Bounded continuous data are encountered in many areas of test application. 
 Examples include visual analogue scales used in the measurement of personality, mood, 
 depression, and quality of life; item response times from tests with item deadlines; 
 confidence ratings; and pain intensity ratings. Using this package, item response theory (IRT) 
 models suitable for bounded continuous item scores can be fitted to data within a Bayesian framework. 
 The package draws on posterior sampling facilities provided by R-package 'rstan' (Stan Development Team, 2025)<https://mc-stan.org/>. 
 Available models include the Beta IRT model by Noel and Dauvier (2007)<doi:10.1177/0146621605287691>, the continuous response 
 model by Samejima (1973)<doi:10.1007/BF03372160>, the unbounded normal model by Mellenbergh (1994)<doi:10.1207/s15327906mbr2903_2>, 
 and the Simplex IRT model by Flores et al. (2020)<doi:10.1007/978-3-030-43469-4_8>. All models can be 
 fitted with or without zero-one inflation (Molenaar et al., 2022)<doi:10.3102/10769986221108455>. 
 Model fit comparisons can be conducted using the Watanabe-Akaike information criterion (WAIC), leave-one-out 
 cross-validation information citerion (LOOIC) and the fully marginalized likelihood (i.e., Bayes factors).

A collection of R functions were implemented
        from published and available analytic solutions for the
        One-Dimensional Boussinesq Equation (ground-water). In
        particular, the function "beq.lin()" is the analytic solution of
        the linearized form of Boussinesq Equation between two
        different head-based boundary (Dirichlet) conditions;
        "beq.song" is the non-linear power-series analytic solution of
        the motion of a wetting front over a dry bedrock (Song at al,
        2007, see complete reference on function documentation).
        Bugs/comments/questions/collaboration of any kind are warmly
        welcomed.

Computational tools for outlier detection and influence diagnostics in meta-analysis (Noma et al. (2025) <doi:10.1101/2025.09.18.25336125>). Bootstrap distributions of influence statistics are computed, and explicit thresholds for identifying outliers are provided. These methods can also be applied to the analysis of influential centers or regions in multicenter or multiregional clinical trials (Aoki, Noma and Gosho (2021) <doi:10.1080/24709360.2021.1921944>, Nakamura and Noma (2021) <doi:10.5691/jjb.41.117>).