It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure.

Maximum likelihood estimation, random values generation, density computation and other functions for the exponential-Poisson generalised exponential-Poisson and Poisson-exponential distributions. References include: Rodrigues G. C., Louzada F. and Ramos P. L. (2018). "Poisson-exponential distribution: different methods of estimation". Journal of Applied Statistics, 45(1): 128--144. <doi:10.1080/02664763.2016.1268571>. Louzada F., Ramos, P. L. and Ferreira, H. P. (2020). "Exponential-Poisson distribution: estimation and applications to rainfall and aircraft data with zero occurrence". Communications in Statistics--Simulation and Computation, 49(4): 1024--1043. <doi:10.1080/03610918.2018.1491988>. Barreto-Souza W. and Cribari-Neto F. (2009). "A generalization of the exponential-Poisson distribution". Statistics and Probability Letters, 79(24): 2493--2500. <doi:10.1016/j.spl.2009.09.003>.

This package implements a basic version of the hierarchical clustering algorithm Genie which links two point groups in such a way that an inequity measure (namely, the Gini index) of the cluster sizes does not significantly increase above a given threshold. This method most often outperforms many other data segmentation approaches in terms of clustering quality as tested on a wide range of benchmark datasets. At the same time, Genie retains the high speed of the single linkage approach, therefore it is also suitable for analysing larger data sets. For more details see (Gagolewski et al. 2016 <DOI:10.1016/j.ins.2016.05.003>). For a faster and more feature-rich implementation, see the genieclust package (Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>).

This package implements Multivariable Functional Mendelian Randomization (MV-FMR) to estimate time-varying causal effects of multiple longitudinal exposures on health outcomes. Extends univariable functional Mendelian Randomisation (MR) (Tian et al., 2024 <doi:10.1002/sim.10222>) to the multivariable setting, enabling joint estimation of multiple time-varying exposures with pleiotropy and mediation scenarios. Key features include: (1) data-driven cross-validation for basis component selection, (2) handling of mediation pathways between exposures, (3) support for both continuous and binary outcomes using Generalized Method of Moments (GMM) and control function approaches, (4) one-sample and two-sample MR designs, (5) bootstrap inference and instrument diagnostics including Q-statistics for overidentification testing. Methods are described in Fontana et al. (2025) <doi:10.48550/arXiv.2512.19064>.

Automatically segments a 3D array that represents a volume of binary voxels into mutually exclusive morphological elements. This package extends existing work for segmenting 2D binary raster data. A paper documenting this approach has been published in the journal Landscape Ecology: Remmel, T.K. (2022) <doi:10.1007/s10980-021-01384-7>. The output is a cartridge (list object) that maintains the input array, the segmentation results in array format, and a summary table. Plotting functionality is provided to produce interactive visual outputs from the produced results cartridge, allowing custom plotting to be performed separately from the segmentation, which speeds-up processing. While the old functions persist, they are being phased out and will eventually be replaced with the new runmorph3d() and plotmorph3d() functions.

NanoString nCounter data are gene expression assays where there is no need for the use of enzymes or amplification protocols and work with fluorescent barcodes (Geiss et al. (2018) <doi:10.1038/nbt1385>). Each barcode is assigned a messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its target can be counted. As a result each count of a specific barcode represents the presence of its target mRNA/miRNA. NACHO (NAnoString quality Control dasHbOard) is able to analyse the exported NanoString nCounter data and facilitates the user in performing a quality control. NACHO does this by visualising quality control metrics, expression of control genes, principal components and sample specific size factors in an interactive web application.

This package provides smooth approximations to the L0 norm penalty for estimating sparse Gaussian graphical models (GGMs). Network estimation is performed using the Local Linear Approximation (LLA) framework (Fan & Li, 2001 <doi:10.1198/016214501753382273>; Zou & Li, 2008 <doi:10.1214/009053607000000802>) with five penalty functions: arctangent (Wang & Zhu, 2016 <doi:10.1155/2016/6495417>), EXP (Wang, Fan, & Zhu, 2018 <doi:10.1007/s10463-016-0588-3>), Gumbel, Log (Candes, Wakin, & Boyd, 2008 <doi:10.1007/s00041-008-9045-x>), and Weibull. Adaptive penalty parameters for EXP, Gumbel, and Weibull are estimated via maximum likelihood, and model selection uses information criteria including AIC, BIC, and EBIC (Extended BIC). Simulation functions generate multivariate normal data from GGMs with stochastic block model or small-world (Watts-Strogatz) network structures.

