Read Japanese city codes (<https://www.e-stat.go.jp/municipalities/cities>) 
    to get city and prefecture names, or convert to city codes at different 
    points in time. In addition, it merges or splits wards of designated cities
    and gets all city codes at a specific point in time.

This package provides an easy and simple way to read, write and display bitmap images stored in the JPEG format. It can read and write both files and in-memory raw vectors.

A Joint PENalty Estimation of Covariance and Inverse Covariance Matrices.

Provides functions for grid square codes in Japan
    (<https://www.stat.go.jp/english/data/mesh/index.html>).
    Generates the grid square codes from longitude/latitude, geometries, and 
    the grid square codes of different scales, and vice versa.

Helpful functions for using mesh code (80km to 100m) data in Japan. Visualize mesh code using 'ggplot2' and 'leaflet', etc.

Provides tools to use API such as 'e-Stat' (<https://www.e-stat.go.jp/>), 
    the portal site for Japanese government statistics, and 
    'RESAS' (Regional Economy and Society Analyzing System, <https://resas.go.jp>).

A highly configurable 'jQuery' plugin offering a simple
    interface to create complex queries/filters in 'Shiny'. The outputted
    rules can easily be parsed into a set of 'R' and/or 'SQL' queries and
    used to filter data. Custom parsing of the rules is also supported.
    For more information about 'jQuery QueryBuilder' see
    <https://querybuilder.js.org/>.

We provide tools to estimate the individualized interval-valued dose rule (I2DR) that maximizes the expected beneficial clinical outcome for each individual and returns an optimal interval-valued dose, by using the jump Q-learning (JQL) method. The jump Q-learning method directly models the conditional mean of the response given the dose level and the baseline covariates via jump penalized least squares regression under the framework of Q learning. We develop a searching algorithm by dynamic programming in order to find the optimal I2DR with the time complexity O(n2) and spatial complexity O(n). To alleviate the effects of misspecification of the Q-function, a residual jump Q-learning is further proposed to estimate the optimal I2DR. The outcome of interest includes the best partition of the entire dosage of interest, the regression coefficients of each partition, and the value function under the estimated I2DR as well as the Wald-type confidence interval of value function constructed through the Bootstrap.