Implements Collective And Point Anomaly (CAPA) Fisch, Eckley, and Fearnhead (2022) <doi:10.1002/sam.11586>, Multi-Variate Collective And Point Anomaly (MVCAPA) Fisch, Eckley, and Fearnhead (2021) <doi:10.1080/10618600.2021.1987257>, Proportion Adaptive Segment Selection (PASS) Jeng, Cai, and Li (2012) <doi:10.1093/biomet/ass059>, and Bayesian Abnormal Region Detector (BARD) Bardwell and Fearnhead (2015) <doi:10.1214/16-BA998>. These methods are for the detection of anomalies in time series data. Further information regarding the use of this package along with detailed examples can be found in Fisch, Grose, Eckley, Fearnhead, and Bardwell (2024) <doi:10.18637/jss.v110.i01>.

Compute an anomaly score for multivariate time series based on the k-nearest neighbors algorithm. Different computations of distances between time series are provided.

Provides functions to estimate and interpret the alpha-NOMINATE ideal point model developed in Carroll et al. (2013, <doi:10.1111/ajps.12029>). alpha-NOMINATE extends traditional spatial voting frameworks by allowing for a mixture of Gaussian and quadratic utility functions, providing flexibility in modeling political actors' preferences. The package uses Markov Chain Monte Carlo (MCMC) methods for parameter estimation, supporting robust inference about individuals' ideological positions and the shape of their utility functions. It also contains functions to simulate data from the model and to calculate the probability of a vote passing given the ideal points of the legislators/voters and the estimated location of the choice alternatives.

Analysis of moderation (ANOMO) method conceptualizes
    the difference and equivalence tests as a moderation problem to 
    test the difference and equivalence of 
    two means (or two effects in two studies).

Analyses of Proportions can be performed on the Anscombe (arcsine-related) transformed  
    data. The 'ANOPA' package can analyze proportions obtained from up to 
    four factors. The factors can be within-subject or between-subject or a mix 
    of within- and between-subject. The main, omnibus analysis can be followed by
    additive decompositions into interaction effects, main effects, simple 
    effects, contrast effects, etc., mimicking precisely the logic of ANOVA. For
    that reason, we call this set of tools 'ANOPA' (Analysis of Proportion
    using Anscombe transform) to highlight its similarities with ANOVA. 
    The 'ANOPA' framework also allows plots of proportions easy to obtain 
    along with confidence intervals. Finally, effect sizes and planning statistical 
    power are easily done under this framework. Only particularity, the 'ANOPA' computes  
    F statistics which have an infinite degree of freedom on the denominator. 
    See Laurencelle and Cousineau (2023) <doi:10.3389/fpsyg.2022.1045436>.