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