[R-sig-teaching] FW: Deducer 0.1 Released

[Ian Fellows]

Hi All,

   I am seeking comments, suggestions, bugs, and users for a data analysis
GUI that has just been released to CRAN. One of the first areas that I think
that this GUI could be of use is in the classroom, so your comments would be
very valuable to me. An online manual is available (though under
construction) here:

http://www.deducer.org/pmwiki/pmwiki.php?n=Main.DeducerManual

   I'd appreciate any feedback or bugs. I'm particularly interested in
experiences using it from within non-JGR GUI's, and under Linux. If any of
you teach introductory/intermediate statistics, I'd like to know how you
would feel about using it in the classroom. 

TIA,
Ian


p.s. Installation instructions: 
install.packages("Deducer",,"http://cran.r-project.org")




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Deducer 0.1 has been released to CRAN

Deducer is designed to be a free, easy to use, alternative to proprietary
software such as SPSS, JMP, and Minitab. It has a menu system to do common
data manipulation and data analysis tasks, and an excel-like spreadsheet in
which to view and edit data frames. The goal of the project is to two fold. 

	1. Provide an intuitive interface so that non-technical users 
	   can learn and perform analyses without programming getting 
	   in their way. 
	2. Increase the efficiency of expert R users when performing 
         common tasks by replacing hundreds of keystrokes with a few 
         mouse clicks. Also, as much as possible the GUI should not 
         get in their way if they just want to do some programming. 

Deducer is integrated into the Windows RGui, and the cross-platform Java
console JGR, and is also usable and accessible from the command line.
Screen shots and examples can be viewed in the online wiki manual:

http://www.deducer.org/pmwiki/pmwiki.php?n=Main.DeducerManual

Comments and questions are more than welcome. A discussion group has been
created for any questions or recommendations.

http://groups.google.com/group/deducer

Deducer Features:

Data manipulation:
	1. Factor editor
	2. Variable recoding
	3. data sorting
	4. data frame merging
	5. transposing a data frame
	6. subseting

Analysis:
	1. Frequencies
	2. Descriptives
	3. Contingency tables
		a. Nicely formatted tables with optional
			i. Percentages
			ii. Expected counts
			iii. Residuals
		b. Statistical tests
			i. chi-squared 
			ii. likelihood ratio
			iii. fisher's exact
			iv. mantel haenszel
			v. kendall's tau
			vi. spearman's rho
			vii. kruskal-wallis
			viii. mid-p values for all exact/monte carlo tests
	4. One sample tests
		a. T-test
		b. Shapiro-wilk
		c. Histogram/box-plot summaries
	5. Two sample tests
		a. T-test (student and welch)
		b. Permutation test
		c. Wilcoxon
		d. Brunner-munzel
		e. Kolmogorov-smirnov
		f. Jitter/box-plot group comparison
	6. K-sample tests
		a. Anova (usual and welch)
		b. Kruskal-wallis
		c. Jitter/boxplot comparison
	7. Correlation
		a. Nicely formatted correlation matrices
		b. Pearson's
		c. Kendall's
		d. Spearman's
		e. Scatterplot paneled array
		f. Circle plot
		g. Full correlation matrix plot
        8.Generalized Linear Models
		a. Model preview
		b. Intuitive model builder
		c. diagnostic plots 
		d. Component residual and added variable plots
		e. Anova (type II and III implementing LR, Wald and F tests)
		f. Parameter summary tables and parameter correlations
		g. Influence and colinearity diagnostics
		h. Post-hoc tests and confidence intervals 
		   with (or without) adjustments for multiple testing.
		i. Custom linear hypothesis tests
		j. Effect mean summaries (with confidence intervals), and
plots
		k. Exports: Residuals, Standardized residuals, Studentized
 		   residuals, Predicted Values (linear and link), Cooks 
		   distance, DFBETA, DFFITS, hat values, and Cov Ratio
		l. Observation weights and subseting
	9. Logistic Regression
		a. All GLM features
		b. ROC Plot
	10. Linear Model
		a. All GLM features
       	b. Heteroskedastic robust tests