This article aims to replicate the recently (2015) plotly article in R. Link to article: Big data analytics with Pandas and SQLite Please make comments or suggestions via email, issues, or pull requests.
Using dygraphs and DT to make interactive reports using R
Splines, Local Regression, Polynomial Regression, GAM
Using caret and e1071. Start to finish: teaching the concepts to full examples.
cross validation and bootstrap theory and examples
use knitr to beautify your table outputs in your reproducible research.
KNN, logistic regression, LDA, QDA, random forests examples using the Boston dataset
More time series analysis: Seasonal ARIMA models + forecasting.
Time series Analysis: Stationarity Smoothing methods
Intro to time series analysis
Lots of start-to-finish times series analysis examples. Very informative.
Forecasting with non-seasonal ARIMA models
ARIMA models for time series analysis
Linear Discriminant Analysis and Quadratic DA in R.
Classification: Logistic Regression
A few start to finish examples of time series analysis with ARIMA models
Model fitting and forecasting
Mostly introduces the `caret` package in R, and how to apply it to solve some machine learning tools
Introduction to forecasting with non-seasonal ARIMA models
Material taken from the coursera class, link inside.
AR, MA, ARMA, ARIMA models Diagnostics Examples
Stationarity, ACF and CCF
The basics of time series analysis
Learning time series analysis in R