This package provides model fitting, prediction, and plotting for joint models of longitudinal and multiple time-to-event data, including methods from Rizopoulos (2012) <doi:10.1201/b12208>. Useful for handling complex survival and longitudinal data in clinical research.

Offer procedures to download financial-economic time series data and enhanced procedures for computing the investment performance indices of Bacon (2004) <DOI:10.1002/9781119206309>.

This package provides a modern, tidy interface to the Jira REST API for both Jira Cloud and Jira Server / Data Center. Authenticate once, query issues with the Jira Query Language (JQL), and retrieve projects, fields, dashboards and more as tibbles. Built on httr2 with automatic pagination, informative errors and support for API tokens and personal access tokens.

This package provides a gridded classification of weather types by applying the Jenkinson and Collison classification. For a given region (it can be either local region or the whole map),it computes at each grid the 11 weather types during the period considered for the analysis. See Otero et al., (2017) <doi:10.1007/s00382-017-3705-y> for more information.

An R-safe profile of RFC 8259 JSON: parsing and generation backed by the Jansson C library, linked as a system library where one is available and compiled from the bundled sources otherwise. The parser rejects, with classed conditions carrying line, column, and byte position: malformed or truncated input, trailing content, duplicate object keys at any depth, invalid UTF-8, escapes encoding a null character, reals overflowing double, and integer literals whose magnitude exceeds 2^53, the range within which a double represents every integer exactly. Number literals with a fraction or exponent convert by ordinary correctly rounded IEEE 754 double conversion. Objects decode to named lists in key order, arrays to unnamed lists, and scalars to length-one vectors. The encoder maps named lists to objects in insertion order, unnamed lists to arrays, guarantees that every finite double, signed zero included, round-trips to the exact same value (whole-number doubles are written as integers), and refuses values with no faithful JSON representation (NA, NaN, infinities, named atomic vectors, classed objects) instead of guessing. No R package dependencies.

This package provides boundary GeoPackage files used by the jpmap package, including Japan prefecture example boundaries and official MLIT N03 administrative area data converted for jpmap'. Keeping these data in a separate package lets jpmap update its functionality without repeatedly redistributing large boundary files on CRAN mirrors.

This package provides tools for using the API of e-Stat (<https://www.e-stat.go.jp/>), a portal site for Japanese government statistics. Includes functions for automatic query generation, data collection and formatting.

This package provides a set of helper functions to conduct joint-significance tests for mediation analysis, as recommended by Yzerbyt, Muller, Batailler, & Judd. (2018) <doi:10.1037/pspa0000132>.

Shared parameter models for the joint modeling of longitudinal and time-to-event data.

This package provides a RStudio addin to send some JavaScript code to the V8 console. The user can send an entire JavaScript file or only some selected lines. This is useful to test the code.

Aids in the calculation and visualization of regions of non-significance using the Johnson-Neyman technique and its extensions as described by Bauer and Curran (2005) <doi:10.1207/s15327906mbr4003_5> to assess the influence of categorical and continuous moderators. Allows correcting for phylogenetic relatedness.

Jade Lizard and Reverse Jade Lizard Option Strategies are presented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Russell A. Stultz (â The option strategy desk reference: an essential reference for option traders (First edition.)â , 2019, ISBN: 9781949443912).

Analysis of repeated measurements and time-to-event data via random effects joint models. Fits the joint models proposed by Henderson and colleagues <doi:10.1093/biostatistics/1.4.465> (single event time) and by Williamson and colleagues (2008) <doi:10.1002/sim.3451> (competing risks events time) to a single continuous repeated measure. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-varying covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by a latent Gaussian process. The model is estimated using an Expectation Maximization algorithm. Some plotting functions and the variogram are also included. This project is funded by the Medical Research Council (Grant numbers G0400615 and MR/M013227/1).

Implementation of some unit and area level EBLUP estimators as well as the estimators of their MSE also under heteroscedasticity. The package further documents the publications Breidenbach and Astrup (2012) <DOI:10.1007/s10342-012-0596-7>, Breidenbach et al. (2016) <DOI:10.1016/j.rse.2015.07.026> and Breidenbach et al. (2018 in press). The vignette further explains the use of the implemented functions.

This package performs power calculations for joint modeling of longitudinal and survival data with k-th order trajectories when the variance-covariance matrix, Sigma_theta, is unknown.

