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
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