Implementations of the k-means, hierarchical agglomerative and DBSCAN clustering methods for functional data which allows for jointly aligning and clustering curves. It supports functional data defined on one-dimensional domains but possibly evaluating in multivariate codomains. It supports functional data defined in arrays but also via the fd and funData classes for functional data defined in the fda and funData packages respectively. It currently supports shift, dilation and affine warping functions for functional data defined on the real line and uses the SRVF framework to handle boundary-preserving warping for functional data defined on a specific interval. Main reference for the k-means algorithm: Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment for curve clustering" <doi:10.1016/j.csda.2009.12.008>. Main reference for the SRVF framework: Tucker, J. D., Wu, W., & Srivastava, A. (2013) "Generative models for functional data using phase and amplitude separation" <doi:10.1016/j.csda.2012.12.001>.

This package implements several methods to meta-analyze studies that report the sample median of the outcome. The methods described by McGrath et al. (2019) <doi:10.1002/sim.8013>, Ozturk and Balakrishnan (2020) <doi:10.1002/sim.8738>, and McGrath et al. (2020a) <doi:10.1002/bimj.201900036> can be applied to directly meta-analyze the median or difference of medians between groups. Additionally, a number of methods (e.g., McGrath et al. (2020b) <doi:10.1177/0962280219889080>, Cai et al. (2021) <doi:10.1177/09622802211047348>, and McGrath et al. (2023) <doi:10.1177/09622802221139233>) are implemented to estimate study-specific (difference of) means and their standard errors in order to estimate the pooled (difference of) means. Methods for meta-analyzing median survival times (McGrath et al. (2025) <doi:10.48550/arXiv.2503.03065>) are also implemented. See McGrath et al. (2024) <doi:10.1002/jrsm.1686> for a detailed guide on using the package.

The goal of checkpoint is to solve the problem of package reproducibility in R. Specifically, checkpoint allows you to install packages as they existed on CRAN on a specific snapshot date as if you had a CRAN time machine. To achieve reproducibility, the checkpoint() function installs the packages required or called by your project and scripts to a local library exactly as they existed at the specified point in time. Only those packages are available to your project, thereby avoiding any package updates that came later and may have altered your results. In this way, anyone using checkpoint's checkpoint() can ensure the reproducibility of your scripts or projects at any time. To create the snapshot archives, once a day (at midnight UTC) Microsoft refreshes the Austria CRAN mirror on the "Microsoft R Archived Network" server (<https://mran.microsoft.com/>). Immediately after completion of the rsync mirror process, the process takes a snapshot, thus creating the archive. Snapshot archives exist starting from 2014-09-17.

Circular genomic permutation approach uses genome wide association studies (GWAS) results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure(Cabrera et al (2012) <doi:10.1534/g3.112.002618>). All single nucleotide polymorphisms (SNPs) in the GWAS are placed in a circular genome according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses Fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values and SNPs annotation to genes. Pathways can be obtained from within the package or can be provided by the user.

This package provides tools for phase-type distributions including the following variants: continuous, discrete, multivariate, in-homogeneous, right-censored, and regression. Methods for functional evaluation, simulation and estimation using the expectation-maximization (EM) algorithm are provided for all models. The methods of this package are based on the following references. Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase-type distributions via the EM algorithm, Olsson, M. (1996). Estimation of phase-type distributions from censored data, Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>, Albrecher, H., Bladt, M., & Yslas, J. (2022) <doi:10.1111/sjos.12505>, Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) <doi:10.1016/j.insmatheco.2022.08.001>, Bladt, M., & Yslas, J. (2022) <doi:10.1080/03461238.2022.2097019>, Bladt, M. (2022) <doi:10.1017/asb.2021.40>, Bladt, M. (2023) <doi:10.1080/10920277.2023.2167833>, Albrecher, H., Bladt, M., & Mueller, A. (2023) <doi:10.1515/demo-2022-0153>, Bladt, M. & Yslas, J. (2023) <doi:10.1016/j.insmatheco.2023.02.008>.

Ranked Set Sampling (RSS) is a stratified sampling method known for its efficiency compared to Simple Random Sampling (SRS). When sample allocation is equal across strata, it is referred to as balanced RSS (BRSS) whereas unequal allocation is called unbalanced RSS (URSS), which is particularly effective for asymmetric or skewed distributions. This package offers practical statistical tools and sampling methods for both BRSS and URSS, emphasizing flexible sampling designs and inference for population means, medians, proportions, and Area Under the Curve (AUC). It incorporates parametric and nonparametric tests, including empirical likelihood ratio (LR) methods. The package provides ranked set sampling methods from a given population, including sampling with imperfect ranking using auxiliary variables. Furthermore, it provides tools for efficient sample allocation in URSS, ensuring greater efficiency than SRS and BRSS. For more details, refer e.g. to Chen et al. (2003) <doi:10.1007/978-0-387-21664-5>, Ahn et al. (2022) <doi:10.1007/978-3-031-14525-4_3>, and Ahn et al. (2024) <doi:10.1111/insr.12589>.

Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is logConDens that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.

The base apply function and its variants, as well as the related functions in the plyr package, typically apply user-defined functions to a single argument (or a list of vectorized arguments in the case of mapply). The multiApply package extends this paradigm with its only function, Apply, which efficiently applies functions taking one or a list of multiple unidimensional or multidimensional arrays (or combinations thereof) as input. The input arrays can have different numbers of dimensions as well as different dimension lengths, and the applied function can return one or a list of unidimensional or multidimensional arrays as output. This saves development time by preventing the R user from writing often error-prone and memory-inefficient loops dealing with multiple complex arrays. Also, a remarkable feature of Apply is the transparent use of multi-core through its parameter ncores'. In contrast to the base apply function, this package suggests the use of target dimensions as opposite to the margins for specifying the dimensions relevant to the function to be applied.

Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper, Wasserstein-2 or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances,and count data with the chi-square distance. Varietal data can either be converted to one or more distributional datasets, or directly compared using the multivariate Wasserstein distance. Also included are tools to plot compositional and count data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R.

Facilitates the import and analysis of SNP (single nucleotide polymorphism') and silicodart (presence/absence) data. The main focus is on data generated by DarT (Diversity Arrays Technology), however, data from other sequencing platforms can be used once SNP or related fragment presence/absence data from any source is imported. Genetic datasets are stored in a derived genlight format (package adegenet'), that allows for a very compact storage of data and metadata. Functions are available for importing and exporting of SNP and silicodart data, for reporting on and filtering on various criteria (e.g. callrate', heterozygosity', reproducibility', maximum allele frequency). Additional functions are available for visualization (e.g. Principle Coordinate Analysis) and creating a spatial representation using maps. dartR.base is the base package of the dartRverse suits of packages. To install the other packages, we recommend to install the dartRverse package, that supports the installation of all packages in the dartRverse'. If you want to cite dartR', you find the information by typing citation('dartR.base') in the console.

Recent gcc and clang compiler versions provide functionality to test for memory violations and other undefined behaviour; this is often referred to as "Address Sanitizer" (or ASAN') and "Undefined Behaviour Sanitizer" ('UBSAN'). The Writing R Extension manual describes this in some detail in Section 4.3 title "Checking Memory Access". . This feature has to be enabled in the corresponding binary, eg in R, which is somewhat involved as it also required a current compiler toolchain which is not yet widely available, or in the case of Windows, not available at all (via the common Rtools mechanism). . As an alternative, pre-built Docker containers such as the Rocker container r-devel-san or the multi-purpose container r-debug can be used. . This package then provides a means of testing the compiler setup as the known code failures provides in the sample code here should be detected correctly, whereas a default build of R will let the package pass. . The code samples are based on the examples from the Address Sanitizer Wiki at <https://github.com/google/sanitizers/wiki>.

This package provides a Bayesian approach to using predictive probability in an ANOVA construct with a continuous normal response, when threshold values must be obtained for the question of interest to be evaluated as successful (Sieck and Christensen (2021) <doi:10.1002/qre.2802>). The Bayesian Mission Mean (BMM) is used to evaluate a question of interest (that is, a mean that randomly selects combination of factor levels based on their probability of occurring instead of averaging over the factor levels, as in the grand mean). Under this construct, in contrast to a Gibbs sampler (or Metropolis-within-Gibbs sampler), a two-stage sampling method is required. The nested sampler determines the conditional posterior distribution of the model parameters, given Y, and the outside sampler determines the marginal posterior distribution of Y (also commonly called the predictive distribution for Y). This approach provides a sample from the joint posterior distribution of Y and the model parameters, while also accounting for the threshold value that must be obtained in order for the question of interest to be evaluated as successful.

This package provides methods for analyzing (cell) motion in two or three dimensions. Available measures include displacement, confinement ratio, autocorrelation, straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks, steps, or subtracks with varying length. While the methodology has been developed for cell trajectory analysis, it is applicable to anything that moves including animals, people, or vehicles. Some of the methodology implemented in this packages was described by: Beauchemin, Dixit, and Perelson (2007) <doi:10.4049/jimmunol.178.9.5505>, Beltman, Maree, and de Boer (2009) <doi:10.1038/nri2638>, Gneiting and Schlather (2004) <doi:10.1137/S0036144501394387>, Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) <doi:10.1371/journal.pone.0080808>, Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) <doi:10.1016/j.immuni.2012.05.014>, Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) <doi:10.1073/pnas.1102288108>, Textor, Sinn, and de Boer (2013) <doi:10.1186/1471-2105-14-S6-S10>, Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) <doi:10.1371/journal.pcbi.1003752>.

