A set of functions for obtaining positional parameters and magnitude difference between components of binary and multiple stellar systems from series of speckle images.

Provides utilities for conducting specification curve analyses (Simonsohn, Simmons & Nelson (2020, <doi: 10.1038/s41562-020-0912-z>) or multiverse analyses (Steegen, Tuerlinckx, Gelman & Vanpaemel, 2016, <doi: 10.1177/1745691616658637>) including functions to setup, run, evaluate, and plot all specifications.

Implementation of SPECS, your favourite Single-Equation Penalized Error-Correction Selector developed in
    Smeekes and Wijler (2021) <doi:10.1016/j.jeconom.2020.07.021>. SPECS provides a fully automated estimation procedure for large and potentially
    (co)integrated datasets. The dataset in levels is converted to a conditional error-correction model, either by the user or
    by means of the functions included in this package, and various specialised forms of penalized regression can be applied to
    the model. Automated options for initializing and selecting a sequence of penalties, as well as the construction of penalty
    weights via an initial estimator, are available. Moreover, the user may choose from a number of pre-specified deterministic
    configurations to further simplify the model building process.

A collection of forecast verification routines developed for the SPECS
    FP7 project. The emphasis is on comparative verification of ensemble forecasts of weather and climate.

A tool for survival analysis using a discrete time approach with ensemble binary classification. 'spect' provides a
	simple interface consistent with commonly used R data analysis packages, such as 'caret', a variety of parameter options
	to help facilitate search automation, a high degree of transparency to the end-user - all intermediate data sets and
	parameters are made available for further analysis and useful, out-of-the-box visualizations of model performance. Methods
	for transforming survival data into discrete-time are adapted from the 'autosurv' package by Suresh et al., (2022)
	<doi:10.1186/s12874-022-01679-6>.