R tutorials

have a look 

qualitative or mixed data analysis

LinkedIn даёт пакет для такого анализа.
#Rstats #research My brief survey of R packages for qualitative (or mixed) data analysis. http://qr.ae/6auZw

May be lme4 will be useful
http://lme4.r-forge.r-project.org/lMMwR/lrgprt.pdf

Javier, I appreciate the suggestion and the link. In my post, I've tried to limit the discussion to R packages that are much more focused on qualitative data analysis than many others. I understand that "lme4" and many other packages contain functionality that deals with qualitative (in a form of categorical) or mixed data, but I was trying to limit the scope of the material for better focus. More importantly, mixed-effects modeling is IMHO inherently quantitative approach. Nevertheless, this is an interesting topic, which I plan to become acquainted more with in the future. Thank you again and feel free to connect!

Check also this package: http://factominer.free.fr/advanced-methods/multiple-factor-analysis.html

Ahmed, I'm aware of "factominer" and some other FA packages. However, I tend to think of them mostly as packages for quantitative data analysis, despite their support for categorical data. Please see my points in the comment for Javier above. Anyway, I appreciate your comment.

Forecasting mortality, fertility, migration and population data

The demography package for R contains functions for various demographic analyses. It provides facilities for demographic statistics, modelling and forecasting. In particular, it implements lifetable calculations; Lee-Carter modelling and variants; functional data analysis of mortality rates, fertility rates, net migration numbers; and stochastic population forecasting.

and more

Top 10 tips to get started with R

(This article was first published on mages' blog, and kindly contributed to R-bloggers) Be motivated. R has a steep learning curve. Find a problem you can't solve otherwise. E.g. plotting multivariate data, a statistical analysis for which an R function exists already. Download and install R. Get to know the R console. Learn how to install additional packages, how to access the history, how to use auto completion and open the help system. Review the R Installation and Administration manual and check out the free books section on CRAN.  Get familiar with the R help files. They can appear cryptic at the start, but there is a structure to them. Read and re-read a couple of help files again and again. Look out for the input and output sections, execute the examples, run the demos, e.g.demo(graphics). Subscribe to R-help and read questions and answers, check outstackoverflow, follow blogs. Search with Rseek.org.  Learn how to get your data into R. The easiest way is usually via a CSV-file (CSV=comma separated values), using read.csv. Look into XLConnect, if you have to deal with spreadsheet files. Move on to write queries against data bases, e.g. using RODBC. Skim through the R Data Import/Export manual.  Try to understand the different data types in R and how to modify them. What are the differences between a matrix and a data frame? What is a factor? What is a list? Think about the different use cases. Review the Introduction to R manual. Do charts! Lots of charts. They are rewarding and keep you motivated. Be inspired by the R Graph Gallery. Check out the following packages: lattice, plotrix, ggplot2,deducer, googleVis. Learn how you can modify and reshape data in R and apply functions on subsets using by, apply, lapply, ave, reshape, sweep, with, within, etc. Set aside a weekend to think about these functions.  Write your R code into files instead of typing it all into the R console. Use anintegrated development environment (IDE), e.g. ESS Emacs, RStudio, StatET Eclipse. Understand the concept of functions. Write a function, which gives "Hello World" back. Modify it, so it has an input argument NAME and it prints "Hello NAME". Review the code of existing R functions. Copy from existing code. Document your code! Start your code by explaining what you want to achieve and only code that much, then write down the next step in plain English and code again. How will you know that your code does what you want it to do? Testing can help. Think your about your code style and how you will be versioning your files. Bonus tip Be patient. Going mad? Check out the R Inferno by Pat Burns. To leave a comment for the author, please follow the link and comment on his blog:mages' blog. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX,SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series,ecdf,trading) and more...

survival analysis

log-regression

см. перевод Касьянчука статьи про логистическую регрессию

Statistics for the Social Sciences

страничка jfox at mcmaster.ca на кране
с короткими рекомендациями
любопытно, что этому блогу уже больше 2х лет, начал 3 февраля 2011

couple of resources

Rob Kabacoff's Quick-R accessing the power of R, and
RDataMining.com: R and Data Mining

still does not work

Это про Rcmdr
Удалось установить только RStudio, a кроме упомянутого Rcmdra ещё хочу Rattle, который тоже не встаёт :(

вопросы задавал в ЖЖ и
в LinkedIn
ответы есть, но, тем не менее, воз и ныне там, а нужда уже подпирает

устанавливал по оригинальной инструкции, есличо

easy introduction

RStudio, RCommader, and Rattle

текст с пояснениями

R u training on line?

