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1.

図書
図書
1. Enumerability, decidability, computability : an introduction to the theory of recursive functions
by Hans Hermes ; translated by G.T. Herman and O. Plassmann
出版情報: Berlin ; New York : Springer, 1965  ix, 245 p. ; 24 cm
シリーズ名: Die Grundlehren der mathematischen Wissenschaften ; Bd. 127
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2.

図書
図書
2. Computer aided tomography and ultrasonics in medicine : proceedings of the IFIP TC-4 Working Conference on Computer Aided Tomography and Ultrasonics in Medicine
edited by Josef Raviv, associate editors, James F. Greenleaf and Gabor T. Herman
出版情報: Amsterdam ; New York : North-Holland Pub. Co. , New York : Sole distributors for the U.S.A. and Canada, Elsevier North-Holland, 1979  x, 319 p. ; 23 cm
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3.

図書
図書
3. Image reconstruction from projections : implementation and applications (gw)
edited by G. T. Herman ; with contributions by M. D. Altschuler ... [et al.]
出版情報: Berlin ; New York : Springer-Verlag, 1979  xii, 284 p. ; 24 cm
シリーズ名: Topics in applied physics ; v. 32
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目次情報: 続きを見る
Introduction / 1：
Why Learn R? / 1.1：
Is R Accurate? / 1.2：
What About Tech Support? / 1.3：
The Five Main Parts of SAS and SPSS / 2：
Programming Conventions / 3：
Typographic Conventions / 4：
Installing and Updating R / 5：
Installing Add-on Packages / 5.1：
Loading an Add-on Package / 5.2：
Updating Your Installation / 5.3：
Uninstalling R / 5.4：
Choosing Repositories / 5.5：
Accessing Data in Packages / 5.6：
Running R / 6：
Running R Interactively on Windows / 6.1：
Running R Interactively on Macintosh / 6.2：
Running R Interactively on Linux or UNIX / 6.3：
Running Programs that Include Other Programs / 6.4：
Running R in Batch Mode / 6.5：
Running R from SPSS / 6.6：
Graphical User Interfaces / 6.7：
R Commander / 6.7.1：
Rattle for Data Mining / 6.7.2：
JGR Java GUI for R / 6.7.3：
Help and Documentation / 7：
Help Files / 7.1：
Starting Help / 7.2：
Help Examples / 7.3：
Help for Functions that Call Other Functions / 7.4：
Help for Packages / 7.5：
Help for Datasets / 7.6：
Books and Manuals / 7.7：
E-mail Lists / 7.8：
Searching the Web / 7.9：
Vignettes / 7.10：
Programming Language Basics / 8：
Simple Calculations / 8.1：
Data Structures / 8.2：
Vectors / 8.2.1：
Factors / 8.2.2：
Data Frames / 8.2.3：
Matrices / 8.2.4：
Arrays / 8.2.5：
Lists / 8.2.6：
Saving Your Work So Far / 8.3：
Comments to Document Your Programs / 8.4：
Controlling Functions (Procedures) / 8.5：
Controlling Functions with Arguments / 8.5.1：
Controlling Functions with Formulas / 8.5.2：
Controlling Functions with an Object's Class / 8.5.3：
Controlling Functions with Extractor Functions - ODS, OMS / 8.5.4：
How Much Output Is There? / 8.5.5：
Writing Your Own Functions (Macros) / 8.5.6：
Data Acquisition / 9：
The R Data Editor / 9.1：
Reading Delimited Text Files / 9.2：
Reading Text Data Within a Program (Datalines, Cards, Begin Data...) / 9.3：
Reading Data from the Keyboard / 9.4：
Reading Fixed-Width Text Files, One Record per Case / 9.5：
Macro Substitution / 9.5.1：
Reading Fixed-Width Text Files, Two or More Records per Case / 9.6：
Importing Data from SAS / 9.7：
Importing Data from SPSS / 9.8：
Exporting Data / 9.9：
Viewing an External Text File / 9.9.1：
Selecting Variables - Var, Variables = / 10：
