Provided R functions for working with the Conditional Negative Binomial distribution.

A GUI with which users can construct and interact
        with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods.

The Chinese ID number contains a lot of information,
    this package helps you get the date of birth, age, age based
    on year, gender, region, zodiac, constellation information from
    the Chinese ID number.

Implementations of recent complex-valued wavelet shrinkage procedures for smoothing irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.

Implementations of recent complex-valued wavelet spectral procedures for analysis of irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.

According to the code or the name of the administrative division at the county level and above provided by the Ministry of Civil Affairs of the People's Republic of China in 2022 (<https://www.mca.gov.cn/mzsj/xzqh/2022/202201xzqh.html>), get the map file online from the website of AutoNavi Map (<http://datav.aliyun.com/portal/school/atlas/area_selector>).

Conventional methods for producing standard scores or percentiles 
    in psychometrics or biometrics are often plagued with 'jumps' or 'gaps' 
    (i.e., discontinuities) in norm tables and low confidence for assessing 
    extreme scores. The continuous norming method introduced by A. Lenhard et al.
    (2016, <doi:10.1177/1073191116656437>; 2019, <doi:10.1371/journal.pone.0222279>;
    2021 <doi: 10.1177/0013164420928457>) estimates percentile development 
    (e. g. over age) and generates continuous test norm scores on the basis of 
    the raw data from standardization samples, without requiring assumptions 
    about the distribution of the raw data: Norm scores are directly established 
    from raw data by modeling the latter ones as a function of both percentile 
    scores and an explanatory variable (e.g., age). The method minimizes bias 
    arising from sampling and measurement error, while handling marked deviations 
    from normality, addressing bottom or ceiling effects and capturing almost 
    all of the variance in the original norm data sample. It includes procedures 
    for post stratification of norm samples to overcome bias in data collection 
    and to mitigate violations of representativeness. An online demonstration is 
    available via <https://cnorm.shinyapps.io/cNORM/>.