Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to <https://github.com/psychmeta/psychmeta/issues> or <issues@psychmeta.com>.

Provides clean-room implementations for estimating psychometric
    network models, including correlation and partial-correlation networks,
    Gaussian graphical models with extended Bayesian information criterion
    (EBIC) regularization, nonparanormal and stepwise selection variants,
    information-filtering networks (the triangulated maximally filtered graph
    and the local-global inverse covariance), relative-importance networks,
    and Ising and mixed graphical models
    <doi:10.3758/s13428-017-0862-1> <doi:10.1007/978-3-031-54464-4_19>. All
    methods are implemented from first principles in base R without compiled
    dependencies and return consistent, tidy outputs. Functions are designed
    to be transparent and report optimization diagnostics where applicable. For
    Gaussian graphical models, the graphical lasso stationarity
    (Karush-Kuhn-Tucker) residual quantifies the deviation of the estimated
    solution from the optimum of the corresponding convex optimization problem.

The main goal of the psycho package is to provide tools for psychologists, neuropsychologists and neuroscientists, 
   to facilitate and speed up the time spent on data analysis. It aims at supporting best practices and tools to format the output 
   of statistical methods to directly paste them into a manuscript, ensuring statistical reporting standardization and conformity.

Automates common psychometric workflows for applied researchers,
    including item descriptives, inter-item correlations, exploratory and
    confirmatory factor analysis, reliability, multi-group measurement
    invariance, and alignment optimization. Decision heuristics are informed by
    procedures such as parallel analysis (Horn, 1965,
    <doi:10.1007/BF02289447>), multivariate normality diagnostics (Mardia,
    1970, <doi:10.1093/biomet/57.3.519>), measurement-invariance fit-change
    rules (Chen, 2007, <doi:10.1080/10705510701301834>), and alignment
    optimization (Asparouhov and Muthen, 2014,
    <doi:10.1080/10705511.2014.919210>), among others. Results can be returned as structured
    R objects and exported as bilingual reports for transparent analytical
    documentation.