Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2024) <doi:10.1016/j.cam.2023.115379>; Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).

Singular spectrum analysis (SSA) decomposes a time series into interpretable components like trends, oscillations, and noise without strict distributional and structural assumptions. For method details see Golyandina N, Zhigljavsky A (2013). <doi:10.1007/978-3-642-34913-3>.

A set of functions to calculate sample size for two-sample difference in means tests. Does adjustments for either nonadherence or variability that comes from using data to estimate parameters.

Computes the trimmed-k mean by removing the k smallest and k largest values from a numeric vector. Created for STAT 5400 at the University of Iowa.

Construction, diagnostics, estimation, exposure-robust inference
    and publication-ready visualisation for shift-share (Bartik) instrumental
    variable designs, in one consistent workflow. Organised around the two
    identification routes of the modern literature. The exogenous share
    approach refers to Goldsmith-Pinkham, Sorkin and Swift (2020) <doi:10.1257/aer.20181047>
    and the exogenous shift approach refers to Borusyak, Hull and
    Jaravel (2022) <doi:10.1093/restud/rdab030> and Adao, Kolesar and Morales
    (2019) <doi:10.1093/qje/qjz025>. Wraps
    'ShiftShareSE' for exposure-robust inference when available.

Includes general data manipulation functions, algorithms for statistical disclosure control (Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6> and functions for hierarchical computations by sparse model matrices (Langsrud, 2023) <doi:10.32614/RJ-2023-088>.

Provides a collection of self-labeled techniques for semi-supervised 
    classification. In semi-supervised classification, both labeled and unlabeled
    data are used to train a classifier. This learning paradigm has obtained promising
    results, specifically in the presence of a reduced set of labeled examples. 
    This package implements a collection of self-labeled techniques to construct a
    classification model. This family of techniques enlarges the original labeled set 
	using the most confident predictions to classify unlabeled data. The techniques 
	implemented can be applied to classification problems in several domains by the 
	specification of a supervised base classifier. At low ratios of labeled data, it 
	can be shown to perform better than classical supervised classifiers.