Convert irregularly spaced longitudinal data into regular intervals for further analysis, 
             and perform clustering using advanced machine learning techniques. 
             The package is designed for handling complex longitudinal datasets, 
             optimizing them for research in healthcare, demography, and other fields 
             requiring temporal data modeling.

It performs interlaboratory studies (ILS) to detect those
    laboratories that provide non-consistent results when comparing to others. It
    permits to work simultaneously with various testing materials, from standard
    univariate, and functional data analysis (FDA) perspectives. The univariate
    approach based on ASTM E691-08 consist of estimating the Mandel's h and
    k statistics to identify those laboratories that provide more significant
    different results, testing also the presence of outliers by Cochran and Grubbs
    tests, Analysis of variance (ANOVA) techniques are provided (F and Tuckey
    tests) to test differences in means corresponding to different laboratories per
    each material. Taking into account the functional nature of data retrieved in
    analytical chemistry, applied physics and engineering (spectra, thermograms,
    etc.). ILS package provides a FDA approach for finding the Mandel's k and h
    statistics distribution by smoothing bootstrap resampling.

Merges and downloads 'SPSS' data from different International Large-Scale Assessments (ILSA), including: Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), and other studies from <https://www.iea.nl/>.

Calculates point estimates and standard errors using replicate weights and plausible values for International Large-Scale Assessments (ILSA), including: means, proportions, quantiles, correlations, singlelevel regressions, and multilevel regressions.

In view of the analysis of the structural characteristics of the tripartite 
    network has been complete, however, there is still a lack of a unified 
    operation that can quickly obtain the corresponding characteristics of the 
    tripartite network. 
    To solve this insufficiency, 'ILSM' was designed for supporting calculating 
    such metrics of tripartite networks by functions of this R package.