Онлайновы курсы и линьки на полезные Rесурсы 

meaning forecast

robjhyndman.com пишет про новый R пакет прогнозирования как бе, но пишет следующим образом:
The accuracy func­tion now works with both time series fore­casts and cross-​​sectional forecasts. то-есть, по-руски прогнозирование -- это предвидение по времени, а попе н достансге -- совсем не обязательно и вводит нас (если на демографическом образовании строицо) в мир indirect techniques

Multivariate regression analysis using categorical explanatory variables

LinkedIN начал новую нитку с этой назвой

Which package is useful to handle categorical variables in a multivariate regression analyisis and where may I find the tutorials? Every information that I find is about quantitative data.
I am currently using a lm function to find the best predictor for a quantitative response variable but I am afraid, it is not the best approach. I also do not want to use dummies.

research vs grants

Rob J Hyn­d­man :
I don’t think telling peo­ple what research areas are impor­tant is very help­ful. The best research comes about when peo­ple are curi­ous and pas­sion­ate, and take a sur­pris­ingly dif­fer­ent per­spec­tive from those who have gone before.
from here
действительно, как раз сейчас пытаюсь ответить на вопросы какбе грантодателей, 1 из х:
Существует ли в стране политика, программа и/или стратегия, направленная на решение вопросов сексуального и репродуктивного здоровья и репродуктивных прав, которая(ые) находится в процессе разработки или исполнения?
ответить надо да/нет

Tutorials from Universities Around the World

Online tutorials for R programming, Statistics and Graphic

Here is a list of FREE R tutorials hosted in official website of universities around the world. The tutorials are listed  in no particular order, actually based on when I have discovered it. They will be categorised soon. Please kindly suggest me other university-hosted online R tutorials by email to me@pairach.com.

1. University of California at Davis,
   Getting Started with the R Data Analysis Package
   by Professor Norm Matloff
2. Clarkson University,
   R Tutorial
   by Kelly Black
3. York University,
   Getting started with R
4. University of Waterloo
   R Tutorial For A WINDOWS Environment (Windows, Unix)
5. University of California at Los Angles, UCLA,
   Resources to help you learn and use R
6. University of California at Riverside.
   Programming in R
7. University of Illinois,
   A Brief Introduction to R
8. University of Texas at Austin,
   R Tutorial Videos
   by Brandon K. Vaughn
9. University of California at Berkeley,
   An Introduction to R (PDF)
   by Phil Spector
10. University of California at Santa Babara,
    R Programming Resource Centre
    by National Center for Ecological Analysis and Synthesis
11. Chiang Mai University,
    Econometrics with R (in Thai)
    by Pairach Piboonrungroj.
12. University of Carnegie Mellon,
    A Tutorial: Some Fundamentals of R.
    by Bruce E. Trumbo
13. University of Illinois State,
    R Tutorial.
    by Dong-Yun Kim
14. University of MacMaster,
    Introduction to the R Statistical Computing Environment.
    by John Fox.
15. University of Princeton,
    Introducing R.
    by Germán Rodríguez
16. University of Amsterdam,
    How to draw graphs with R,  [Graphics]
    by A.M. (Angelos-Miltiadis) Krypotos
17. University of North Texas,
    Do it yourself – Introduction to R [Intro]
18. University of Warwick,
    R programming page [Biosciences: Molecular Organisation and Assembly in Cells]
    by Peter Cock.
19. University of Illinois at Urbana-Champaign
    R tutorial for Applied Econometrics
    by Prof. Roger Koenker
    
20. Coastal Carolina University
    R tutorials [General]
    by  William B. King
21. University of Colorado Denver
    R Tutorial [General]
    by  Stephanie Santorico and Mark Shin
22. Stanford School of Medicine (Biomedical Informatics)
    R Tutorial [VDO on Introduction + Translational Bioinformatics]
23. Harding University
    Producing Simple Graphs with R [basic graphic e.g., line, bar, hist, line]
    by Frank McCown
24. University of Kentucky, Department of Statistics
    Use Software R to do Survival Analysis and Simulation [pdf]
    by Mai Zhou

Forecasting time series using R by Prof Rob J Hyndman at Melbourne R Users

comparing R, Matlab, SAS, STATA, SPSS

The following table compares the standard procedures of the five packages in detail. By "standard" I mean built-in or readily available from the official or widely known and reliable public web-sites.

