• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.893-910
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• 2009

Abstract We can use R package as a statistical package on the statistical education for college students. R is an interactive mode package and graphical presentation tools are powerful in R. The greatest advantage is that R is a general public license package. We need to consider R package as a standard statistical package on the statistical education for college students. We can consider the applications of R for inferential statistics in elementary statistics course.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.87-105
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• 2020

Statistical engineering contains the design of experiments, quality control/management, and reliability engineering. R is a free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. R package has many functions and libraries for statistical engineering. We can use R package as a useful tool for statistical engineering. This paper shows the applications of R package for statistical engineering and suggests a R Task View for statistical engineering.

• Communications of Mathematical Education
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• v.21 no.2 s.30
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• pp.199-225
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• 2007

We can use R package as a statistical package on the education of probability and statistics in elementary, middle and high school mathematics. R is an interactive mode package and graphical presentation tools in R are powerful. The greatest advantage is that R is a general public license package. We need to consider R package as a standard statistical package on the education of probability and statistics in elementary, middle and high school mathematics.

• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.673-679
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• 2021

The moment calculation in a truncated multivariate normal distribution is a long-standing problem in statistical computation. Recently, Kan and Robotti (2017) developed an algorithm able to calculate all orders of moment under different types of truncation. This result was implemented in an R package MomTrunc by Galarza et al. (2021); however, it is difficult to use the package in practical statistical problems because the computational burden increases exponentially as the order of the moment or the dimension of the random vector increases. Meanwhile, Lee (2021) presented an efficient numerical method in both accuracy and computational burden using Gauss-Hermit quadrature. This article introduces trunmnt implementation of Lee's work as an R package. The Package is believed to be useful for moment calculations in most practical statistical problems.

• Communications of Mathematical Education
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• v.21 no.2 s.30
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• pp.227-270
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• 2007

We described the overall explanation about R statistical package in Jang(2007). With referring the contents of the 7th national mathematics curriculum, we suggest the plan for applications of R package on probability and statistics education in elementary, middle and high school mathematics.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.369-391
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• 2021

The vlda is an R (R Development Core team et al., 2011) package which provides functions for visualization of multidimensional longitudinal data. In particular, the R package vlda was developed to assist in producing a plot that more effectively expresses changes over time for two different types (long format and wide format) and uses a consistent calling scheme for longitudinal data. The main features of this package allow us to identify the relationship between categories and objects using an indicator matrix with object information, as well as to cluster objects. The R package vlda can be used to understand trends in observations over time in addition to identifying relative relationships at a simple visualization level. It also offers a new interactive implementation to perform additional interpretation, therefore it is useful for longitudinal data visual analysis. Due to the synergistic relationship between the existing VLDA plot and interactive features, the user is empowered by a refined observe the visual aspects of the VLDA plot layout. Furthermore, it allows the projection of supplementary information (supplementary objects and variables) that often occurs in longitudinal data of graphs. In this study, practical examples are provided to highlight the implemented methods of real applications.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.1
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• pp.109-114
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• 2010

In this paper, we developed the web content of statistical analysis using statistical package and Active Server Page (ASP). A statistical package is very difficult to learn and use for non-statisticians, however, non-statisticians want to do analyze the data without learning statistical packages such as SAS, S-plus, and R. Therefore, we developed the web based statistical analysis contents using S-plus which is the popular statistical package and ASP. In real application, we developed the web content for various statistical analyses such as exploratory data analysis, analysis of variance, and time series on the web using water quality data. The developed statistical analysis web content is very useful for non-statisticians such as public service person and researcher. Consequently, combining a web based contents with a statistical package, the users can access the site quickly and analyze data easily.

• Knowledge Management Research
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• v.20 no.2
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• pp.1-24
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• 2019

This tutorial introduces procedures and methods for performing structural equation modeling using R. To do this, we present advanced analysis methods based on structural equation model such as mediation effect analysis, moderation effect analysis, moderated mediation effect analysis, and multiple-group analysis with R program code using R lavaan package that supports structural equation modeling. R is flexible and scalable, unlike traditional commercial statistical packages. Therefore, new analytical techniques are likely to be implemented ahead of any other statistical package. From this point of view, R will be a very appropriate choice for applying new analytical techniques or advanced techniques that researchers need. Considering that various studies in the social sciences are applying structural equations modeling techniques and increasing interest in open source R, this tutorial is expected to be useful for researchers who are looking for alternatives to existing commercial statistical packages.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.179-189
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• 2024

In multivariate regression with a high-dimensional response Y ∈ ℝ^r and a relatively low-dimensional predictor X ∈ ℝ^p (where r ≥ 2), the statistical analysis of such data presents significant challenges due to the exponential increase in the number of parameters as the dimension of the response grows. Most existing dimension reduction techniques primarily focus on reducing the dimension of the predictors (X), not the dimension of the response variable (Y). Yoo and Cook (2008) introduced a response dimension reduction method that preserves information about the conditional mean E(Y | X). Building upon this foundational work, Yoo (2018) proposed two semi-parametric methods, principal response reduction (PRR) and principal fitted response reduction (PFRR), then expanded these methods to unstructured principal fitted response reduction (UPFRR) (Yoo, 2019). This paper reviews these four response dimension reduction methodologies mentioned above. In addition, it introduces the implementation of the mbrdr package in R. The mbrdr is a unique tool in the R community, as it is specifically designed for response dimension reduction, setting it apart from existing dimension reduction packages that focus solely on predictors.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.665-674
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• 2015

Lack of a general matrix formula hampers implementation of the semi-partial correlation, also known as part correlation, to the higher-order coefficient. This is because the higher-order semi-partial correlation calculation using a recursive formula requires an enormous number of recursive calculations to obtain the correlation coefficients. To resolve this difficulty, we derive a general matrix formula of the semi-partial correlation for fast computation. The semi-partial correlations are then implemented on an R package ppcor along with the partial correlation. Owing to the general matrix formulas, users can readily calculate the coefficients of both partial and semi-partial correlations without computational burden. The package ppcor further provides users with the level of the statistical significance with its test statistic.