Miscellaneous functions for (1) data management (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures), (3) missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), (4) multilevel data (e.g., multilevel descriptive statistics, within-group and between-group correlation matrix, multilevel confirmatory factor analysis, level-specific fit indices, cross-level measurement equivalence evaluation, multilevel composite reliability, and multilevel R-squared measures), (5) item analysis (e.g., confirmatory factor analysis, coefficient alpha and omega, between-group and longitudinal measurement equivalence evaluation), (6) statistical analysis (e.g., bootstrap confidence intervals, collinearity and residual diagnostics, dominance analysis, between- and within-subject analysis of variance, latent class analysis, t-test, z-test, sample size determination), and (7) functions to interact with Blimp and Mplus'.

We provide the framework to analyze multiresolution partitions (e.g. country, provinces, subdistrict) where each individual data point belongs to only one partition in each layer (e.g. i belongs to subdistrict A, province P, and country Q). We assume that a partition in a higher layer subsumes lower-layer partitions (e.g. a nation is at the 1st layer subsumes all provinces at the 2nd layer). Given N individuals that have a pair of real values (x,y) that generated from independent variable X and dependent variable Y. Each individual i belongs to one partition per layer. Our goal is to find which partitions at which highest level that all individuals in the these partitions share the same linear model Y=f(X) where f is a linear function. The framework deploys the Minimum Description Length principle (MDL) to infer solutions. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2021) <doi:10.1145/3424670>.

The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata.

We provide a computationally efficient and robust implementation of the recently proposed C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes) method (Konigorski et al., 2019, submitted). C-JAMP allows estimating and testing the association of one or multiple predictors on multiple outcomes in a joint model, and is implemented here with a focus on large-scale genome-wide association studies with two phenotypes. The use of copula functions allows modeling a wide range of multivariate dependencies between the phenotypes, and previous results are supporting that C-JAMP can increase the power of association studies to identify associated genetic variants in comparison to existing methods (Konigorski, Yilmaz, Pischon, 2016, <DOI:10.1186/s12919-016-0045-6>; Konigorski, Yilmaz, Bull, 2014, <DOI:10.1186/1753-6561-8-S1-S72>). In addition to the C-JAMP functions, functions are available to generate genetic and phenotypic data, to compute the minor allele frequency (MAF) of genetic markers, and to estimate the phenotypic variance explained by genetic markers.

saseR is a highly performant and fast framework for aberrant expression and splicing analyses. The main functions are: \itemize\item \code\linkBamtoAspliCounts - Process BAM files to ASpli counts \item \code\linkconvertASpli - Get gene, bin or junction counts from ASpli SummarizedExperiment \item \code\linkcalculateOffsets - Create an offsets assays for aberrant expression or splicing analysis \item \code\linksaseRfindEncodingDim - Estimate the optimal number of latent factors to include when estimating the mean expression \item \code\linksaseRfit - Parameter estimation of the negative binomial distribution and compute p-values for aberrant expression and splicing For information upon how to use these functions, check out our vignette at \urlhttps://github.com/statOmics/saseR/blob/main/vignettes/Vignette.Rmd and the saseR paper: Segers, A. et al. (2023). Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses. bioRxiv. \urlhttps://doi.org/10.1101/2023.06.29.547014.

Companion package of Carrion-i-Silvestre & Sansó (2023): "Generalized Extreme Value Approximation to the CUMSUMQ Test for Constant Unconditional Variance in Heavy-Tailed Time Series". It implements the Modified Iterative Cumulative Sum of Squares Algorithm, which is an extension of the Iterative Cumulative Sum of Squares (ICSS) Algorithm of Inclan and Tiao (1994), and it checks for changes in the unconditional variance of a time series controlling for the tail index of the underlying distribution. The fourth order moment is estimated non-parametrically to avoid the size problems when the innovations are non-Gaussian (see, Sansó et al., 2004). Critical values and p-values are generated using a Generalized Extreme Value distribution approach. References Carrion-i-Silvestre J.J & Sansó A (2023) <https://www.ub.edu/irea/working_papers/2023/202309.pdf>. Inclan C & Tiao G.C (1994) <doi:10.1080/01621459.1994.10476824>, Sansó A & Aragó V & Carrion-i-Silvestre J.L (2004) <https://dspace.uib.es/xmlui/bitstream/handle/11201/152078/524035.pdf>.