Takes as input a stable oxygen isotope (d18O) profile measured in growth direction (D) through a shell + uncertainties in both variables (d18O_err & D_err). It then models the seasonality in the d18O record by fitting a combination of a growth and temperature sine wave to year-length chunks of the data (see Judd et al., (2018) <doi:10.1016/j.palaeo.2017.09.034>). This modeling is carried out along a sliding window through the data and yields estimates of the day of the year (Julian Day) and local growth rate for each data point. Uncertainties in both modeling routine and the data itself are propagated and pooled to obtain a confidence envelope around the age of each data point in the shell. The end result is a shell chronology consisting of estimated ages of shell formation relative to the annual cycle with their uncertainties. All formulae in the package serve this purpose, but the user can customize the model (e.g. number of days in a year and the mineralogy of the shell carbonate) through input parameters.

This package provides functions for basic hydraulic calculations related to water flow in circular pipes both flowing full (under pressure), and partially full (gravity flow), and trapezoidal open channels. For pressure flow this includes friction loss calculations by solving the Darcy-Weisbach equation for head loss, flow or diameter, plotting a Moody diagram, matching a pump characteristic curve to a system curve, and solving for flows in a pipe network using the Hardy-Cross method. The Darcy-Weisbach friction factor is calculated using the Colebrook (or Colebrook-White equation), the basis of the Moody diagram, the original citation being Colebrook (1939) <doi:10.1680/ijoti.1939.13150>. For gravity flow, the Manning equation is used, again solving for missing parameters. The derivation of and solutions using the Darcy-Weisbach equation and the Manning equation are outlined in many fluid mechanics texts such as Finnemore and Maurer (2024, ISBN:978-1-264-78729-6). Some gradually- and rapidly-varied flow functions are included. For the Manning equation solutions, this package uses modifications of original code from the iemisc package by Irucka Embry.

Self-Consistent Field(SCF) calculation method is one of the most important steps in the calculation methods of quantum chemistry. Ehrenreich, H., & Cohen, M. H. (1959). <doi:10.1103/PhysRev.115.786> However, the most prevailing software in this area, Gaussian''s SCF convergence process is hard to monitor, especially while the job is still running, causing researchers difficulty in knowing whether the oscillation has started or not, wasting time and energy on useless configurations or abandoning the jobs that can actually work. M.J. Frisch, G.W. Trucks, H.B. Schlegel et al. (2016). <https://gaussian.com> SCFMonitor enables Gaussian quantum chemistry calculation software users to easily read the Gaussian .log files and monitor the SCF convergence and geometry optimization process with little effort and clear, beautiful, and clean outputs. It can generate graphs using tidyverse to let users check SCF convergence and geometry optimization processes in real-time. The software supports processing .log files remotely using with rbase::url(). This software is a suitcase for saving time and energy for the researchers, supporting multiple versions of Gaussian'.

Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm. Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959) <doi:10.1007/BF01386390>), bidirectional Dijkstra (Goldberg, Andrew & Fonseca F. Werneck, Renato (2005) <https://archive.siam.org/meetings/alenex05/papers/03agoldberg.pdf>), A* search (P. E. Hart, N. J. Nilsson et B. Raphael (1968) <doi:10.1109/TSSC.1968.300136>), new bidirectional A* (Pijls & Post (2009) <https://repub.eur.nl/pub/16100/ei2009-10.pdf>), Contraction hierarchies (R. Geisberger, P. Sanders, D. Schultes and D. Delling (2008) <doi:10.1007/978-3-540-68552-4_24>), PHAST (D. Delling, A.Goldberg, A. Nowatzyk, R. Werneck (2011) <doi:10.1016/j.jpdc.2012.02.007>). Algorithms for solving the traffic assignment problem are All-or-Nothing assignment, Method of Successive Averages, Frank-Wolfe algorithm (M. Fukushima (1984) <doi:10.1016/0191-2615(84)90029-8>), Conjugate and Bi-Conjugate Frank-Wolfe algorithms (M. Mitradjieva, P. O. Lindberg (2012) <doi:10.1287/trsc.1120.0409>), Algorithm-B (R. B. Dial (2006) <doi:10.1016/j.trb.2006.02.008>).

