Locally Gaussian Distributions: Estimation and Methods (lg)
An implementation of locally Gaussian distributions. It provides methods for implementing the locally Gaussian density estimator (LGDE) by Otneim and Tjøstheim (2017a) <doi:10.1007/s11222-016-9706-6>, as well as the corresponding estimator for conditional density functions by Otneim and Tjøstheim (2017b) <doi:10.1007/s11222-017-9732-z>.

Tukey Region and Median (TukeyRegion)
Tukey regions are polytopes in the Euclidean space, viz. upper-level sets of the Tukey depth function on given data. The bordering hyperplanes of a Tukey region are computed as well as its vertices, facets, centroid, and volume. In addition, the Tukey median set, which is the non-empty Tukey region having highest depth level, and its barycenter (= Tukey median) are calculated. Tukey regions are visualized in dimension two and three. For details see Liu, Mosler, and Mozharovskyi (2017) <arXiv:1412.5122>.

Multiplier Data Envelopment Analysis and Cross Efficiency (MultiplierDEA)
Functions are provided for calculating efficiency using multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross efficiency using single and two-phase approach. In addition, it includes some datasets for calculating efficiency and cross efficiency.

Computes Revisitation Metrics for Trajectory Data (recurse)
Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. Also includes functions to plot data.

Simulating Neutral Landscape Models (NLMR)
Provides neutral landscape models (Gardner et al. 1987 <doi:10.1007/BF02275262>, With 1997 <doi:10.1046/j.1523-1739.1997.96210.x>) that can easily extend in existing landscape analyses. Neutral landscape models range from ‘hard’ neutral models (only random functions) to ‘soft’ ones (with parameters) and generate landscape patterns that are not grounded in ecological reasoning. Thus, these patterns can be used as null models in landscape ecology. ‘NLMR’ combines a large number of algorithms from published software (Saura & Martínez 2000 <doi:10.1023/A:1008107902848>, Etherington et al. 2015 <doi:10.1111/2041-210X.12308>) for simulating neutral landscapes and includes utility functions to classify and combine the landscapes. The simulation results are obtained in a geospatial data format (raster* objects from the ‘raster’ package) and can, therefore, be used in any sort of raster data operation that is performed with standard observation data.

Health-Economic Simulation Modeling and Decision Analysis (hesim)
Functionality for developing and analyzing the output of health-economic simulation models. Contains random sampling functions for conducting probabilistic sensitivity analyses (Claxton et al. 2005) <doi:10.1002/hec.985> and individual patient simulations (Brennan et al. 2006) <doi:10.1002/hec.1148>. Individualized cost-effectiveness analysis (Basu and Meltzer 2007, Ioannidis and Garber 2011) <doi:10.1177/0272989X06297393>, <doi:10.1371/journal.pmed.1001058> can be performed on simulation output and used to summarize a probabilistic sensitivity analysis at the subgroup or individual level. Core functions are written in C++ to facilitate computationally intensive modeling.

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