Calculates total survey error (TSE) for one or more surveys, using both scale-dependent and scale-independent metrics.  Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTIND and its documentation), properly input column names (see functions documentation), and run your functions.  For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc.

Implements BCH (Bolck-Croon-Hagenaars) <doi:10.1093/pan/mph001> and ML (Vermunt's maximum likelihood) <doi:10.1093/pan/mpq025> approaches for three-step estimation of latent class models with covariates and distal outcomes, following Bakk, Tekle & Vermunt (2013) <doi:10.1177/0081175012470644>, Bakk, Oberski & Vermunt (2014) <https://www.jstor.org/stable/24573086>, and Bakk & Kuha (2018) <doi:10.1007/s11336-017-9592-7>. Built on 'multilevLCA' (Lyrvall et al., 2025) <doi:10.1080/00273171.2025.2473935> for Step-1 measurement model estimation, this package extends it with support for Gaussian, Poisson, and binomial distal outcome families. Unlike 'poLCA', which relies on one-step estimation and cannot accommodate a measurement model from a different sample, this package uses a stepwise approach to prevent the structural model from influencing latent class formation. Implements correct sandwich variance estimation that propagates measurement uncertainty from the first-step through classification-error correction in the final step (Bakk, Oberski & Vermunt, 2014). Supports polytomous items and missing data in the measurement model with full information maximum likelihood. A data-generating process replicating the Bakk & Kuha (2018) simulation study is included.

A framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions 'predict()' and 'forecast()' to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as 'update_weights()' or 'update_base_models()'. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. "Arbitrated Ensemble for Time Series Forecasting." to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: "Arbitrated Ensemble for Solar Radiation Forecasting." International Work-Conference on Artificial Neural Networks. Springer, 2017 <doi:10.1007/978-3-319-59153-7_62>.