Offers a set of tools for visualizing and analyzing size and power properties of the test for equal predictive accuracy, the Diebold-Mariano test that is based on heteroskedasticity and autocorrelation-robust (HAR) inference. A typical HAR inference is involved with non-parametric estimation of the long-run variance, and one of its tuning parameters, the truncation parameter, trades off a size and power. Lazarus, Lewis, and Stock (2021)<doi:10.3982/ECTA15404> theoretically characterize the size-power frontier for the Gaussian multivariate location model. 'ForeComp' computes and visualizes the finite-sample size-power frontier of the Diebold-Mariano test based on fixed-b asymptotics together with the Bartlett kernel. To compute the finite-sample size and power, it works with the best approximating ARMA process to the given dataset. It informs the user how their choice of the truncation parameter performs and how robust the testing outcomes are.

Routines for forecasting univariate time series using Theta Models.

Reading and writing data stored by some versions of
	'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka',
	and for reading and writing some 'dBase' files.

Computes likelihood ratios based on pigmentation traits. Also, it allows computing conditional probabilities for unidentified individuals based on missing person characteristics. A set of tailored plots are incorporated to analyze likelihood ratio distributions.

The 'forensIT' package is a comprehensive statistical toolkit tailored for handling missing person cases. By leveraging information theory metrics, it enables accurate assessment of kinship, particularly when limited genetic evidence is available. With a focus on optimizing statistical power, 'forensIT' empowers investigators to effectively prioritize family members, enhancing the reliability and efficiency of missing person investigations.

Include assessing site classes based on the stand height growth and establishing a nonlinear mixed-effect biomass model under different site classes based on the whole stand model to achieve more accurate estimation of carbon sequestration. In particular, a carbon sequestration potential productivity calculation method based on the potential mean annual increment is proposed. This package is applicable to both natural forests and plantations. It can quantitatively assess stand’s potential productivity, realized productivity, and possible improvement under certain site, and can be used in many aspects such as site quality assessment, tree species suitability evaluation, and forest degradation evaluation. Reference: Lei X, Fu L, Li H, et al (2018) <doi:10.11707/j.1001-7488.20181213>. Fu L, Sharma R P, Zhu G, et al (2017) <doi:10.3390/f8040119>.