quend

#!/bin/bash
#!/usr/bin/env bash
# Run this script in the main directory of openssl.
# Requires python3


set -e

# download necessary parsing script
wget https://gist.githubusercontent.com/quend/e44520133e2cd8a6da98ce1c2a02a7b1/raw/8c93794dc2dc6cdd84255eb2de8416b59cf53a9b/comp_db_generate.py

joshwatson

'''
bookmarks.py - Create/List bookmarks in Binary Ninja

Copyright (c) 2016 Josh Watson

Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the "Software"),
to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the

solve_test.py

#!/usr/bin/env python

import angr

p = angr.Project('test')

key_str = angr.StringSpec(sym_length=20)
initial_state = p.factory.entry_state(args=['./test', key_str])
pg = p.factory.path_group(initial_state)

steipete

#!/bin/bash

# This script downlaods and builds the iOS and Mac openSSL libraries with Bitcode enabled

# Credits:
# https://github.com/st3fan/ios-openssl
# https://github.com/x2on/OpenSSL-for-iPhone/blob/master/build-libssl.sh
# Peter Steinberger, PSPDFKit GmbH, @steipete.

set -e

tomhopper

#' @title PRESS
#' @author Thomas Hopper
#' @description Returns the PRESS statistic (predictive residual sum of squares).
#'              Useful for evaluating predictive power of regression models.
#' @param linear.model A linear regression model (class 'lm'). Required.
#' 
PRESS <- function(linear.model) {
  #' calculate the predictive residuals
  pr <- residuals(linear.model)/(1-lm.influence(linear.model)$hat)
  #' calculate the PRESS