An Implementation of Parametric and Nonparametric Event Study (estudy2)
An implementation of a most commonly used event study methodology, including both parametric and nonparametric tests. It contains variety aspects of the rate of return estimation (the core calculation is done in C++), as well as three classical for event study market models: mean adjusted returns, market adjusted returns and single-index market models. There are 6 parametric and 6 nonparametric tests provided, which examine cross-sectional daily abnormal return (see the documentation of the functions for more information). Parametric tests include tests proposed by Brown and Warner (1980) <DOI:10.1016/0304-405X(80)90002-1>, Brown and Warner (1985) <DOI:10.1016/0304-405X(85)90042-X>, Boehmer et al. (1991) <DOI:10.1016/0304-405X(91)90032-F>, Patell (1976) <DOI:10.2307/2490543>, and Lamb (1995) <DOI:10.2307/253695>. Nonparametric tests covered in estudy2 are tests described in Corrado and Zivney (1992) <DOI:10.2307/2331331>, McConnell and Muscarella (1985) <DOI:10.1016/0304-405X(85)90006-6>, Boehmer et al. (1991) <DOI:10.1016/0304-405X(91)90032-F>, Cowan (1992) <DOI:10.1007/BF00939016>, Corrado (1989) <DOI:10.1016/0304-405X(89)90064-0>, Campbell and Wasley (1993) <DOI:10.1016/0304-405X(93)90025-7>, Savickas (2003) <DOI:10.1111/1475-6803.00052>, Kolari and Pynnonen (2010) <DOI:10.1093/rfs/hhq072>. Furthermore, tests for the cumulative abnormal returns proposed by Brown and Warner (1985) <DOI:10.1016/0304-405X(85)90042-X> and Lamb (1995) <DOI:10.2307/253695> are included.

Approximate False Positive Rate Control in Selection Frequency for Random Forest (forestControl)
Approximate false positive rate control in selection frequency for random forest using the methods described by Ender Konukoglu and Melanie Ganz (2015) <arXiv:1410.2838>. Methods for calculating the selection frequency threshold at false positive rates and selection frequency false positive rate feature selection.

Correlation-Adjusted Regression Survival (CARS) Scores (carSurv)
Contains functions to estimate the Correlation-Adjusted Regression Survival (CARS) Scores. The method is described in Welchowski, T. and Zuber, V. and Schmid, M., (2018), Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection, <arXiv:1802.08178>.

Judd & McClelland Formatting for ANOVA Output (supernova)
Produces ANOVA tables in the format used by Judd, McClelland, and Ryan (2017, ISBN:978-1138819832) in their introductory textbook, Data Analysis. This includes proportional reduction in error and formatting to improve ease the transition between the book and R.

Fast Gaussian Process Computation Using Vecchia’s Approximation (GpGp)
Functions for reordering input locations, finding ordered nearest neighbors (with help from ‘FNN’ package), grouping operations, approximate likelihood evaluations, profile likelihoods, Gaussian process predictions, and conditional simulations. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. The original approximation is due to Vecchia (1988) <http://…/2345768>, and the reordering and grouping methods are from Guinness (2018) <doi:10.1080/00401706.2018.1437476>.

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