An API for automatic data queries to the fedstat <https://www.fedstat.ru>, using a small set of functions with a common interface.

This package provides a simple tool for the bulk creation and testing of parametric survival models. Simply provide fssg with a formula and some data, and let it identify the best distributions for you.

R API client package for Fingrid Open Data <https://data.fingrid.fi/> on the electricity market and the power system. get_data() function holds the main application logic to retrieve time-series data. API calls require free user account registration. Data is made available by Fingrid Oyj and distributed under Creative Commons 4.0 <https://creativecommons.org/licenses/by/4.0/>.

In order to achieve accurate estimation without sparsity assumption on the precision matrix, element-wise inference on the precision matrix, and joint estimation of multiple Gaussian graphical models, a novel method is proposed and efficient algorithm is implemented. FLAG() is the main function given a data matrix, and FlagOneEdge() will be used when one pair of random variables are interested where their indices should be given. Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications, see Qian Y (2023) <doi:10.14711/thesis-991013223054603412>, Qian Y, Hu X, Yang C (2023) <doi:10.48550/arXiv.2306.17584>.

This package provides a collection of datasets essential for functional genomic analysis. Gene names, gene positions, cytoband information, sourced from Ensembl and phenotypes association graph prepared from GWAScatalog are included. Data is available in both GRCh37 and 38 builds. These datasets facilitate a wide range of genomic studies, including the identification of genetic variants, exploration of genomic features, and post-GWAS functional analysis.

This method is a new class of model selection strategies, for mixed model selection, which includes linear and generalized linear mixed models. The idea involves a procedure to isolate a subgroup of what are known as correct models (of which the optimal model is a member). This is accomplished by constructing a statistical fence, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from among those within the fence according to a criterion which can be made flexible. References: 1. Jiang J., Rao J.S., Gu Z., Nguyen T. (2008), Fence Methods for Mixed Model Selection. The Annals of Statistics, 36(4): 1669-1692. <DOI:10.1214/07-AOS517> <https://projecteuclid.org/euclid.aos/1216237296>. 2. Jiang J., Nguyen T., Rao J.S. (2009), A Simplified Adaptive Fence Procedure. Statistics and Probability Letters, 79, 625-629. <DOI:10.1016/j.spl.2008.10.014> <https://www.researchgate.net/publication/23991417_A_simplified_adaptive_fence_procedure> 3. Jiang J., Nguyen T., Rao J.S. (2010), Fence Method for Nonparametric Small Area Estimation. Survey Methodology, 36(1), 3-11. <http://publications.gc.ca/collections/collection_2010/statcan/12-001-X/12-001-x2010001-eng.pdf>. 4. Jiming Jiang, Thuan Nguyen and J. Sunil Rao (2011), Invisible fence methods and the identification of differentially expressed gene sets. Statistics and Its Interface, Volume 4, 403-415. <http://www.intlpress.com/site/pub/files/_fulltext/journals/sii/2011/0004/0003/SII-2011-0004-0003-a014.pdf>. 5. Thuan Nguyen & Jiming Jiang (2012), Restricted fence method for covariate selection in longitudinal data analysis. Biostatistics, 13(2), 303-314. <DOI:10.1093/biostatistics/kxr046> <https://academic.oup.com/biostatistics/article/13/2/303/263903/Restricted-fence-method-for-covariate-selection-in>. 6. Thuan Nguyen, Jie Peng, Jiming Jiang (2014), Fence Methods for Backcross Experiments. Statistical Computation and Simulation, 84(3), 644-662. <DOI:10.1080/00949655.2012.721885> <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3891925/>. 7. Jiang, J. (2014), The fence methods, in Advances in Statistics, Hindawi Publishing Corp., Cairo. <DOI:10.1155/2014/830821>. 8. Jiming Jiang and Thuan Nguyen (2015), The Fence Methods, World Scientific, Singapore. <https://www.abebooks.com/9789814596060/Fence-Methods-Jiming-Jiang-981459606X/plp>.

This contains functions that can be used to estimate a smoothed and a non-smoothed (empirical) time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve for correlated right-censored time-to-event data. See Beyene and Chen (2024) <doi:10.1177/09622802231220496>.

This package provides clean, tidy access to economic data from the Federal Reserve Economic Data ('FRED') API <https://fred.stlouisfed.org/docs/api/fred/>. FRED is maintained by the Federal Reserve Bank of St. Louis and contains over 800,000 time series from 118 sources covering GDP, employment, inflation, interest rates, trade, and more. Dedicated functions fetch series observations, search for series, browse categories, releases, and tags, and retrieve series metadata. Multiple series can be fetched in a single call, in long or wide format. Server-side unit transformations (percent change, log, etc.) and frequency aggregation are supported, with readable transform aliases such as yoy_pct and log_diff'. Real-time and vintage helpers (built on ALFRED') return a series as it appeared on a given date, the first-release version, every revision, or a panel of selected vintages. Data is cached locally for subsequent calls. This product uses the FRED API but is not endorsed or certified by the Federal Reserve Bank of St. Louis'.

An implementation of the methodology described in Petersen and Mueller (2016) <doi:10.1214/15-AOS1363> for the functional data analysis of samples of density functions. Densities are first transformed to their corresponding log quantile densities, followed by ordinary Functional Principal Components Analysis (FPCA). Transformation modes of variation yield improved interpretation of the variability in the data as compared to FPCA on the densities themselves. The standard fraction of variance explained (FVE) criterion commonly used for functional data is adapted to the transformation setting, also allowing for an alternative quantification of variability for density data through the Wasserstein metric of optimal transport.

