Provides S3 calibrators, metrics, and diagnostics for binary and
    multiclass probability calibration. Binary methods include Platt
    scaling, temperature scaling, beta calibration, histogram binning, and
    isotonic regression. Multiclass methods include temperature scaling, vector
    scaling, Dirichlet calibration, and a one-vs-rest wrapper for the binary
    calibrators. A calibration-inference layer adds debiased calibration errors,
    bootstrap confidence intervals, and a kernel calibration hypothesis test for
    binary and multiclass predictions, including the strong (canonical)
    multiclass case. Methods follow Platt (1999, ISBN:9780262194488), Zadrozny
    and Elkan (2002) <doi:10.1145/775047.775151>, Guo et al. (2017)
    <https://proceedings.mlr.press/v70/guo17a.html>, Kull et al. (2017)
    <doi:10.1214/17-EJS1338SI>, Kull et al. (2019)
    <doi:10.48550/arXiv.1910.12656>, Widmann et al. (2019)
    <doi:10.48550/arXiv.1910.11385>, and Kumar et al. (2019)
    <doi:10.48550/arXiv.1909.10155>.

Implements an efficient and powerful Bayesian approach for sparse high-dimensional linear regression. It uses minimal prior assumptions on the parameters through plug-in empirical Bayes estimates of hyperparameters. An efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The E-step is motivated by the popular two-group approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear regression, implemented using one-at-a-time or all-at-once type optimization. More information can be found in McLain, Zgodic, and Bondell (2022) <arXiv:2209.08139>.

Hybridization probes for target sequences can be made based on melting temperature value calculated by R package 'TmCalculator' <https://CRAN.R-project.org/package=TmCalculator> and methods extended from Beliveau, B. J.,(2018) <doi:10.1073/pnas.1714530115>, and those hybridization probes can be used to capture specific target regions in fluorescence in situ hybridization and next generation sequence experiments.