Jae H Kim
5 packages on CRAN
Bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series
Computational resources for test proposed by Gibbons, Ross, Shanken (1989)<DOI:10.2307/1913625>.
Calculates the optimal level of significance based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. The details are covered in Kim, Jae H. and Choi, In, 2020, Choosing the Level of Significance: A Decision-Theoretic Approach, Abacus. See also Kim, Jae H., 2020, Decision-theoretic hypothesis testing: A primer with R package OptSig, The American Statistician.
Estimation, Hypothesis Testing, Prediction for Stationary Vector Autoregressive Models
A collection of statistical tests for martingale difference hypothesis