Small area estimation unit level models (Battese-Harter-Fuller model) with a Bayesian Hierarchical approach. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Battese et al. (1988) <doi:10.1080/01621459.1988.10478561>.

Using principal component analysis as a base model, SCOUTer offers a new approach to simulate outliers in a simple and precise way. The user can generate new observations defining them by a pair of well-known statistics: the Squared Prediction Error (SPE) and the Hotelling's T^2 (T^2) statistics. Just by introducing the target values of the SPE and T^2, SCOUTer returns a new set of observations with the desired target properties. Authors: Alba González, Abel Folch-Fortuny, Francisco Arteaga and Alberto Ferrer (2020).

This package provides a simple interface to recursively list files from a directory, filter them using a regular expression, read their contents, and extract lines that match a user-defined pattern. The package returns a dataframe containing the matched lines, their line numbers, file paths, and the corresponding matched substrings. Designed for quick code base exploration, log inspection, or any use case involving pattern-based file and line filtering.

This package provides the hyphenation algorithm used for TeX'/'LaTeX and similar software, as proposed by Liang (1983, <https://tug.org/docs/liang/>). Mainly contains the function hyphen() to be used for hyphenation/syllable counting of text objects. It was originally developed for and part of the koRpus package, but later released as a separate package so it's lighter to have this particular functionality available for other packages. Support for various languages needs be added on-the-fly or by plugin packages (<https://undocumeantit.github.io/repos/>); this package does not include any language specific data. Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, request features, or discuss the development of the package, please subscribe to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).

Fits linear regression models on datasets residing in SQL databases without pulling data into R memory. Computes sufficient statistics inside the database engine via a single aggregation query and solves the normal equations in R.

Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional data sets. The algorithm is built on the irwsva.build function of the sva package and proposes a revision on it that achieves an order of magnitude faster running time while trading no accuracy loss in return.

This package provides symmetric matrix and tensor operations and decomposition algorithms including symmetric NMF (symNMF), PageRank, Label Propagation, Higher-order Power Method, and TOPHITS. Designed to work with rTensor objects. Methods are described in Kuang et al. (2012) <doi:10.1137/1.9781611972825.10>, Kolda and Mayo (2011) <doi:10.1137/100801482>, and Kolda and Bader (2006) <doi:10.1137/1.9781611972764.26>.

Calculation methods of solar radiation and performance of photovoltaic systems from daily and intradaily irradiation data sources.

This package provides a coalescent simulator that allows the rapid simulation of biological sequences under neutral models of evolution, see Staab et al. (2015) <doi:10.1093/bioinformatics/btu861>. Different to other coalescent based simulations, it has an optional approximation parameter that allows for high accuracy while maintaining a linear run time cost for long sequences. It is optimized for simulating massive data sets as produced by Next- Generation Sequencing technologies for up to several thousand sequences.