Selecting Variables in SAS and SPSS / 10.1：
Selecting All Variables / 10.2：
Selecting Variables by Index Number / 10.3：
Selecting Variables by Column Name / 10.4：
Selecting Variables Using Logic / 10.5：
Selecting Variables by String Search (varname: or varname1-varnameN) / 10.6：
Selecting Variables Using $ Notation / 10.7：
Selecting Variables by Simple Name: attach and with / 10.8：
Selecting Variables with the subset Function (varname1-varnameN) / 10.9：
Selecting Variables by List / 10.10：
Generating Indexes A to Z from Two Variable Names / 10.11：
Saving Selected Variables to a New Dataset / 10.12：
Example Programs for Variable Selection / 10.13：
Selecting Observations - Where, If, Select If, Filter / 11：
Selecting Observations in SAS and SPSS / 11.1：
Selecting All Observations / 11.2：
Selecting Observations by Index Number / 11.3：
Selecting Observations by Row Name / 11.4：
Selecting Observations Using Logic / 11.5：
Selecting Observations by String Search / 11.6：
Selecting Observations with the subset Function / 11.7：
Generating Indexes from A to Z from Two Row Names / 11.8：
Variable Selection Methods with No Counterpart for Selecting Observations / 11.9：
Saving Selected Observations to a New Data Frame / 11.10：
Example Programs for Selecting Observations / 11.11：
Selecting Both Variables and Observations / 12：
Converting Data Structures / 13：
Converting from Logical to Index and Back / 13.1：
Data Management / 14：
Transforming Variables / 14.1：
Procedures or Functions? The apply Function Decides / 14.2：
Applying the mean Function / 14.2.1：
Finding N or NVALID / 14.2.2：
Conditional Transformations / 14.3：
Multiple Conditional Transformations / 14.4：
Missing Values / 14.5：
Substituting Means for Missing Values / 14.5.1：
Finding Complete Observations / 14.5.2：
When "99" Has Meaning / 14.5.3：
Renaming Variables (... and Observations) / 14.6：
Renaming Variables - Advanced Examples / 14.7：
Renaming by Index / 14.7.1：
Renaming by Column Name / 14.7.2：
Renaming Many Sequentially Numbered Variable Names / 14.7.3：
Renaming Observations / 14.7.4：
Recoding Variables / 14.8：
Recoding a Few Variables / 14.8.1：
Recoding Many Variables / 14.8.2：
Keeping and Dropping Variables / 14.9：
Stacking/Concatenating/Adding Datasets / 14.10：
Joining/Merging Data Frames / 14.11：
Creating Summarized or Aggregated Datasets / 14.12：
The aggregate Function / 14.12.1：
The tapply Function / 14.12.2：
Merging Aggregates with Original Data / 14.12.3：
Tabular Aggregation / 14.12.4：
The reshape Package / 14.12.5：
By or Split File Processing / 14.13：
Comparing Summarization Methods / 14.13.1：
Example Programs for By or Split File Processing / 14.13.2：
Removing Duplicate Observations / 14.14：
Selecting First or Last Observations per Group / 14.15：
Reshaping Variables to Observations and Back / 14.16：
Sorting Data Frames / 14.17：
Value Labels or Formats (and Measurement Level) / 15：
Character Factors / 15.1：
Numeric Factors / 15.2：
Making Factors of Many Variables / 15.3：
Converting Factors into Numeric or Character Variables / 15.4：
Dropping Factor Levels / 15.5：
Variable Labels / 16：
Generating Data / 17：
Generating Numeric Sequences / 17.1：
Generating Factors / 17.2：
Generating Repetitious Patterns (not factors) / 17.3：
Generating Integer Measures / 17.4：
Generating Continuous Measures / 17.5：
Generating a Data Frame / 17.6：
How R Stores Data / 18：
