Software

R packages published on GitHub, plus browser-based HTML apps and a server-hosted web app — no installation required.

R packages

nmfkc

Non-Negative Matrix Factorization with Kernel Covariates. Available on CRAN.

Docs GitHub CRAN

ljmds

Local Jaccard MDS for Longitudinal Binary Data.

Docs GitHub

nolap

Non-overlapping arrangement of scatter-plot points.

Docs GitHub

pord

Exact conditional tests for paired ordinal data recorded on the same K-point scale (e.g. expectation vs. achievement). A generalization of Fisher's exact test conditioning on both margins, with the exact null distribution obtained by dynamic programming.

GitHub

HTML apps

All run without installation. Your data is processed in the browser and never uploaded. Each app has its own Japanese/English toggle.

From text to visuals — connected by one CSV

First, the Text analysis app builds a document-feature matrix (DFM) from your text and exports it as CSV. That same CSV loads directly into the three apps below — taking you from raw text to word clouds, co-occurrence networks, biplots, and group-wise topics without writing any code.

Text analysis (build DFM) → ① DFM visualization ② Correspondence analysis ③ NMF · Topic model

Text analysis (Document-Feature Matrix)

Tokenize Japanese text and build a document-feature matrix (DFM). Detect collocations, remove or replace words, and export to CSV.

Open app GitHub

DFM visualization (word cloud & co-occurrence network)

Loads the DFM CSV exported by the Text analysis app and draws a word cloud and a co-occurrence network. Adjust the number of words shown, thresholds, and community-detection methods interactively.

Open app GitHub

Correspondence analysis

Loads a DFM with attribute columns (from the Text analysis app), applies correspondence analysis to the attribute × word crosstab, and draws a biplot placing categories and words on the same plane.

Open app GitHub

Non-negative Matrix Factorization (NMF · Topic model)

Runs non-negative matrix factorization (NMF). The Topic model tab reads a DFM with attribute columns (from the Text analysis app) and shows topic composition by group; the Matrix factorization tab takes any matrix and shows heatmaps & two-way clustering.

Open app GitHub

Classification tree (CART)

Build a CART classification tree and view decision rules, a confusion matrix, and variable importance.

Open app GitHub

Multiple linear regression

Fits a multiple linear regression model by ordinary least squares (OLS). Categorical (text) features are auto-detected and can be dummy-coded. Output is formatted like R's summary(lm(...)).

Open app GitHub

Web apps

Apps that run on an external server (R Shiny).

Regression & Classification Tree (CART)

An R Shiny app that builds regression and classification trees (CART).

Open app