Understanding the current status of forest resources is essential for monitoring changes in forest ecosystems and generating related statistics. In South Korea, the National Forest Inventory (NFI) surveys over 4,500 sample plots nationwide every five years and records 70 items, including forest stand, forest resource, and forest vegetation surveys. Many researchers use NFI as the primary data for research, such as biomass estimation or analyzing the importance value of each species over time and space, depending on the research purpose. However, the large volume of accumulated forest survey data from across the country can make it challenging to manage and utilize such a vast dataset. To address this issue, we developed an R package that efficiently handles large-scale NFI data across time and space. The package offers a comprehensive workflow for NFI data analysis. It starts with data processing, where read_nfi() function reconstructs NFI data according to the researcher's needs while performing basic integrity checks for data quality.Following this, the package provides analytical tools that operate on the verified data. These include functions like summary_nfi() for summary statistics, diversity_nfi() for biodiversity analysis, iv_nfi() for calculating species importance value, and biomass_nfi() and cwd_biomass_nfi() for biomass estimation. Finally, for visualization, the tsvis_nfi() function generates graphs and maps, allowing users to visualize forest ecosystem changes across various spatial and temporal scales. This integrated approach and its specialized functions can enhance the efficiency of processing and analyzing NFI data, providing researchers with insights into forest ecosystems. The NFI Excel files (.xlsx) are not included in the R package and must be downloaded separately. Users can access these NFI Excel files by visiting the Korea Forest Service Forestry Statistics Platform <https://kfss.forest.go.kr/stat/ptl/article/articleList.do?curMenu=11694&bbsId=microdataboard> to download the annual NFI Excel files, which are bundled in .zip archives. Please note that this website is only available in Korean, and direct download links can be found in the notes section of the read_nfi() function.

Estimates kriging models for geographical point-referenced data. Method is described in Gill (2020) <doi:10.1177/1532440020930197>.

Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA() and KFPCA_reg(). Moreover, least square estimates of functional principal component scores are also provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <arXiv:2102.01286>. Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <doi:10.1016/j.jmva.2021.104864>.

Kendall random walks are a continuous-space Markov chains generated by the Kendall generalized convolution. This package provides tools for simulating these random walks and studying distributions related to them. For more information about Kendall random walks see Jasiulis-GoÅ dyn (2014) <arXiv:1412.0220>.

This package provides methods to extract information on pathways, genes and various single-nucleotid polymorphisms (SNPs) from online databases. It provides functions for data preparation and evaluation of genetic influence on a binary outcome using the logistic kernel machine test (LKMT). Three different kernel functions are offered to analyze genotype information in this variance component test: A linear kernel, a size-adjusted kernel and a network-based kernel).

Application of a Known Biomass Production Model (KBPM): (1) the fitting of KBPM to each stock; (2) the estimation of the effects of environmental variability; (3) the retrospective analysis to identify regime shifts; (4) the estimation of forecasts. For more details see Schaefer (1954) <https://www.iattc.org/GetAttachment/62d510ee-13d0-40f2-847b-0fde415476b8/Vol-1-No-2-1954-SCHAEFER,-MILNER-B-_Some-aspects-of-the-dynamics-of-populations-important-to-the-management-of-the-commercial-marine-fisheries.pdf>, Pella and Tomlinson (1969) <https://www.iattc.org/GetAttachment/9865079c-6ee7-40e2-9e30-c4523ff81ddf/Vol-13-No-3-1969-PELLA,-JEROME-J-,-and-PATRICK-K-TOMLINSON_A-generalized-stock-production-model.pdf> and MacCall (2002) <doi:10.1577/1548-8675(2002)022%3C0272:UOKBPM%3E2.0.CO;2>.

This package provides a wrapper for querying WISKI databases via the KiWIS REST API. WISKI is an SQL relational database used for the collection and storage of water data developed by KISTERS and KiWIS is a REST service that provides access to WISKI databases via HTTP requests (<https://www.kisters.eu/water-weather-and-environment/>). Contains a list of default databases (called hubs') and also allows users to provide their own KiWIS URL. Supports the entire query process- from metadata to specific time series values. All data is returned as tidy tibbles.

Offers a graphical user interface for the evaluation of inter-rater agreement with Cohen's and Fleiss Kappa. The calculation of kappa statistics is done using the R package irr', so that KappaGUI is essentially a Shiny front-end for irr'.

This package provides functions to search, retrieve, apply and update classification standards and code lists using Statistics Norway's API <https://www.ssb.no/klass> from the system KLASS'. Retrieves classifications by date with options to choose language, hierarchical level and formatting.