Managing Your Files and Workspace / 19：
Loading and Listing Objects / 19.1：
Understanding Your Search Path / 19.2：
Attaching Data Frames / 19.3：
Attaching Files / 19.4：
Removing Objects from Your Workspace / 19.5：
Minimizing Your Workspace / 19.6：
Setting Your Working Directory / 19.7：
Saving Your Workspace / 19.8：
Saving Your Programs and Output / 19.9：
Saving Your History (Journal) / 19.10：
Graphics Overview / 20：
SAS/GRAPH / 20.1：
SPSS Graphics / 20.2：
R Graphics / 20.3：
The Grammar of Graphics / 20.4：
Other Graphics Packages / 20.5：
Graphics Procedures Versus Graphics Systems / 20.6：
Graphics Devices / 20.7：
Practice Data: Mydata100 / 20.8：
Traditional Graphics / 21：
Barplots / 21.1：
Barplots of Counts / 21.1.1：
Barplots for Subgroups of Counts / 21.1.2：
Barplots of Means / 21.1.3：
Adding Titles, Labels, Colors, and Legends / 21.2：
Graphics Parameters and Multiple Plots on a Page / 21.3：
Pie Charts / 21.4：
Dotcharts / 21.5：
Histograms / 21.6：
Basic Histograms / 21.6.1：
Histograms Overlaid / 21.6.2：
Normal QQ Plots / 21.7：
Strip Charts / 21.8：
Scatterplots / 21.9：
Scatterplots with Jitter / 21.9.1：
Scatterplots with Large Datasets / 21.9.2：
Scatterplots with Lines / 21.9.3：
Scatterplots with Linear Fit by Group / 21.9.4：
Scatterplots by Group or Level (Coplots) / 21.9.5：
Scatterplots with Confidence Ellipse / 21.9.6：
Scatterplots with Confidence and Prediction Intervals / 21.9.7：
Plotting Labels Instead of Points / 21.9.8：
Scatterplot Matrices / 21.9.9：
Dual Axes Plots / 21.10：
Boxplots / 21.11：
Error Bar and Interaction Plots / 21.12：
Adding Equations and Symbols to Graphs / 21.13：
Summary of Graphics Elements and Parameters / 21.14：
Plot Demonstrating Many Modifications / 21.15：
Example Traditional Graphics Programs / 21.16：
Graphics with ggplot2 (GPL) / 22：
Overview qplot and ggplot / 22.1：
Bar Charts / 22.2：
Bar Charts with Subgroups / 22.3：
Plots by Group or Level / 22.5：
Pre-summarized Data / 22.6：
Adding Titles and Labels / 22.7：
Strip Plots / 22.9：
Scatterplots with Fit Lines / 22.12：
Scatterplots with Reference Lines / 22.15：
Changing Plot Symbols by Group / 22.16：
Adding Linear Fits by Group / 22.18：
Scatterplots Faceted by Groups / 22.19：
Scatterplot Matrix / 22.20：
Error Barplots / 22.21：
Logarithmic Axes / 22.23：
Aspect Ratio / 22.24：
Multiple Plots on a Page / 22.25：
Saving ggplot2 Graphs to a File / 22.26：
An Example Specifying All Defaults / 22.27：
Summary of Graphic Elements and Parameters / 22.28：
Statistics / 23：
Scientific Notation / 23.1：
Descriptive Statistics / 23.2：
Cross-Tabulation / 23.3：
Correlation / 23.4：
Linear Regression / 23.5：
Plotting Diagnostics / 23.5.1：
Comparing Models / 23.5.2：
Making Predictions with New Data / 23.5.3：
t-Test - Independent Groups / 23.6：
Equality of Variance / 23.7：
t-Test - Paired or Repeated Measures / 23.8：
Wilcoxon Mann-Whitney Rank Sum Test - Independent Groups / 23.9：
Wilcoxon Signed-Rank Test - Paired Groups / 23.10：
Analysis of Variance / 23.11：
Sums of Squares / 23.12：
Kruskal-Wallis Test / 23.13：
Conclusion / 24：
A Glossary of R Jargon / Appendix A：
A Comparison of SAS and SPSS Products with R Packages and Functions / Appendix B：
Automating Your Settings / Appendix C：
A comparison of the major attributes of SAS and SPSS to R / Appendix D：
Bibliography
Index
Introduction / 1：
Why Learn R? / 1.1：
Is R Accurate? / 1.2：
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