* Quantify the Robustness of Causal Inferences* (

**konfound**)

Statistical methods that quantify the conditions necessary to alter inferences, also known as sensitivity analysis, are becoming increasingly important to a variety of quantitative sciences. A series of recent works, including Frank (2000) <doi:10.1177/0049124100029002001> and Frank et al. (2013) <doi:10.3102/0162373713493129> extend previous sensitivity analyses by considering the characteristics of omitted variables or unobserved cases that would change an inference if such variables or cases were observed. These analyses generate statements such as ‘an omitted variable would have to be correlated at xx with the predictor of interest (e.g., treatment) and outcome to invalidate an inference of a treatment effect’. Or ‘one would have to replace pp percent of the observed data with null hypothesis cases to invalidate the inference’. We implement these recent developments of sensitivity analysis and provide modules to calculate these two robustness indices and generate such statements in R. In particular, the functions konfound(), pkonfound() and mkonfound() allow users to calculate the robustness of inferences for a user’s own model, a single published study and multiple studies respectively.

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**Weibull Analysis for Reliability Engineering****WeibullR**)

Life data analysis in the graphical tradition of Waloddi Weibull. Methods derived from Robert B. Abernethy (2008, ISBN 0-965306-3-2), Wayne Nelson (1982, ISBN: 9780471094586) <DOI:10.1002/0471725234>, William Q. Meeker and Lois A. Escobar (1998, ISBN: 1-471-14328-6), John I. McCool, (2012, ISBN: 9781118217986) <DOI:10.1002/9781118351994>.

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**Find Optimal Sampling Locations Based on Spatial Covariate(s)****SurfaceTortoise**)

Create sampling designs using the surface reconstruction algorithm. Original method by: Olsson, D. 2002. A method to optimize soil sampling from ancillary data. Poster presenterad at: NJF seminar no. 336, Implementation of Precision Farming in Practical Agriculture, 10-12 June 2002, Skara, Sweden.

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**Compressive Big Data Analytics****CBDA**)

Classification performed on Big Data. It uses concepts from compressive sensing, and implements ensemble predictor (i.e., ‘SuperLearner’) and knockoff filtering as the main machine learning and feature mining engines.

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**Stochastic Integrating****SI**)

An implementation of four stochastic methods of integrating in R, including: 1. Stochastic Point Method (or Monte Carlo Method); 2. Mean Value Method; 3. Important Sampling Method; 4. Stratified Sampling Method. It can be used to estimate one-dimension or multi-dimension integration by Monte Carlo methods. And the estimated variance (precision) is given. Reference: Caflisch, R. E. (1998) <doi:10.1017/S0962492900002804>.