A portfolio of tools for economic complexity analysis and
    industrial upgrading navigation. The package implements essential measures
    in international trade and development economics, including the relative
    comparative advantage (RCA), economic complexity index (ECI) and product
    complexity index (PCI). It enables users to analyze export structures,
    explore product relatedness, and identify potential upgrading paths grounded
    in economic theory, following the framework in Hausmann et al. (2014)
    <doi:10.7551/mitpress/9647.001.0001>.

Computes partial eta-squared effect sizes for fixed effects in
    linear mixed models fitted with the 'lme4' package. Supports crossed,
    nested, and mixed (crossed-and-nested) random effects structures with any
    number of grouping factors. Mixed designs handle cases where grouping
    factors are simultaneously crossed with some variables and nested within
    others (e.g., photos nested within models, but both crossed with
    participants). Factor predictors are supported directly, and a single
    factor-level (omnibus) effect size can be obtained for a multi-level factor
    or multi-df interaction. Random slope variances are translated to the
    outcome scale using a variance decomposition approach, correctly accounting
    for predictor scaling and interaction terms. Both general and operative
    effect sizes are provided, with optional parametric bootstrap confidence
    intervals. For correlated predictors, per-predictor effect sizes use unique
    (semipartial) variance by default. Methods are based on Correll, Mellinger, McClelland, and Judd
    (2020) <doi:10.1016/j.tics.2019.12.009>, Correll, Mellinger, and Pedersen
    (2022) <doi:10.3758/s13428-021-01687-2>, and Rights and Sterba (2019)
    <doi:10.1037/met0000184>.

It provides functions to perform permutation conditional random one-sample and two-samples t-tests in a multivariate framework.

Implements entrywise splitting cross-validation (ECV) and its
    penalized variant (pECV) for selecting the number of factors in
    generalized factor models.