Package for processing downloaded MODIS Calibrated radiances
    Product HDF files. Specifically, MOD02 calibrated radiance product files, and
    the associated MOD03 geolocation files (for MODIS-TERRA). The package will be
    most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath
    products; <https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and
    adds the directory with the MRTSwath executable to the default R PATH by editing
    ~/.Rprofile.

Provides tools for exploratory process data analysis. Process data refers to the data describing
    participants' problem-solving processes in computer-based assessments. It is often recorded in computer
    log files. This package provides functions to read, process, and write process data. It also implements
    two feature extraction methods to compress the information stored in process data into standard 
    numerical vectors. This package also provides recurrent neural network based models that relate response processes 
    with other binary or scale variables of interest. The functions that involve training and evaluating neural networks 
    are wrappers of functions in 'keras'.

A small, dependency-free way to generate random names. Methods 
    provided include the adjective-surname approach of Docker containers 
    ('<https://github.com/moby/moby/blob/master/pkg/namesgenerator/names-generator.go>'),
    and combinations of common English or Spanish words.

Computes classical process capability indices (Cp, Cpk, Cpu, Cpl,
    Cpm, Cpmk, Pp, Ppk, Ppu, Ppl, Z) and the generalized process capability
    index Cpy (Maiti, Saha & Nanda, 2010) <doi:10.1080/16843703.2010.11673233>
    for any continuous or discrete quality characteristic. Users supply the
    probability density function (PDF) and cumulative distribution function (CDF)
    of the characteristic, and the package returns point estimates, bootstrap
    confidence intervals (percentile and BCa), and sensitivity tables/plots
    across ranges of short-term standard deviation (sigma), long-term standard
    deviation (s), desired yield (p0), and significance levels. Classical indices
    are recoverable as special cases under the normal distribution. The package
    follows the theory and notation of Kane (1986) <doi:10.1080/00224065.1986.11978984>,
    Chan, Cheng & Spiring (1988) <doi:10.1080/00224065.1988.11979102>,
    Pearn, Kotz & Johnson (1992) <doi:10.1080/00224065.1992.11979403>,
    Kotz & Johnson (2002) <doi:10.1080/00224065.2002.11980119>,
    Montgomery (2020, ISBN:978-1-119-39930-8), Juran (1974, ISBN:978-0-07-033176-1),
    Harry & Schroeder (2000, ISBN:978-0-385-49437-2), and the AIAG SPC Reference
    Manual (2005, ISBN:978-1-60534-026-3).