This package implements numerous methods for testing for, modelling, and correcting for heteroskedasticity in the classical linear regression model. The most novel contribution of the package is found in the functions that implement the as-yet-unpublished auxiliary linear variance models and auxiliary nonlinear variance models that are designed to estimate error variances in a heteroskedastic linear regression model. These models follow principles of statistical learning described in Hastie (2009) <doi:10.1007/978-0-387-21606-5>. The nonlinear version of the model is estimated using quasi-likelihood methods as described in Seber and Wild (2003, ISBN: 0-471-47135-6). Bootstrap methods for approximate confidence intervals for error variances are implemented as described in Efron and Tibshirani (1993, ISBN: 978-1-4899-4541-9), including also the expansion technique described in Hesterberg (2014) <doi:10.1080/00031305.2015.1089789>. The wild bootstrap employed here follows the description in Davidson and Flachaire (2008) <doi:10.1016/j.jeconom.2008.08.003>. Tuning of hyper-parameters makes use of a golden section search function that is modelled after the MATLAB function of Zarnowiec (2022) <https://www.mathworks.com/matlabcentral/fileexchange/25919-golden-section-method-algorithm>. A methodological description of the algorithm can be found in Fox (2021, ISBN: 978-1-003-00957-3). There are 25 different functions that implement hypothesis tests for heteroskedasticity. These include a test based on Anscombe (1961) <https://projecteuclid.org/euclid.bsmsp/1200512155>, Ramsey's (1969) BAMSET Test <doi:10.1111/j.2517-6161.1969.tb00796.x>, the tests of Bickel (1978) <doi:10.1214/aos/1176344124>, Breusch and Pagan (1979) <doi:10.2307/1911963> with and without the modification proposed by Koenker (1981) <doi:10.1016/0304-4076(81)90062-2>, Carapeto and Holt (2003) <doi:10.1080/0266476022000018475>, Cook and Weisberg (1983) <doi:10.1093/biomet/70.1.1> (including their graphical methods), Diblasi and Bowman (1997) <doi:10.1016/S0167-7152(96)00115-0>, Dufour, Khalaf, Bernard, and Genest (2004) <doi:10.1016/j.jeconom.2003.10.024>, Evans and King (1985) <doi:10.1016/0304-4076(85)90085-5> and Evans and King (1988) <doi:10.1016/0304-4076(88)90006-1>, Glejser (1969) <doi:10.1080/01621459.1969.10500976> as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999) <doi:10.1080/07474939908800438>, Goldfeld and Quandt (1965) <doi:10.1080/01621459.1965.10480811>, Harrison and McCabe (1979) <doi:10.1080/01621459.1979.10482544>, Harvey (1976) <doi:10.2307/1913974>, Honda (1989) <doi:10.1111/j.2517-6161.1989.tb01749.x>, Horn (1981) <doi:10.1080/03610928108828074>, Li and Yao (2019) <doi:10.1016/j.ecosta.2018.01.001> with and without the modification of Bai, Pan, and Yin (2016) <doi:10.1007/s11749-017-0575-x>, Rackauskas and Zuokas (2007) <doi:10.1007/s10986-007-0018-6>, Simonoff and Tsai (1994) <doi:10.2307/2986026> with and without the modification of Ferrari, Cysneiros, and Cribari-Neto (2004) <doi:10.1016/S0378-3758(03)00210-6>, Szroeter (1978) <doi:10.2307/1913831>, Verbyla (1993) <doi:10.1111/j.2517-6161.1993.tb01918.x>, White (1980) <doi:10.2307/1912934>, Wilcox and Keselman (2006) <doi:10.1080/10629360500107923>, Yuce (2008) <https://dergipark.org.tr/en/pub/iuekois/issue/8989/112070>, and Zhou, Song, and Thompson (2015) <doi:10.1002/cjs.11252>. Besides these heteroskedasticity tests, there are supporting functions that compute the BLUS residuals of Theil (1965) <doi:10.1080/01621459.1965.10480851>, the conditional two-sided p-values of Kulinskaya (2008) <doi:10.48550/arXiv.0810.2124>, and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). For handling heteroskedasticity, in addition to the new auxiliary variance model methods, there is a function to implement various existing Heteroskedasticity-Consistent Covariance Matrix Estimators from the literature, such as those of White (1980) <doi:10.2307/1912934>, MacKinnon and White (1985) <doi:10.1016/0304-4076(85)90158-7>, Cribari-Neto (2004) <doi:10.1016/S0167-9473(02)00366-3>, Cribari-Neto et al. (2007) <doi:10.1080/03610920601126589>, Cribari-Neto and da Silva (2011) <doi:10.1007/s10182-010-0141-2>, Aftab and Chang (2016) <doi:10.18187/pjsor.v12i2.983>, and Li et al. (2017) <doi:10.1080/00949655.2016.1198906>.

This package creates SEER (Surveillance, Epidemiology and End Results) and A-bomb data binaries from ASCII sources and provides tools for estimating SEER second cancer risks. Methods are described in <doi:10.1038/leu.2015.258>.

sqliter helps users, mainly data munging practioneers, to organize their sql calls in a clean structure. It simplifies the process of extracting and transforming data into useful formats.

Calculates the power and sample size for Cochran-Mantel-Haenszel tests. There are also several helper functions for working with probability, odds, relative risk, and odds ratio values.

