Contains tools for survey statistics (especially in educational
    assessment) for datasets with replication designs (jackknife, 
    bootstrap, replicate weights; see Kolenikov, 2010;
    Pfefferman & Rao, 2009a, 2009b, <doi:10.1016/S0169-7161(09)70003-3>,
    <doi:10.1016/S0169-7161(09)70037-9>); Shao, 1996, 
    <doi:10.1080/02331889708802523>). 
    Descriptive statistics, linear and logistic regression, 
    path models for manifest variables with measurement error 
    correction and two-level hierarchical regressions for weighted 
    samples are included. Statistical inference can be conducted for 
    multiply imputed datasets and nested multiply imputed datasets
    and is in particularly suited for the analysis of plausible values
    (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; 
    Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham &
    Yanagida, 2016). The package development was supported by BIFIE 
    (Federal Institute for Educational Research, Innovation and Development 
    of the Austrian School System; Salzburg, Austria).

Methods for detecting and visualizing cladogenic shifts in multivariate trait data on phylogenies. Implements penalized-likelihood multivariate generalized least squares models, enabling analyses of high-dimensional trait datasets and large trees via searchOptimalConfiguration(). Includes a greedy step-wise shift-search algorithm following approaches developed in Smith et al. (2023) <doi:10.1111/nph.19099> and Berv et al. (2024) <doi:10.1126/sciadv.adp0114>. Methods build on multivariate GLS approaches described in Clavel et al. (2019) <doi:10.1093/sysbio/syy045> and implemented in the mvgls() function from the 'mvMORPH' package. Documentation and vignettes are available at <https://jakeberv.com/bifrost/>, including worked examples for the jaw-shape dataset.

Estimation of bifurcating autoregressive models of any order, p, BAR(p) as well as several types of bias correction for the least squares estimators of the autoregressive parameters as described in Zhou and Basawa (2005) <doi:10.1016/j.spl.2005.04.024> and Elbayoumi and Mostafa (2020) <doi:10.1002/sta4.342>. Currently, the bias correction methods supported include bootstrap (single, double and fast-double) bias correction and linear-bias-function-based bias correction. Functions for generating and plotting bifurcating autoregressive data from any BAR(p) model are also included. This new version includes calculating several type of bias-corrected and -uncorrected confidence intervals for the least squares estimators of the autoregressive parameters as described in Elbayoumi and Mostafa (2023)
             <doi:10.6339/23-JDS1092>.