Web cookies (also called HTTP cookies, browser cookies, or simply cookies) are small pieces of data that websites store on your device (computer, phone, etc.) through your web browser. They are used to remember information about you and your interactions with the site.
Purpose of Cookies:
Session Management:
Keeping you logged in
Remembering items in a shopping cart
Saving language or theme preferences
Personalization:
Tailoring content or ads based on your previous activity
Tracking & Analytics:
Monitoring browsing behavior for analytics or marketing purposes
Types of Cookies:
Session Cookies:
Temporary; deleted when you close your browser
Used for things like keeping you logged in during a single session
Persistent Cookies:
Stored on your device until they expire or are manually deleted
Used for remembering login credentials, settings, etc.
First-Party Cookies:
Set by the website you're visiting directly
Third-Party Cookies:
Set by other domains (usually advertisers) embedded in the website
Commonly used for tracking across multiple sites
Authentication cookies are a special type of web cookie used to identify and verify a user after they log in to a website or web application.
What They Do:
Once you log in to a site, the server creates an authentication cookie and sends it to your browser. This cookie:
Proves to the website that you're logged in
Prevents you from having to log in again on every page you visit
Can persist across sessions if you select "Remember me"
What's Inside an Authentication Cookie?
Typically, it contains:
A unique session ID (not your actual password)
Optional metadata (e.g., expiration time, security flags)
Analytics cookies are cookies used to collect data about how visitors interact with a website. Their primary purpose is to help website owners understand and improve user experience by analyzing things like:
How users navigate the site
Which pages are most/least visited
How long users stay on each page
What device, browser, or location the user is from
What They Track:
Some examples of data analytics cookies may collect:
Page views and time spent on pages
Click paths (how users move from page to page)
Bounce rate (users who leave without interacting)
User demographics (location, language, device)
Referring websites (how users arrived at the site)
Here’s how you can disable cookies in common browsers:
1. Google Chrome
Open Chrome and click the three vertical dots in the top-right corner.
Go to Settings > Privacy and security > Cookies and other site data.
Choose your preferred option:
Block all cookies (not recommended, can break most websites).
Block third-party cookies (can block ads and tracking cookies).
2. Mozilla Firefox
Open Firefox and click the three horizontal lines in the top-right corner.
Go to Settings > Privacy & Security.
Under the Enhanced Tracking Protection section, choose Strict to block most cookies or Custom to manually choose which cookies to block.
3. Safari
Open Safari and click Safari in the top-left corner of the screen.
Go to Preferences > Privacy.
Check Block all cookies to stop all cookies, or select options to block third-party cookies.
4. Microsoft Edge
Open Edge and click the three horizontal dots in the top-right corner.
Go to Settings > Privacy, search, and services > Cookies and site permissions.
Select your cookie settings from there, including blocking all cookies or blocking third-party cookies.
5. On Mobile (iOS/Android)
For Safari on iOS: Go to Settings > Safari > Privacy & Security > Block All Cookies.
For Chrome on Android: Open the app, tap the three dots, go to Settings > Privacy and security > Cookies.
Be Aware:
Disabling cookies can make your online experience more difficult. Some websites may not load properly, or you may be logged out frequently. Also, certain features may not work as expected.
Reproducible Science with LaTeX (with HaiYing Wang). Journal of Data Science. The LaTeX package runcode is available on CTAN.
The talk2stat Python package allows programs (such as latextopdf, nodejs) to communicate with statistical packages.
edgefinder (current version is 0.1.10) – a method for recovering a gene network’s structure from co-expression data. The code for simulations and a case study files are provided here edgefinder.tar. (PLOS ONE). The preferred way to install the package is directly from the github.com repository, by running devtools::install_github("haimbar/edgefinder")
The zipped R package is also available here. To install from the tar.gz file, download the file and run install.packages("edgefinder_0.1.10.tar.gz",repos=NULL) (Note that the web server may change the zipped files *tar.gz files to *tar_.gz, so rename the file by removing the extra underscore before running the install.packages command).
QREM – quantile regression via the EM algorithm, based on the paper on arxiv. The preferred way to install the package is directly from the github.com repository, by running devtools::install_github("haimbar/QREM")
Or, you can download the R package QREM and install by using install.packages("QREM_0.1.8.tar.gz",repos=NULL) (Note that the web server changed the zipped files *tar.gz files to *tar_.gz, so rename the file by removing the extra underscore before running the install.packages command)
SEMMS – “Scalable EMpirical Bayes Model Selection” — a variable selection method which uses a parsimonious mixture model to identify significant predictors in the Generalized Linear Models (GLM) framework (JCGS 2019, arxiv). Install the package from github, using devtools::install_github("haimbar/SEMMS"). The R package (version 0.2.5) contains some data sets, and a vignette with detailed examples. To install from the zip file, download the package and run install.packages("SEMMS_0.2.5.tar.gz",repos=NULL) (Note that the web server changed the zipped files *tar.gz files to *tar_.gz, so rename the file by removing the extra underscore before running the install.packages command). The project’s source is available on github at https://www.github.com/haimbar/SEMMS . Fifty free eprint versions of the paper are available from the publisher, Taylor and Francis. See also a short description in the YoungStatS blog.
DVX is an interactive program written in R, which can be used to perform Differential Variation and eXpression analysis of gene expression (or similar) data (STAT).
R–CMap is an open-source concept mapping software. The latest version is now available on github. The older version is available here. For more background about concept mapping, its applications, and about R-CMap, see the Special Issue in Evaluation and Program Planning
MCMC code for the paper “A Spatial Capture-Recapture Model with Dependent Animal Movement” (in preparation), with Paul McLaughlin, Ph.D. Code was written by Dr. McLaughlin. To change the dataset, see the “User Input” section. The data has to include capture histories, trap locations, the number of trapping occasions, the size of a buffer zone around the trapping area, chain length and burn-in settings for the MCMC code, and the upper bound for the number of animals.