This package provides a critical first step in systematic literature reviews and mining of academic texts is to identify relevant texts from a range of sources, particularly databases such as Web of Science or Scopus'. These databases often export in different formats or with different metadata tags. synthesisr expands on the tools outlined by Westgate (2019) <doi:10.1002/jrsm.1374> to import bibliographic data from a range of formats (such as bibtex', ris', or ciw') in a standard way, and allows merging and deduplication of the resulting dataset.

This package provides a system that computes metrics to assess the segmentation accuracy of geospatial data. These metrics calculate the discrepancy between segmented and reference objects, and indicate the segmentation accuracy. For more details on choosing evaluation metrics, we suggest seeing Costa et al. (2018) <doi:10.1016/j.rse.2017.11.024> and Jozdani et al. (2020) <doi:10.1016/j.isprsjprs.2020.01.002>.

Access, modify, aggregate and plot data from the Sapfluxnet project, the first global database of sap flow measurements.

Manipulating input and output files of the STICS crop model. Files are either JavaSTICS XML files or text files used by the model fortran executable. Most basic functionalities are reading or writing parameter names and values in both XML or text input files, and getting data from output files. Advanced functionalities include XML files generation from XML templates and/or spreadsheets, or text files generation from XML files by using xslt transformation.

Fast and efficient sampling from general univariate probability density functions. Implements a rejection sampling approach designed to take advantage of modern CPU caches and minimise evaluation of the target density for most samples. Many standard densities are internally implemented in C for high performance, with general user defined densities also supported. A paper describing the methodology will be released soon.

This package provides a simulation-based tool made to help researchers to become familiar with multilevel variations, and to build up sampling designs for their study. This tool has two main objectives: First, it provides an educational tool useful for students, teachers and researchers who want to learn to use mixed-effects models. Users can experience how the mixed-effects model framework can be used to understand distinct biological phenomena by interactively exploring simulated multilevel data. Second, it offers research opportunities to those who are already familiar with mixed-effects models, as it enables the generation of data sets that users may download and use for a range of simulation-based statistical analyses such as power and sensitivity analysis of multilevel and multivariate data [Allegue, H., Araya-Ajoy, Y.G., Dingemanse, N.J., Dochtermann N.A., Garamszegi, L.Z., Nakagawa, S., Reale, D., Schielzeth, H. and Westneat, D.F. (2016) <doi: 10.1111/2041-210X.12659>].

We designed this package to provides several functions for area and subarea level of small area estimation under Twofold Subarea Level Model using hierarchical Bayesian (HB) method with Univariate Normal distribution for variables of interest. Some dataset simulated by a data generation are also provided. The rjags package is employed to obtain parameter estimates using Gibbs Sampling algorithm. Model-based estimators involves the HB estimators which include the mean, the variation of mean, and the quantile. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Torabi and Rao (2014) <doi:10.1016/j.jmva.2014.02.001>, Leyla Mohadjer et al.(2007) <http://www.asasrms.org/Proceedings/y2007/Files/JSM2007-000559.pdf>, and Erciulescu et al.(2019) <doi:10.1111/rssa.12390>.

Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.

This package provides a flexible moving average algorithm for modeling drug exposure in pharmacoepidemiology studies as presented in the article: Ouchi, D., Giner-Soriano, M., Gómez-Lumbreras, A., Vedia Urgell, C.,Torres, F., & Morros, R. (2022). "Automatic Estimation of the Most Likely Drug Combination in Electronic Health Records Using the Smooth Algorithm : Development and Validation Study." JMIR medical informatics, 10(11), e37976. <doi:10.2196/37976>.

Simple and quick method of exporting the most often used survival analysis results to an Excel sheet.

This package provides statistical diagnostics to evaluate whether proxy indicators reliably represent an unobservable target construct. The main function senser() assesses proxies across multiple dimensions including monotonicity, information content, stability, distributional alignment, and potential bias risk. It prints a concise, interpretable summary suitable for analytical and policy-oriented assessment, without claiming causal inference.