Fits heterogeneous panel data models with interactive effects for linear regression, logistic, count, probit, quantile, and clustering. Based on Ando, T. and Bai, J. (2015) "A simple new test for slope homogeneity in panel data models with interactive effects" <doi: 10.1016/j.econlet.2015.09.019>, Ando, T. and Bai, J. (2015) "Asset Pricing with a General Multifactor Structure" <doi: 10.1093/jjfinex/nbu026> , Ando, T. and Bai, J. (2016) "Panel data models with grouped factor structure under unknown group membership" <doi: 10.1002/jae.2467>, Ando, T. and Bai, J. (2017) "Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures" <doi: 10.1080/01621459.2016.1195743>, Ando, T. and Bai, J. (2020) "Quantile co-movement in financial markets" <doi: 10.1080/01621459.2018.1543598>, Ando, T., Bai, J. and Li, K. (2021) "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity" <doi: 10.1016/j.jeconom.2020.11.013.>.

The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) <arXiv:1202.3878> is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, <doi:10.1016/j.ins.2018.03.010>). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0.

Decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components. BFAST can be used to analyze different types of satellite image time series and can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics. The algorithm can be extended to label detected changes with information on the parameters of the fitted piecewise linear models. BFAST monitoring functionality is described in Verbesselt et al. (2010) <doi:10.1016/j.rse.2009.08.014>. BFAST monitor provides functionality to detect disturbance in near real-time based on BFAST'- type models, and is described in Verbesselt et al. (2012) <doi:10.1016/j.rse.2012.02.022>. BFAST Lite approach is a flexible approach that handles missing data without interpolation, and will be described in an upcoming paper. Furthermore, different models can now be used to fit the time series data and detect structural changes (breaks).

The network autocorrelation model (NAM) can be used for studying the degree of social influence regarding an outcome variable based on one or more known networks. The degree of social influence is quantified via the network autocorrelation parameters. In case of a single network, the Bayesian methods of Dittrich, Leenders, and Mulder (2017) <DOI:10.1016/j.socnet.2016.09.002> and Dittrich, Leenders, and Mulder (2019) <DOI:10.1177/0049124117729712> are implemented using a normal, flat, or independence Jeffreys prior for the network autocorrelation. In the case of multiple networks, the Bayesian methods of Dittrich, Leenders, and Mulder (2020) <DOI:10.1177/0081175020913899> are implemented using a multivariate normal prior for the network autocorrelation parameters. Flat priors are implemented for estimating the coefficients. For Bayesian testing of equality and order-constrained hypotheses, the default Bayes factor of Gu, Mulder, and Hoijtink, (2018) <DOI:10.1111/bmsp.12110> is used with the posterior mean and posterior covariance matrix of the NAM parameters based on flat priors as input.

This package provides the function feis() to estimate fixed effects individual slope (FEIS) models. The FEIS model constitutes a more general version of the often-used fixed effects (FE) panel model, as implemented in the package plm by Croissant and Millo (2008) <doi:10.18637/jss.v027.i02>. In FEIS models, data are not only person demeaned like in conventional FE models, but detrended by the predicted individual slope of each person or group. Estimation is performed by applying least squares lm() to the transformed data. For more details on FEIS models see Bruederl and Ludwig (2015, ISBN:1446252442); Frees (2001) <doi:10.2307/3316008>; Polachek and Kim (1994) <doi:10.1016/0304-4076(94)90075-2>; Ruettenauer and Ludwig (2020) <doi:10.1177/0049124120926211>; Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE and random effects estimators against heterogeneous slopes, the package also provides the functions feistest() for an artificial regression test and bsfeistest() for a bootstrapped version of the Hausman test.

The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to estimate the phylogenetic heritability of continuous traits, to test hypotheses of neutral evolution versus stabilizing selection, to quantify the strength of stabilizing selection, to estimate measurement error and to make predictions about the evolution of a phenotype and phenotypic variation in a population. The package implements combined maximum likelihood and Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed Model, fast parallel likelihood calculation, maximum likelihood inference of the genotypic values at the tips, functions for summarizing and plotting traces and posterior samples, functions for simulation of a univariate continuous trait evolution model along a phylogenetic tree. So far, the package has been used for estimating the heritability of quantitative traits in macroevolutionary and epidemiological studies, see e.g. Bertels et al. (2017) <doi:10.1093/molbev/msx246> and Mitov and Stadler (2018) <doi:10.1093/molbev/msx328>. The algorithm for parallel POUMM likelihood calculation has been published in Mitov and Stadler (2019) <doi:10.1111/2041-210X.13136>.

This package implements a set of routines to perform structured matrix factorization with minimum volume constraints. The NMF procedure decomposes a matrix X into a product C * D. Given conditions such that the matrix C is non-negative and has sufficiently spread columns, then volume minimization of a matrix D delivers a correct and unique, up to a scale and permutation, solution (C, D). This package provides both an implementation of volume-regularized NMF and "anchor-free" NMF, whereby the standard NMF problem is reformulated in the covariance domain. This algorithm was applied in Vladimir B. Seplyarskiy Ruslan A. Soldatov, et al. "Population sequencing data reveal a compendium of mutational processes in the human germ line". Science, 12 Aug 2021. <doi:10.1126/science.aba7408>. This package interacts with data available through the simulatedNMF package, which is available in a drat repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/vrnmf>. The size of the simulatedNMF package is approximately 8 MB.