TYPE OF STATISTICAL ANALYSIS	R	MATLAB	SAS	STATA	SPSS
Nonparametric Tests	Yes	Yes	Yes	Yes	Yes
T-test	Yes	Yes	Yes	Yes	Yes
ANOVA & MANOVA	Yes	Yes	Yes	Yes	Yes
ANCOVA & MANCOVA	Yes	Yes	Yes	Yes	Yes
Linear Regression	Yes	Yes	Yes	Yes	Yes
Generalized Least Squares	Yes	Yes	Yes	Yes	Yes
Ridge Regression	Yes	Yes	Yes
Lasso	Yes	Yes	Experimental
Generalized Linear Models	Yes	Yes	Yes	Yes	Yes
Mixed Effects Models	Yes	Yes	Yes	Yes	Yes
Logistic Regression	Yes	Yes	Yes	Yes	Yes
Nonlinear Regression	Yes	Yes	Yes
Discriminant Analysis	Yes	Yes	Yes	Yes	Yes
Nearest Neighbor	Yes	Yes	Yes		Yes
Factor & Principal Components Analysis	Yes	Yes	Yes	Yes	Yes
Copula Models	Yes	Yes	Experimental
Cross-Validation	Yes	Yes	Yes
Bayesian Statistics	Yes	Yes	Limited
Monte Carlo, Classic Methods	Yes	Yes	Yes	Yes	Limited
Markov Chain Monte Carlo	Yes	Yes	Yes
Bootstrap & Jackknife	Yes	Yes	Yes	Yes
EM Algorithm	Yes	Yes	Yes
Missing Data Imputation	Yes	Yes	Yes	Yes	Yes
Outlier Diagnostics	Yes	Yes	Yes	Yes	Yes
Robust Estimation	Yes	Yes	Yes	Yes
Longitudinal (Panel) Data	Yes	Yes	Yes	Yes
Survival Analysis	Yes	Yes	Yes	Yes	Yes
Path Analysis	Yes	Yes	Yes
Propensity Score Matching	Yes	Yes	Limited
Stratified Samples (Survey Data)	Yes	Yes	Yes	Yes	Yes
Experimental Design	Yes	Yes
Quality Control	Yes	Yes		Yes	Yes
Reliability Theory	Yes	Yes	Yes	Yes	Yes
Univariate Time Series	Yes	Yes	Yes	Yes	Limited
Multivariate Time Series	Yes	Yes	Yes	Yes
Markov Chains	Yes	Yes
Hidden Markov Models	Yes	Yes
Stochastic Volatility Models	Yes	Yes	Limited	Limited	Limited
Diffusions	Yes	Yes
Counting Processes	Yes	Yes	Yes
Filtering	Yes	Yes	Limited	Limited
Instrumental Variables	Yes	Yes	Yes	Yes
Simultaneous Equations	Yes	Yes	Yes	Yes
Splines	Yes	Yes	Yes
Nonparametric Smoothing Methods	Yes	Yes	Yes	Yes
Extreme Value Theory	Yes	Yes
Variance Stabilization	Yes	Yes
Cluster Analysis	Yes	Yes	Yes	Yes	Yes
Neural Networks	Yes	Yes	Yes		Limited
Classification & Regression Trees	Yes	Yes	Yes		Limited
Boosting Classification & Regression Trees	Yes	Yes
Random Forests	Yes	Yes
Support Vector Machines	Yes	Yes
Signal Processing	Yes	Yes
Wavelet Analysis	Yes	Yes	Yes
ROC Curves	Yes	Yes	Yes	Yes	Yes
Optimization	Yes	Yes	Yes

Please read the detailed description of the services offered in the areas of statistical consulting and financial consulting: home page, types of service, experience, case studies and payment options. You may also find the following pages useful: statistics resources and finance resources.

Source: http://stanfordphd.com/Statistical_Software.html