Tung Thanh Le ttungl
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| ## Data analysis project - carry out an IPTW causal analysis | |
| install.packages("tableone") | |
| install.packages("Matching") | |
| install.packages("ipw") | |
| install.packages("survey") | |
| install.packages("MatchIt") | |
| library(tableone) |
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| R version 4.2.0 (2022-04-22) -- "Vigorous Calisthenics" | |
| Copyright (C) 2022 The R Foundation for Statistical Computing | |
| Platform: aarch64-apple-darwin20 (64-bit) | |
| R is free software and comes with ABSOLUTELY NO WARRANTY. | |
| You are welcome to redistribute it under certain conditions. | |
| Type 'license()' or 'licence()' for distribution details. | |
| Natural language support but running in an English locale |
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| ################### | |
| #RHC Example | |
| #install packages (if needed) | |
| install.packages("tableone") | |
| install.packages("ipw") | |
| install.packages("sandwich") | |
| install.packages("survey") |
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| ################### | |
| #RHC Example | |
| #install packages | |
| install.packages("tableone") ## useful when you're doing a matched analysis. | |
| install.packages("Matching") ## carry out the actual matching. | |
| #load packages | |
| library(tableone) | |
| library(Matching) |
ttungl
/ ex matching.py
Created
April 4, 2022 06:30
— forked from BioSciEconomist/ex matching.py
Very basic propensity score matching and IPTW analysis with balance diagnostics
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| # *----------------------------------------------------------------- | |
| # | PROGRAM NAME: ex matching.py | |
| # | DATE: 6/25/21 | |
| # | CREATED BY: MATT BOGARD | |
| # | PROJECT FILE: | |
| # *---------------------------------------------------------------- | |
| # | PURPOSE: very basic matching and IPTW analysis with balance diagnostics | |
| # *---------------------------------------------------------------- |
ttungl
/ buy_sell_stock_1.py
Last active
October 31, 2021 22:46
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| def maxProfit(self, prices: List[int]) -> int: | |
| # buy and then sell. | |
| # sol 1: | |
| # time O(n) space O(1) | |
| if not prices: | |
| return 0 | |
| buy_price = float('inf') | |
| max_profit = 0 | |
| for p in prices: # O(n) | |
| if buy_price < p: |
ttungl
/ merge_two_sorted_arrays.py
Created
October 31, 2021 03:06
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| def merge_two_arrays_3(a,b): ## len_a=n, len_b=m, m>n | |
| # n is length of a; m is length of b | |
| # time O(m+n) space O(n+m) | |
| from collections import deque # first in first out | |
| da = deque(a) | |
| db = deque(b) | |
| res = [] | |
| while len(da) or len(db): |
ttungl
/ stacking_example.py
Last active
October 31, 2021 03:10
— forked from geffy/stacking_example.py
Stacking example
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| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Mon Sep 23 23:16:44 2017 | |
| @author: Marios Michailidis | |
| This is an example that performs stacking to improve mean squared error | |
| This examples uses 2 bases learners (a linear regression and a random forest) | |
| and linear regression (again) as a meta learner to achieve the best score. | |
| The initial train data are split in 2 halves to commence the stacking. |
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| // http://ideone.com/7RLpDt | |
| #include <stdio.h> | |
| #include <stdlib.h> | |
| void Print_Array(int * array, int array_size){ | |
| for (int i = 0; i < array_size; ++i) | |
| { | |
| if(i == (array_size-1)){ | |
| printf("%d \n", array[i]); | |
| } else { |