r-cran-whatif

GNU R evaluate counterfactuals

Inferences about counterfactuals are essential for prediction, answering what if questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based largely on speculation hidden in convenient modeling assumptions that few would be willing to defend. Unfortunately, standard statistical approaches assume the veracity of the model rather than revealing the degree of model-dependence, which makes this problem hard to detect. WhatIf offers easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models. If an analysis fails the tests offered here, then it is known that substantive inferences will be sensitive to at least some modeling choices that are not based on empirical evidence, no matter what method of inference one chooses to use. WhatIf implements the methods for evaluating counterfactuals discussed in Gary King and Langche Zeng, 2006, "The Dangers of Extreme Counterfactuals," Political Analysis 14 (2) <DOI:10.1093/pan/mpj004>; and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The Pitfalls of Counterfactual Inference," International Studies Quarterly 51 (March) <DOI:10.1111/j.1468-2478.2007.00445.x>.

libbotan-2-5

multiplatform crypto library (2.x version)

python-datrie

Super-fast, efficiently stored Trie for Python

libtorch-thnn

libTHNN.so of Neural Network Package for Torch Framework

lua-torch-nn

Neural Network Package for Torch Framework

libmodpbase64-0

collection of high performance c-string transformations

This implementation is frequently 2x faster than standard implementations. The transformations include base64, base16, base85, base2, url and javascript escaping, as well as fast number to string and upper/lower case conversions.