A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.

A d-statistic tests the null hypothesis of no treatment effect in a matched, nonrandomized study of the effects caused by treatments.  A d-statistic focuses on subsets of matched pairs that demonstrate insensitivity to unmeasured bias in such an observational study, correcting for double-use of the data by conditional inference. This conditional inference can, in favorable circumstances, substantially increase the power of a sensitivity analysis (Rosenbaum (2010) <doi:10.1007/978-1-4419-1213-8_14>).  There are two examples, one concerning unemployment from Lalive et al. (2006) <doi:10.1111/j.1467-937X.2006.00406.x>, the other concerning smoking and periodontal disease from Rosenbaum (2017) <doi:10.1214/17-STS621>.

For an observational study with binary treatment, binary outcome and K strata, implements a d-statistic that uses those strata most insensitive to unmeasured bias in treatment assignment.<doi:10.1093/biomet/asaa032>  The package has one function, dstat2x2xk.

Simultaneously detect the number and locations of change points in piecewise linear models under stationary Gaussian noise allowing autocorrelated random noise. The core idea is to transform the problem of detecting change points into the detection of local extrema (local maxima and local minima)through kernel smoothing and differentiation of the data sequence, see Cheng et al. (2020) <doi:10.1214/20-EJS1751>. A low-computational and fast algorithm call 'dSTEM' is introduced to detect change points based on the 'STEM' algorithm in D. Cheng and A. Schwartzman (2017) <doi:10.1214/16-AOS1458>.

Functions for direct surrogate variable analysis, which can identify hidden factors in high-dimensional biomedical data.

Data science methods used in wind energy applications. 
              Current functionalities include creating a multi-dimensional power curve model, 
              performing power curve function comparison, covariate matching, and energy decomposition. 
              Relevant works for the developed functions are: 
              funGP() - Prakash et al. (2022) <doi:10.1080/00401706.2021.1905073>, 
              AMK() - Lee et al. (2015) <doi:10.1080/01621459.2014.977385>, 
              tempGP() - Prakash et al. (2022) <doi:10.1080/00401706.2022.2069158>, 
              ComparePCurve() - Ding et al. (2021) <doi:10.1016/j.renene.2021.02.136>,
              deltaEnergy() - Latiffianti et al. (2022) <doi:10.1002/we.2722>,
              syncSize() - Latiffianti et al. (2022) <doi:10.1002/we.2722>,
              imptPower() - Latiffianti et al. (2022) <doi:10.1002/we.2722>,
              All other functions - Ding (2019, ISBN:9780429956508).