This package provides automated downloading, parsing, cleaning, unit conversion and formatting of Global Surface Summary of the Day ('GSOD') weather data from the from the USA National Centers for Environmental Information ('NCEI'). Units are converted from from United States Customary System ('USCS') units to International System of Units ('SI'). Stations may be individually checked for number of missing days defined by the user, where stations with too many missing observations are omitted. Only stations with valid reported latitude and longitude values are permitted in the final data. Additional useful elements, saturation vapour pressure ('es'), actual vapour pressure ('ea') and relative humidity ('RH') are calculated from the original data using the improved August-Roche-Magnus approximation (Alduchov & Eskridge 1996) and included in the final data set. The resulting metadata include station identification information, country, state, latitude, longitude, elevation, weather observations and associated flags. For information on the GSOD data from NCEI', please see the GSOD readme.txt file available from, <https://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt>.

Implementation of the three-step approach of latent transition cognitive diagnosis model (CDM) with covariates. This approach can be used to assess changes in attribute mastery status and to evaluate the covariate effects on both the initial states and transition probabilities over time using latent logistic regression. Because stepwise approaches often yield biased estimates, correction for classification error probabilities (CEPs) is considered in this approach. The three-step approach for latent transition CDM with covariates involves the following steps: (1) fitting a CDM to the response data without covariates at each time point separately, (2) assigning examinees to latent states at each time point and computing the associated CEPs, and (3) estimating the latent transition CDM with the known CEPs and computing the regression coefficients. The method was proposed in Liang et al. (2023) <doi:10.3102/10769986231163320> and demonstrated using mental health data in Liang et al. (in press; annotated R code and data utilized in this example are available in Mendeley data) <doi:10.17632/kpjp3gnwbt.1>.

This package performs distance sampling simulations. dsims repeatedly generates instances of a user defined population within a given survey region. It then generates realisations of a survey design and simulates the detection process. The data are then analysed so that the results can be compared for accuracy and precision across all replications. This process allows users to optimise survey designs for their specific set of survey conditions. The effects of uncertainty in population distribution or parameters can be investigated under a number of simulations so that users can be confident that they have achieved a robust survey design before deploying vessels into the field. The distance sampling designs used in this package from dssd are detailed in Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 978-0199225873). General distance sampling methods are detailed in Introduction to Distance Sampling: Estimating Abundance of Biological Populations, Buckland et. al. (2004, ISBN-13: 978-0198509271). Find out more about estimating animal/plant abundance with distance sampling at <https://distancesampling.org/>.

This data package contains four datasets of quantitative PCR (qPCR) amplification curves that were used as supplementary data in the research article by Sisti et al. (2010), <doi:10.1186/1471-2105-11-186>. The primary dataset comprises a ten-fold dilution series spanning copy numbers from 3.14 Ã 10^7 to 3.14 Ã 10^2, with twelve replicates per concentration. These samples are based on a pGEM-T Promega plasmid containing a 104 bp fragment of the mitochondrial gene NADH dehydrogenase 1 (MT-ND1), amplified using the ND1/ND2 primer pair. The remaining three datasets contain qPCR results in the presence of specific PCR inhibitors: tannic acid, immunoglobulin G (IgG), and quercetin, respectively, to assess their effects on the amplification process. These datasets are useful for researchers interested in PCR kinetics. The original raw data file is available as Additional File 1: <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-11-186/MediaObjects/12859_2009_3643_MOESM1_ESM.XLS>.

Mahalanobis-Taguchi (MT) system is a collection of multivariate analysis methods developed for the field of quality engineering. MT system consists of two families depending on their purpose. One is a family of Mahalanobis-Taguchi (MT) methods (in the broad sense) for diagnosis (see Woodall, W. H., Koudelik, R., Tsui, K. L., Kim, S. B., Stoumbos, Z. G., and Carvounis, C. P. (2003) <doi:10.1198/004017002188618626>) and the other is a family of Taguchi (T) methods for forecasting (see Kawada, H., and Nagata, Y. (2015) <doi:10.17929/tqs.1.12>). The MT package contains three basic methods for the family of MT methods and one basic method for the family of T methods. The MT method (in the narrow sense), the Mahalanobis-Taguchi Adjoint (MTA) methods, and the Recognition-Taguchi (RT) method are for the MT method and the two-sided Taguchi (T1) method is for the family of T methods. In addition, the Ta and Tb methods, which are the improved versions of the T1 method, are included.

