Compute bending energies, principal warps, partial warp scores, and the non-affine component of shape variation for 2D landmark configurations, as well as Mardia-Dryden distributions and self-similar distributions of landmarks, as described in Mitteroecker et al. (2020) <doi:10.1093/sysbio/syaa007>. Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) <doi:10.5061/dryad.j6q573n8s> and Grunstra et al. (2020) <doi:10.5061/dryad.zkh189373>.

Data sets and functions used in the polish book 
    "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). 
    See more at <http://biecek.pl/R>. Among others you will find here 
    data about housing prices, cancer patients, running times and many others.

List, query and manipulate all system processes, on
    'Windows', 'Linux' and 'macOS'.

It is often advantageous to test a hypothesis more than once
    in the context of propensity score analysis (Rosenbaum, 2012)
    <doi:10.1093/biomet/ass032>. The functions in this package facilitate
    bootstrapping for propensity score analysis (PSA). By default,
    bootstrapping using two classification tree methods (using 'rpart' and
    'ctree' functions), two matching methods (using 'Matching' and
    'MatchIt' packages), and stratification with logistic regression.  A
    framework is described for users to implement additional propensity
    score methods.  Visualizations are emphasized for diagnosing balance;
    exploring the correlation relationships between bootstrap samples and
    methods; and to summarize results.

A collection of functions that primarily produce graphics to
    aid in a Propensity Score Analysis (PSA).  Functions include: cat.psa
    and box.psa to test balance within strata of categorical and
    quantitative covariates, circ.psa for a representation of the
    estimated effect size by stratum, loess.psa that provides a graphic
    and loess based effect size estimate, and various balance functions
    that provide measures of the balance achieved via a PSA in a
    categorical covariate.