Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) <doi:10.1214/21-AOAS1466>.

Density, distribution function, quantile function 
	and random generation for the sum of independent non-identical
	binomial distribution with parameters \code{size} and \code{prob}.

Implements an approach aimed at assessing the accuracy and
    effectiveness of raw scores obtained in scales that contain locally dependent
    items. The program uses as input the calibration (structural) item estimates
    obtained from fitting extended unidimensional factor-analytic solutions in
    which the existing local dependencies are included. Measures of reliability
    (Omega) and information are proposed at three levels: (a) total score, (b)
    bivariate-doublet, and (c) item-by-item deletion, and  are compared to those
    that would be obtained if all the items had been locally independent. All the
    implemented procedures can be obtained from: (a) linear factor-analytic
    solutions in which the item scores are treated as approximately continuous,
    and (b) non-linear solutions in which the item scores are treated as
    ordered-categorical. A detailed guide can be obtained at the following url.

Provides access to packages developed for downloading, reading and analyzing
  microdata from household surveys in Integrated System of Household Surveys - SIPD
  conducted by Brazilian Institute of Geography and Statistics - IBGE.
	More information can be obtained from the official website <https://www.ibge.gov.br/>.

Processor for selected ion flow tube mass spectrometer
            (SIFT-MS) output file from breath analysis. It allows the filtering of the SIFT output file (i.e., variation
            over time of the target analyte concentration) and the following
            analysis for the determination of: maximum, average, and standard deviation
            value of target concentration measured at each exhalation, and the
            respiratory rate over the measurement. Additionally, it is possible to
            align the SIFT-MS data with other on-line techniques such as 
            cardio pulmonary exercise test (CPET) for a comprehensive 
            characterization of breath samples.