Publications by Steven P. Sanderson II, MPH
Subsetting Named Lists in R
Introduction In R, lists are a fundamental data structure that allows us to store multiple objects of different data types under a single name. Often times, we want to extract certain elements of a list based on their names, and this can be accomplished through the use of the subset function. In this blog post, we will take a look at how to use t...
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Attributes in R Functions: An Overview
Introduction R is a powerful programming language that is widely used for data analysis, visualization, and machine learning. One of the features of R that makes it versatile and flexible is the ability to assign attributes to functions. Attributes are metadata associated with an object in R, and they can be used to store additional information a...
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Median: A Simple Way to Detect Excess Events Over Time with {healthyR}
Introduction As we collect data over time, it’s important to look for patterns and trends that can help us understand what’s happening. One common way to do this is to look at the median of the data. The median is the middle value of a set of numbers, and it can be a useful tool for detecting whether there is an excess of events, either posit...
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{healthyR.ts}: The New and Improved Library for Time Series Analysis
Introduction Are you looking for a powerful and efficient library for time series analysis? Look no further than {healthyR.ts}! This library has recently been updated with new functions and improvements, making it easier for you to analyze and visualize your time series data. One of the new functions in {healthyR.ts} is ts_geometric_brownian_moti...
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Boilerplate XGBoost with {healthyR.ai}
Introduction XGBoost, short for “eXtreme Gradient Boosting,” is a powerful and popular machine learning library that is specifically designed for gradient boosting. It is an open-source library and is available in many programming languages, including R. Gradient boosting is a technique that combines the predictions of multiple weak models to...
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Geometric Brownian Motion with {healthyR.ts}
Introduction Geometric Brownian motion (GBM) is a widely used model in financial analysis for modeling the behavior of stock prices. It is a stochastic process that describes the evolution of a stock price over time, assuming that the stock price follows a random walk with a drift term and a volatility term. One of the advantages of GBM is that i...
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Augmenting a Brownian Motion to a Time Series with {healthyR.ts}
Introduction Time series analysis is a crucial tool for forecasting and understanding trends in various industries, including finance, economics, and engineering. However, traditional time series analysis methods can be limiting, and they may not always capture the complex dynamics of real-world data. That’s where the R package {healthyR.ts} co...
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Auto K-Means with {healthyR.ai}
Introduction Today’s post is going to center around the automatic k-means functionality of {healthyR.ai}. I am not going to get into what it is or how it works, but rather the function call itself and how it works and what it puts out. The function is called hai_kmeans_automl. This function is a wrapper around the h2o::h2o.kmeans() function, bu...
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The building of {tidyAML}
Introduction Yesterday I posted on An Update to {tidyAML} where I was discussing some of my thought process and how things could potentially work for the package. Today I want to showcase how the function fast_regression_parsnip_spec_tbl() and it’s complimentary function fast_classification_parsnip_spec_tbl() actually work or maybe don’t work...
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Making Non Stationary Data Stationary
Introduction In the most basic sense for time series, a series is stationary if the properties of the generating process (the process that generates the data) do not change over time, the process remains constant. This does not mean the data does not change, it simply means the process does not change. You can bake a vanilla cake or a chocolate c...
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