This package provides a collection of functions to test and estimate Seemingly Unrelated Regression (usually called SUR) models, with spatial structure, by maximum likelihood and three-stage least squares. The package estimates the most common spatial specifications, that is, SUR with Spatial Lag of X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), SUR with Spatial Durbin Error Model (called SUR-SDEM), SUR with Spatial Autoregressive terms and Spatial Autoregressive Disturbances (called SUR-SARAR), SUR-SARAR with Spatial Lag of X regressors (called SUR-GNM) and SUR with Spatially Independent Model (called SUR-SIM). The methodology of these models can be found in next references Minguez, R., Lopez, F.A., and Mur, J. (2022) <doi:10.18637/jss.v104.i11> Mur, J., Lopez, F.A., and Herrera, M. (2010) <doi:10.1080/17421772.2010.516443> Lopez, F.A., Mur, J., and Angulo, A. (2014) <doi:10.1007/s00168-014-0624-2>.

An implementation for high-dimensional time series analysis methods, including factor model for vector time series proposed by Lam and Yao (2012) <doi:10.1214/12-AOS970> and Chang, Guo and Yao (2015) <doi:10.1016/j.jeconom.2015.03.024>, martingale difference test proposed by Chang, Jiang and Shao (2023) <doi:10.1016/j.jeconom.2022.09.001>, principal component analysis for vector time series proposed by Chang, Guo and Yao (2018) <doi:10.1214/17-AOS1613>, cointegration analysis proposed by Zhang, Robinson and Yao (2019) <doi:10.1080/01621459.2018.1458620>, unit root test proposed by Chang, Cheng and Yao (2022) <doi:10.1093/biomet/asab034>, white noise test proposed by Chang, Yao and Zhou (2017) <doi:10.1093/biomet/asw066>, CP-decomposition for matrix time series proposed by Chang et al. (2023) <doi:10.1093/jrsssb/qkac011> and Chang et al. (2024) <doi:10.48550/arXiv.2410.05634>, and statistical inference for spectral density matrix proposed by Chang et al. (2022) <doi:10.48550/arXiv.2212.13686>.

This package provides a prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events.

The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (<https://www.cmsaf.eu>). It is a joint cooperation of eight National Meteorological and Hydrological Services. The cmsaf R-package includes a shiny based interface for an easy application of the cmsafops and cmsafvis packages - the CM SAF R Toolbox. The Toolbox offers an easy way to prepare, manipulate, analyse and visualize CM SAF NetCDF formatted data. Other CF conform NetCDF data with time, longitude and latitude dimension should be applicable, but there is no guarantee for an error-free application. CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu/safira>). Detailed information and test data are provided on the CM SAF webpage (<http://www.cmsaf.eu/R_toolbox>).

This package provides a glycolipid mass spectrometry technology has the potential to accurately identify individual bacterial species from polymicrobial samples. To develop bacterial identification algorithms (e.g. machine learning) using this glycolipid technology, it is necessary to generate a large number of various in-silico polymicrobial mass spectra that are similar to real mass spectra. MGMS2 (Membrane Glycolipid Mass Spectrum Simulator) generates such in-silico mass spectra, considering errors in m/z (mass-to-charge ratio) and variances of intensity values, occasions of missing signature ions, and noise peaks. It estimates summary statistics of monomicrobial mass spectra for each strain or species and simulates polymicrobial glycolipid mass spectra using the summary statistics of monomicrobial mass spectra. References: Ryu, S.Y., Wendt, G.A., Chandler, C.E., Ernst, R.K. and Goodlett, D.R. (2019) <doi:10.1021/acs.analchem.9b03340> "Model-based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids." Gibb, S. and Strimmer, K. (2012) <doi:10.1093/bioinformatics/bts447> "MALDIquant: a versatile R package for the analysis of mass spectrometry data.".

NuPoP is an R package for Nucleosome Positioning Prediction.This package is built upon a duration hidden Markov model proposed in Xi et al, 2010; Wang et al, 2008. The core of the package was written in Fotran. In addition to the R package, a stand-alone Fortran software tool is also available at https://github.com/jipingw. The Fortran codes have complete functonality as the R package. Note: NuPoP has two separate functions for prediction of nucleosome positioning, one for MNase-map trained models and the other for chemical map-trained models. The latter was implemented for four species including yeast, S.pombe, mouse and human, trained based on our recent publications. We noticed there is another package nuCpos by another group for prediction of nucleosome positioning trained with chemicals. A report to compare recent versions of NuPoP with nuCpos can be found at https://github.com/jiping/NuPoP_doc. Some more information can be found and will be posted at https://github.com/jipingw/NuPoP.