Fits ordinal regression models with elastic net penalty. Supported model families include cumulative probability, stopping ratio, continuation ratio, and adjacent category. These families are a subset of vector glm's which belong to a model class we call the elementwise link multinomial-ordinal (ELMO) class. Each family in this class links a vector of covariates to a vector of class probabilities. Each of these families has a parallel form, which is appropriate for ordinal response data, as well as a nonparallel form that is appropriate for an unordered categorical response, or as a more flexible model for ordinal data. The parallel model has a single set of coefficients, whereas the nonparallel model has a set of coefficients for each response category except the baseline category. It is also possible to fit a model with both parallel and nonparallel terms, which we call the semi-parallel model. The semi-parallel model has the flexibility of the nonparallel model, but the elastic net penalty shrinks it toward the parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2021) <doi:10.18637/jss.v099.i06>.

Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation. (Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>, Breiman, L. (1996), <doi:10.1007/BF00058655>, Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>, Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>, Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>, Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).

Efficient sampling of truncated multivariate (scale) mixtures of normals under linear inequality constraints is nontrivial due to the analytically intractable normalizing constant. Meanwhile, traditional methods may subject to numerical issues, especially when the dimension is high and dependence is strong. Algorithms proposed by Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> are adopted for overcoming difficulties in simulating truncated distributions. Efficient rejection sampling for simulating truncated univariate normal distribution is included in the package, which shows superiority in terms of acceptance rate and numerical stability compared to existing methods and R packages. An efficient function for sampling from truncated multivariate normal distribution subject to convex polytope restriction regions based on Gibbs sampler for conditional truncated univariate distribution is provided. By extending the sampling method, a function for sampling truncated multivariate Student's t distribution is also developed. Moreover, the proposed method and computation remain valid for high dimensional and strong dependence scenarios. Empirical results in Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> illustrated the superior performance in terms of various criteria (e.g. mixing and integrated auto-correlation time).

Thematic quality indices are provided to facilitate the evaluation and quality control of geospatial data products (e.g. thematic maps, remote sensing classifications, etc.). The indices offered are based on the so-called confusion matrix. This matrix is constructed by comparing the assigned classes or attributes of a set of pairs of positions or objects in the product and the ground truth. In this package it is considered that the classes of the ground truth correspond to the columns and that the classes of the product to be valued correspond to the rows. The package offers two object classes with their methods: ConfMatrix (Confusion matrix) and QCCS (Quality Control Columns Set). The ConfMatrix class of objects offers more than 20 methods based on the confusion matrix. The QCCS class of objects offers a different perspective in which the ground truth is considered to allow the values of the column marginals to be fixed, see Ariza López et al. (2019) <doi:10.3390/app9204240> and Canran Liu et al. (2007) <doi:10.1016/j.rse.2006.10.010> for more details. The package was created with R6'.

Since their introduction by Bose and Nair (1939) <https://www.jstor.org/stable/40383923>, partially balanced incomplete block (PBIB) designs remain an important class of incomplete block designs. The concept of association scheme was used by Bose and Shimamoto (1952) <doi:10.1080/01621459.1952.10501161> for the classification of these designs. The constraint of resources always motivates the experimenter to advance towards PBIB designs, more specifically to higher associate class PBIB designs from balanced incomplete block designs. It is interesting to note that many times higher associate PBIB designs perform better than their counterpart lower associate PBIB designs for the same set of parameters v, b, r, k and lambda_i (i=1,2...m). This package contains functions named GETD() for generating m-associate (m>=2) class PBIB designs along with parameters (v, b, r, k and lambda_i, i = 1, 2,â ¦,m) based on Generalized Triangular (GT) Association Scheme. It also calculates the Information matrix, Average variance factor and canonical efficiency factor of the generated design. These designs, besides having good efficiency, require smaller number of replications and smallest possible concurrence of treatment pairs.

Different inference procedures are proposed in the literature to correct for selection bias that might be introduced with non-random selection mechanisms. A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA). The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitzâ Thompson type estimators. This method is usually used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding information of auxiliary variables. Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities. The package NonProbEst implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample.

This package provides a collection of standard factor retention methods in Exploratory Factor Analysis (EFA), making it easier to determine the number of factors. Traditional methods such as the scree plot by Cattell (1966) <doi:10.1207/s15327906mbr0102_10>, Kaiser-Guttman Criterion (KGC) by Guttman (1954) <doi:10.1007/BF02289162> and Kaiser (1960) <doi:10.1177/001316446002000116>, and flexible Parallel Analysis (PA) by Horn (1965) <doi:10.1007/BF02289447> based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) <doi:10.1037/met0000074>, Comparison Data (CD) by Ruscio and Roche (2012) <doi:10.1037/a0025697>, and Hull method by Lorenzo-Seva et al. (2011) <doi:10.1080/00273171.2011.564527>, as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) <doi:10.3758/s13428-023-02122-4> and Factor Forest (FF) by Goretzko and Buhner (2020) <doi:10.1037/met0000262>. Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors.