The Mark–Recapture method, originally developed in ecology to estimate wildlife populations, provides a statistically rigorous alternative for estimating the size of partially observed human populations. Here is an example demonstrating how to apply the Mark–Recapture method to estimate the size of a smoking population using simulated data.

Greenfield Analysis (GFA)is a facility location optimization technique used to identify the most suitable placement of new service centers, warehouses, or healthcare facilities when no prior infrastructure constraints exist. In this analysis, we applied a center-of-gravity (CoG) approach to identify four optimal facility locations in Connecticut.

When detecting HCV outbreaks, HLM will alert you if the current quarter’s case count is much higher than the average of previous quarters, without adjusting for changes in population size or time trends. In contrast, NB regression can model expected case counts based on factors such as time, region, population, and seasonality, providing a more accurate expected count along with confidence intervals.

This tutorial provides a practical guide to using the R programming language to detect infectious disease outbreaks through time–space analysis. You’ll learn how to use R’s powerful data-handling capabilities and functions to identify unusual increases in specific diagnoses.

The OneR (One Rule) classification model is a simple yet effective rule-based machine learning algorithm. It generates one rule for each feature in the dataset and then selects the rule with the lowest error rate for classification. Despite its simplicity, OneR performs surprisingly well on many classification tasks, often competing with more complex machine learning models.

Forecasting plays a vital role in identifying patterns within time-series data and supporting informed decision-making across diverse fields such as business, healthcare, economics, and environmental studies.

Regression analysis in machine learning (ML) is a method used to examine the connection between independent variables and a dependent variable. This type of analysis it is known as predictive modeling, in which an algorithm or method is used to predict continuous outcomes. Here are the steps for conducting a ML regression analysis and deploying the final selected model.

{tidycensus} enables users to interact with specific US Census Bureau data APIs. It retrieves data frames compatible with tidyverse and integrates a straightforward geography feature.

Hierarchical cluster analysis example in R.

Steps on how to K-Means and K-Medoids clustering. K-means clustering is an unsupervised machine learning algorithm that identifies groups in unlabeled data. K-medoids is an unsupervised method with unlabelled data to be clustered. It is an improvised version of the K-Means algorithm mainly designed to deal with outlier data sensitivity.

Create {ggplo2} and {tmap} maps in R by merging shapefile and data source file.

Using the {rgeoda} package to create Local Moran and Local Getis-Ord Maps.

Using the {gtsummary} package offers a stylish and adaptable method for producing analytical and summary tables that are ready for publication.

Creating a HeatMap from 2012 Starbucks locations in CT, MA and RI.

Analyzing large amounts of data in search of anomalies can be a frustrating task. You need techniques that allow you to quickly evaluate data in a way that highlights potential anomalies and prevents you from conducting analysis that are meaningless.

In this presentation we will learn how to impute missing values using the {missForest} package. This package uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data.

Here are the instructions on how to perform classical time series analysis and machine learning modeling in one framework.

This presentation shows the steps on how to deploy a GLM (Logistic Regression) machine learning model created in R via a Plumber API.

Compare the estimated accuracy of different machine learning algorithms (models). Select the most accurate model for your predictive analytics project. When working on a machine learning project, you often have several good models to choose from. Each of the models you selected needs to be measure for accuracy. In order to select the best and final model(s), you should use several different methods to estimated the accuracy of your machine learning models. Here are the steps on how to select the best and final model(s) using the Vertical Box-and-Whisker Plot method.

The {rayshader} is an open source package for producing 2D and 3D data visualizations in R.

The {forester} package is an AutoML tool in R for tabular data regression and binary classification tasks. It wraps up all machine learning processes into a single train() function.

The spmodel is an R package used to fit, summarize, and predict for a variety spatial statistical models applied to point-referenced or areal (lattice) data. Parameters are estimated using various methods, including likelihood-based optimization and weighted least squares based on variograms.

The ADI scores shown here identify areas where deprivation and affluence exist within communities in Connecticut. Organizations implementing Overdose, HIV and Hep C prevention interventions can use this information to identify high deprivation areas in Connecticut. It is recommended for organizations in Connecticut to focus their Overdose, HIV and Hep C prevention efforts in high level deprivation areas.

The {vtree} package is a tool for calculating and displaying variable trees.

Here are the steps for spatial counting points (i.e., Long and Lat coordinates) using the {sf} package

Steps on how to assess variable importance in a logistic regression analysis.

A logistic regression is used to predict a class (or category) variable (y) based on one or more predictor variables (x).

The {summarytools} package allows you to quickly create an EDA report in R.