Computes the Jackknife Mutual Information (JMI) between two random vectors and provides the p-value for dependence tests. See Zeng, X., Xia, Y. and Tong, H. (2018) <doi:10.1073/pnas.1715593115>.

An httpuv based bridge between R and JavaScript'. Provides an easy way to exchange commands and data between a web page and a currently running R session.

Fit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864).

The jscore() function in the package calculates the J-Score metric between two clustering assignments. The score is designed to address some problems with existing common metrics such as problem of matching. The details of J-score is described in Ahmadinejad and Liu. (2021) <arXiv:2109.01306>.

Uses the Jaccard similarity index to account for population structure in sequencing studies. This method was specifically designed to detect population stratification based on rare variants, hence it will be especially useful in rare variant analysis.

This package provides functions and data to reproduce all plots in the book "Practical Smoothing. The Joys of P-splines" by Paul H.C. Eilers and Brian D. Marx (2021, ISBN:978-1108482950).

Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).

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.

Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.

Allow to run jshint on JavaScript files with a R command or a RStudio addin. The report appears in the RStudio viewer pane.

This package performs Joins and Minus Queries on Excel Files fulljoinXL() Merges all rows of 2 Excel files based upon a common column in the files. innerjoinXL() Merges all rows from base file and join file when the join condition is met. leftjoinXL() Merges all rows from the base file, and all rows from the join file if the join condition is met. rightjoinXL() Merges all rows from the join file, and all rows from the base file if the join condition is met. minusXL() Performs 2 operations source-minus-target and target-minus-source If the files are identical all output files will be empty. Choose two Excel files via a dialog box, and then follow prompts at the console to choose a base or source file and columns to merge or minus on.

In a typical experiment for the intuitive judgment of frequencies (JoF) different stimuli with different frequencies are presented. The participants consider these stimuli with a constant duration and give a judgment of frequency. These judgments can be simulated by formal models: PASS 1 and PASS 2 based on Sedlmeier (2002, ISBN:978-0198508632), MINERVA 2 baesd on Hintzman (1984) <doi:10.3758/BF03202365> and TODAM 2 based on Murdock, Smith & Bai (2001) <doi:10.1006/jmps.2000.1339>. The package provides an assessment of the frequency by determining the core aspects of these four models (attention, decay, and presented frequency) that can be compared to empirical results.

This package provides a Joint PENalty Estimation of Covariance and Inverse Covariance Matrices.

Implementation of joint sparse optimization (JSparO) to infer the gene regulatory network for cell fate conversion. The proximal gradient method is implemented to solve different low-order regularization models for JSparO.

This package provides a suite of common statistical methods such as descriptives, t-tests, ANOVAs, regression, correlation matrices, proportion tests, contingency tables, and factor analysis. This package is also useable from the jamovi statistical spreadsheet (see <https://www.jamovi.org> for more information).

This package provides method used to check whether data have outlier in efficiency measurement of big samples with data envelopment analysis (DEA). In this jackstrap method, the package provides two criteria to define outliers: heaviside and k-s test. The technique was developed by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers." <doi:10.1007/s11123-005-4702-4>.

This is a collection of tools for more efficiently understanding and sharing the results of (primarily) regression analyses. There are also a number of miscellaneous functions for statistical and programming purposes. Support for models produced by the survey and lme4 packages are points of emphasis.

Maximum likelihood estimation for the semiparametric joint modeling of survival and longitudinal data. Refer to the Journal of Statistical Software article: <doi:10.18637/jss.v093.i02>.

The Impact Factor of a journal reported by Journal Citation Reports ('JCR') of Clarivate Analytics is provided. The impact factor is available for those journals only that were included Journal Citation Reports JCR'.

This package implements time series z-normalization, SAX, HOT-SAX, VSM, SAX-VSM, RePair, and RRA algorithms facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly), and characteristic pattern discovery along with interpretable time series classification.

Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death. Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model. The methods are applicable for meta-analytic data containing individual-patient information from several studies. Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). Methodologies were published in Emura et al. (2017) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>, Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>, Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>. See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>. Survival data from ovarian cancer patients are also available.

This package provides a small package containing functions to perform a joint calibration of totals and quantiles. The calibration for totals is based on Deville and Särndal (1992) <doi:10.1080/01621459.1992.10475217>, the calibration for quantiles is based on Harms and Duchesne (2006) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X20060019255>. The package uses standard calibration via the survey', sampling or laeken packages. In addition, entropy balancing via the ebal package and empirical likelihood based on codes from Wu (2005) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2005002/article/9051-eng.pdf> can be used. See the paper by BerÄ sewicz and Szymkowiak (2023) for details <arXiv:2308.13281>.

Simplifies the process of estimating above ground biomass components for teak trees using a few basic inputs, based on the equations taken from the journal "Allometric equations for estimating above ground biomass and leaf area of planted teak (Tectona grandis) forests under agroforestry management in East Java, Indonesia" (Purwanto & Shiba, 2006) <doi:10.60409/forestresearch.76.0_1>. This function is most reliable when applied to trees from the same region where the equations were developed, specifically East Java, Indonesia. This function help to estimate the stem diameter at the lowest major living branch (DB) using the stem diameter at breast height with R^2 = 0.969. Estimate the branch dry weight (WB) using the stem diameter at breast height and tree height (R^2 = 0.979). Estimate the stem weight (WS) using the stem diameter at breast height and tree height (R^2 = 0.997. Also estimate the leaf dry weight (WL) using the stem diameter at the lowest major living branch (R^2 = 0.996).

This package provides an interface to Jamendo API <https://developer.jamendo.com/v3.0>. Pull audio, features and other information for a given Jamendo user (including yourself!) or enter an artist's -, album's -, or track's name and retrieve the available information in seconds.