* Pedigree-based Relatedness Coefficients* (

**ribd**)

Recursive algorithms for computing various relatedness coefficients, including pairwise kinship, kappa and identity coefficients. Both autosomal and X-linked coefficients are computed. Founders are allowed to be inbred. In addition to the standard pairwise coefficients, ribd also computes a range of lesser-known coefficients, including generalised kinship coefficients (Karigl (1981) <doi:10.1111/j.1469-1809.1981.tb00341.x>; Weeks and Lange (1988) <h…/PMC1715269> ), two-locus coefficients (Thompson (1988) <doi:10.1093/imammb/5.4.261>) and multi-person coefficients. This package is part of the ped suite, a collection of packages for pedigree analysis with ‘pedtools’ as the core package for creating and handling pedigree objects.

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**Project Specific Workspace Organization Templates****cabinets**)

Creates project specific directory and file templates that are written to a .Rprofile file. Upon starting a new R session, these templates can be used to streamline the creation of new directories that are standardized to the user’s preferences.

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**Probability Computations on Pedigrees****pedprobr**)

An implementation of the Elston-Stewart algorithm for calculating pedigree likelihoods given genetic marker data (Elston and Stewart (1971) <doi:10.1159/000152448>). The standard algorithm is extended to allow inbred founders. Mutation modelling is supported by the ‘pedmut’ package. ‘pedprobr’ is part of the ped suite, a collection of packages for pedigree analysis in R, based on ‘pedtools’ for handling pedigrees and markers.

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**Computerized Adaptive Multistage Testing****Rmst**)

Assemble the panels of computerized adaptive multistage testing by the bottom-up and the top-down approach, and simulate the administration of the assembled panels. The full documentation and tutorials are at <https://…/Rmst>. Reference: Luo and Kim (2018) <doi:10.1111/jedm.12174>.

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**Flexible Bayes Factor Testing of Scientific Expectations****BFpack**)

Implementation of various default Bayes factors for testing statistical hypotheses. The package is intended for applied quantitative researchers in the social and behavioral sciences, medical research, and related fields. The Bayes factor tests can be executed for statistical models such as univariate and multivariate normal linear models, generalized linear models, special cases of linear mixed models, survival models, relational event models. Parameters that can be tested are location parameters (e.g., regression coefficients), variances (e.g., group variances), and measures of association (e.g,. bivariate correlations). The statistical underpinnings are described in Mulder, Hoijtink, and Xin (2019) <arXiv:1904.00679>, Mulder and Gelissen (2019) <arXiv:1807.05819>, Mulder (2016) <DOI:10.1016/j.jmp.2014.09.004>, Mulder and Fox (2019) <DOI:10.1214/18-BA1115>, Mulder and Fox (2013) <DOI:10.1007/s11222-011-9295-3>, Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder <DOI:10.1037/met0000116>, Hoijtink, Mulder, van Lissa, and Gu, (2018) <DOI:10.31234/osf.io/v3shc>, Gu, Mulder, and Hoijtink, (2018) <DOI:10.1111/bmsp.12110>, Hoijtink, Gu, and Mulder, (2018) <DOI:10.1111/bmsp.12145>, and Hoijtink, Gu, Mulder, and Rosseel, (2018) <DOI:10.1037/met0000187>.