An easy way to conduct flexible scan. Monte-Carlo method is used to test the spatial clusters given the cases, population, and shapefile. A table with formal style and a map with clusters are included in the result report. The method can be referenced at: Toshiro Tango and Kunihiko Takahashi (2005) <doi:10.1186/1476-072X-4-11>.

Inference methods for factor copula models for continuous data in Krupskii and Joe (2013) <doi:10.1016/j.jmva.2013.05.001>, Krupskii and Joe (2015) <doi:10.1016/j.jmva.2014.11.002>, Fan and Joe (2024) <doi:10.1016/j.jmva.2023.105263>, one factor truncated vine models in Joe (2018) <doi:10.1002/cjs.11481>, and Gaussian oblique factor models. Functions for computing tail-weighted dependence measures in Lee, Joe and Krupskii (2018) <doi:10.1080/10485252.2017.1407414> and estimating tail dependence parameter.

This package provides access to a range of functions for computing and visualizing the Full Bayesian Significance Test (FBST) and the e-value for testing a sharp hypothesis against its alternative, and the Full Bayesian Evidence Test (FBET) and the (generalized) Bayesian evidence value for testing a composite (or interval) hypothesis against its alternative. The methods are widely applicable as long as a posterior MCMC sample is available.

The free algebra in R with non-commuting indeterminates. Uses disordR discipline (Hankin, 2022, <doi:10.48550/ARXIV.2210.03856>). To cite the package in publications please use Hankin (2022) <doi:10.48550/ARXIV.2211.04002>.

This package provides a computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software SKAT and coxKM'. coxKM can be obtained from <https://github.com/lin-lab/coxKM>.

Package for time value of money calculation, time series analysis and computational finance.

An easy package for scraping and processing Australia Rules Football (AFL) data. fitzRoy provides a range of functions for accessing publicly available data from AFL Tables <https://afltables.com/afl/afl_index.html>, Footy Wire <https://www.footywire.com> and The Squiggle <https://squiggle.com.au>. Further functions allow for easy processing, cleaning and transformation of this data into formats that can be used for analysis.

The Clutter model is a significant forest growth simulation tool. Grounded on individual trees and comprehensively considering factors such as competition among trees and the impact of environmental elements on growth, it can accurately reflect the growth process of forest stands. It can be applied in areas like forest resource management, harvesting planning, and ecological research. With the help of the Clutter model, people can better understand the dynamic changes of forests and provide a scientific basis for rational forest management and protecting the ecological environment. This R package can effectively realize the construction of forest growth and harvest models based on the Clutter model and achieve optimized forest management.References: Farias A, Soares C, Leite H et al(2021)<doi:10.1007/s10342-021-01380-1>. Guera O, Silva J, Ferreira R, et al(2019)<doi:10.1590/2179-8087.038117>.

General linear modeling with multiple responses (MANCOVA). An overall p-value for each model term is calculated by the 50-50 MANOVA method by Langsrud (2002) <doi:10.1111/1467-9884.00320>, which handles collinear responses. Rotation testing, described by Langsrud (2005) <doi:10.1007/s11222-005-4789-5>, is used to compute adjusted single response p-values according to familywise error rates and false discovery rates (FDR). The approach to FDR is described in the appendix of Moen et al. (2005) <doi:10.1128/AEM.71.4.2086-2094.2005>. Unbalanced designs are handled by Type II sums of squares as argued in Langsrud (2003) <doi:10.1023/A:1023260610025>. Furthermore, the Type II philosophy is extended to continuous design variables as described in Langsrud et al. (2007) <doi:10.1080/02664760701594246>. This means that the method is invariant to scale changes and that common pitfalls are avoided.

Wrapper functions around the Facebook Marketing API to create, read, update and delete custom audiences, images, campaigns, ad sets, ads and related content.

Feature subset selection algorithms modularized in search algorithms and measure utilities.

This package provides a collection of functions for outlier detection in functional data analysis. Methods implemented include directional outlyingness by Dai and Genton (2019) <doi:10.1016/j.csda.2018.03.017>, MS-plot by Dai and Genton (2018) <doi:10.1080/10618600.2018.1473781>, total variation depth and modified shape similarity index by Huang and Sun (2019) <doi:10.1080/00401706.2019.1574241>, and sequential transformations by Dai et al. (2020) <doi:10.1016/j.csda.2020.106960 among others. Additional outlier detection tools and depths for functional data like functional boxplot, (modified) band depth etc., are also available.

The four-gamete test is based on the infinite-sites model which assumes that the probability of the same mutation occurring twice (recurrent or parallel mutations) and the probability of a mutation back to the original state (reverse mutations) are close to zero. Without these types of mutations, the only explanation for observing the four dilocus genotypes (example below) is recombination (Hudson and Kaplan 1985, Genetics 111:147-164). Thus, the presence of all four gametes is also called phylogenetic incompatibility.

This package provides a suite of methods for detecting influential subjects in longitudinal datasets, particularly when observations occur at irregular time points. The methods identify individuals whose response trajectories deviate significantly from the population pattern, enabling detection of anomalies or subjects exerting undue influence on model outcomes.

This package provides a small utility which wraps Rscript and provides access to all R functions from the shell.