of LiDAR (Light Detection and Ranging) data for forest inventory applications.

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.

Synchrony in Motion Energy Analysis (MEA) Time-Series
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>.

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>.

Multiple Imputation using Denoising Autoencoders
c(
person(given = "Thomas",
family = "Robinson",
role = c("aut", "cre", "cph"),
email = "ts.robinson1994@gmail.com",
comment = c(ORCID = "0000-0001-7097-1599")),
person(given = "Ranjit",
family = "Lall",
role = c("aut","cph"),
email = "R.Lall@lse.ac.uk",
comment = c(ORCID = "0000-0003-1455-3506")),
person(given = "Alex",
family = "Stenlake",
role = c("ctb","cph"))
)
A tool that allows users to impute missing data with 'MIDAS', a multiple imputation method using denoising autoencoders as documented in Lall and Robinson (2020) <doi:10.33774/apsa-2020-3tk40-v3>. This method has significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when run on large datasets with many columns or categories. Alongside interfacing with 'Python' to run the core algorithm, this package contains tools to process the data before and after model training, run imputation model diagnostics, generate multiple completed datasets, and estimate multiply-imputed regression models.
R (>= 3.6.0), data.table, mltools, reticulate
Thomas Robinson [aut, cre, cph]
(<https://orcid.org/0000-0001-7097-1599>),
Ranjit Lall [aut, cph] (<https://orcid.org/0000-0003-1455-3506>),
Alex Stenlake [ctb, cph]