Showing 43 of total 43 results (show query)

jomulder

BFpack:Flexible Bayes Factor Testing of Scientific Expectations

Implementation of default Bayes factors for testing statistical hypotheses under various statistical models. 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, correlation analysis, generalized linear models, special cases of linear mixed models, survival models, relational event models. Parameters that can be tested are location parameters (e.g., group means, regression coefficients), variances (e.g., group variances), and measures of association (e.g,. polychoric/polyserial/biserial/tetrachoric/product moments correlations), among others. The statistical underpinnings are described in O'Hagan (1995) <DOI:10.1111/j.2517-6161.1995.tb02017.x>, De Santis and Spezzaferri (2001) <DOI:10.1016/S0378-3758(00)00240-8>, Mulder and Xin (2022) <DOI:10.1080/00273171.2021.1904809>, Mulder and Gelissen (2019) <DOI:10.1080/02664763.2021.1992360>, 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 (2017) <DOI:10.1037/met0000116>, Hoijtink, Mulder, van Lissa, and Gu (2018) <DOI:10.1037/met0000201>, 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>. When using the packages, please refer to the package Mulder et al. (2021) <DOI:10.18637/jss.v100.i18> and the relevant methodological papers.

Maintained by Joris Mulder. Last updated 1 months ago.

fortranopenblas

1.5 match 15 stars 8.24 score 55 scripts 3 dependents

dzangri

stmgui:Shiny Application for Creating STM Models

Provides an application that acts as a GUI for the 'stm' text analysis package.

Maintained by Dan Zangri. Last updated 8 years ago.

1.7 match 5 stars 3.40 score 4 scripts

cran

Compositional:Compositional Data Analysis

Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. We further include functions for percentages (or proportions). The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include: a) Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. <doi:10.48550/arXiv.1106.1451> b) Tsagris M. (2014). "The k-NN algorithm for compositional data: a revised approach with and without zero values present". Journal of Data Science, 12(3): 519--534. <doi:10.6339/JDS.201407_12(3).0008>. c) Tsagris M. (2015). "A novel, divergence based, regression for compositional data". Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. <doi:10.48550/arXiv.1511.07600>. d) Tsagris M. (2015). "Regression analysis with compositional data containing zero values". Chilean Journal of Statistics, 6(2): 47--57. <https://soche.cl/chjs/volumes/06/02/Tsagris(2015).pdf>. e) Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved supervised classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. <doi:10.1007/s00357-016-9207-5>. f) Tsagris M., Preston S. and Wood A.T.A. (2017). "Nonparametric hypothesis testing for equality of means on the simplex". Journal of Statistical Computation and Simulation, 87(2): 406--422. <doi:10.1080/00949655.2016.1216554>. g) Tsagris M. and Stewart C. (2018). "A Dirichlet regression model for compositional data with zeros". Lobachevskii Journal of Mathematics, 39(3): 398--412. <doi:10.1134/S1995080218030198>. h) Alenazi A. (2019). "Regression for compositional data with compositional data as predictor variables with or without zero values". Journal of Data Science, 17(1): 219--238. <doi:10.6339/JDS.201901_17(1).0010>. i) Tsagris M. and Stewart C. (2020). "A folded model for compositional data analysis". Australian and New Zealand Journal of Statistics, 62(2): 249--277. <doi:10.1111/anzs.12289>. j) Alenazi A.A. (2022). "f-divergence regression models for compositional data". Pakistan Journal of Statistics and Operation Research, 18(4): 867--882. <doi:10.18187/pjsor.v18i4.3969>. k) Tsagris M. and Stewart C. (2022). "A Review of Flexible Transformations for Modeling Compositional Data". In Advances and Innovations in Statistics and Data Science, pp. 225--234. <doi:10.1007/978-3-031-08329-7_10>. l) Alenazi A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. <doi:10.1080/03610926.2021.2014890>. m) Tsagris M., Alenazi A. and Stewart C. (2023). "Flexible non-parametric regression models for compositional response data with zeros". Statistics and Computing, 33(106). <doi:10.1007/s11222-023-10277-5>. n) Tsagris. M. (2025). "Constrained least squares simplicial-simplicial regression". Statistics and Computing, 35(27). <doi:10.1007/s11222-024-10560-z>. o) Sevinc V. and Tsagris. M. (2024). "Energy Based Equality of Distributions Testing for Compositional Data". <doi:10.48550/arXiv.2412.05199>.

Maintained by Michail Tsagris. Last updated 2 months ago.

0.9 match 3 stars 3.64 score 4 dependents