An interface to 'LuaJIT' <https://luajit.org>, a just-in-time 
    compiler for the 'Lua' scripting language <https://www.lua.org>. Allows 
    users to run 'Lua' code from 'R'.

Functions to work with date-times and time-spans: fast and
    user friendly parsing of date-time data, extraction and updating of
    components of a date-time (years, months, days, hours, minutes, and
    seconds), algebraic manipulation on date-time and time-span objects.
    The 'lubridate' package has a consistent and memorable syntax that
    makes working with dates easy and fun.

Likelihood inference under covariate assumptions (LUCA) in case-control studies of a rare disease assuming independence or simple dependence of genetic and
        non-genetic covariates.

Reproduces the harmonized DB of the ESTAT survey of the same name. The survey data is served as separate spreadsheets with noticeable differences in the collected attributes. The tool here presented carries out a series of instructions that harmonize the attributes in terms of name, meaning, and occurrence, while also introducing a series of new variables, instrumental to adding value to the product. Outputs include one harmonized table with all the years, and three separate geometries, corresponding to the theoretical point, the gps location where the measurement was made and the 250m east-facing transect.

Print vectors (and data frames) of floating point numbers
    using a non-scientific format optimized for human readers.  Vectors
    of numbers are rounded using significant digits, aligned at the
    decimal point, and all zeros trailing the decimal point are dropped.
    See: Wright (2016). Lucid: An R Package for Pretty-Printing Floating Point
    Numbers. In JSM Proceedings, Statistical Computing Section. Alexandria,
    VA: American Statistical Association. 2270-2279.

An implementation of estimating the Latent Unknown Clusters By Integrating Multi-omics Data (LUCID) model (Peng (2019) <doi:10.1093/bioinformatics/btz667>). LUCID conducts integrated clustering using exposures, omics data (and outcome as an option). This is a major update from the last version while conserving all the previous features. This package implements three different integration strategies for multiple omics data analysis within the LUCID framework: LUCID early integration (the original LUCID model), LUCID in parallel (intermediate), and LUCID in serial (late). Automated model selection for each LUCID model is available to obtain the optimal number of latent clusters, and an integrated imputation approach is implemented to handle sporadic and list-wise missing multiple omics data.