Shrinks a failing R script to the smallest subset of statements that still triggers the same error, using the delta debugging algorithm of Zeller and Hildebrandt (2002) <doi:10.1109/32.988498>. Each candidate reduction is evaluated in a separate R process, so dependencies between statements and their side effects are respected. The result is a one-minimal example, in which removing any remaining statement makes the error disappear; this is the form most useful for bug reports and for questions on community forums. When no statement can be removed, because the failure is nested inside a function body, reduction continues within the surviving statements. A general delta debugging routine and a helper for reducing data frames to the rows that reproduce a failure are also provided.

Makes it easy to build panel data in wide format from Panel Survey of Income Dynamics (PSID) delivered raw data. Downloads data directly from the PSID server using the SAScii package. psidR takes care of merging data from each wave onto a cross-period index file, so that individuals can be followed over time. The user must specify which years they are interested in, and the PSID variable names (e.g. ER21003) for each year (they differ in each year). The package offers helper functions to retrieve variable names from different waves. There are different panel data designs and sample subsetting criteria implemented ("SRC", "SEO", "immigrant" and "latino" samples). More information about the PSID can be obtained at <https://simba.isr.umich.edu/data/data.aspx>.

The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test. This package can be used with any test dataset on which you have computed prior probabilities of case status, posterior probabilities of case status, and you have the observed case-control status. In comparison with the C-statistic (area under ROC curve), the expected weight of evidence (expected information for discrimination) has several advantages as a summary measure of predictive performance. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.

There are three main goals to the vctrs package:

1. To propose vec_size() and vec_type() as alternatives to length() and class(). These definitions are paired with a framework for type-coercion and size-recycling.

2. To define type- and size-stability as desirable function properties, use them to analyse existing base function, and to propose better alternatives. This work has been particularly motivated by thinking about the ideal properties of c(), ifelse(), and rbind().

3. To provide a new vctr base class that makes it easy to create new S3 vectors. vctrs provides methods for many base generics in terms of a few new vctrs generics, making implementation considerably simpler and more robust.

TENET identifies key transcription factors (TFs) and regulatory elements (REs) linked to a specific cell type by finding significantly correlated differences in gene expression and RE DNA methylation between case and control input datasets, and identifying the top genes by number of significant RE DNA methylation site links. It also includes many tools for visualization and analysis of the results, including plots displaying and comparing methylation and expression data and methylation site link counts, survival analysis, TF motif searching in the vicinity of linked RE DNA methylation sites, custom TAD and peak overlap analysis, and UCSC Genome Browser track file generation. A utility function is also provided to download methylation, expression, and patient survival data from The Cancer Genome Atlas (TCGA) for use in TENET or other analyses.

This package provides a non-parametric framework based on estimation statistics principle. Its main purpose is to infer orders of empirical distributions from different categories based on a probability of finding a value in one distribution that is greater than an expectation of another distribution. Given a set of ordered-pair of real-category values the framework is capable of 1) inferring orders of domination of categories and representing orders in the form of a graph; 2) estimating magnitude of difference between a pair of categories in forms of mean-difference confidence intervals; and 3) visualizing domination orders and magnitudes of difference of categories. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2020) <doi:10.1016/j.heliyon.2020.e05435>.

Runs the eDITH (environmental DNA Integrating Transport and Hydrology) model, which implements a mass balance of environmental DNA (eDNA) transport at a river network scale coupled with a species distribution model to obtain maps of species distribution. eDITH can work with both eDNA concentration (e.g., obtained via quantitative polymerase chain reaction) or metabarcoding (read count) data. Parameter estimation can be performed via Bayesian techniques (via the BayesianTools package) or optimization algorithms. An interface to the DHARMa package for posterior predictive checks is provided. See Carraro and Altermatt (2024) <doi:10.1111/2041-210X.14317> for a package introduction; Carraro et al. (2018) <doi:10.1073/pnas.1813843115> and Carraro et al. (2020) <doi:10.1038/s41467-020-17337-8> for methodological details.

Full Consistency Method (FUCOM) for multi-criteria decision-making (MCDM), developed by Dragam Pamucar in 2018 (<doi:10.3390/sym10090393>). The goal of the method is to determine the weights of criteria such that the deviation from full consistency is minimized. Users provide a character vector specifying the ranking of each criterion according to its significance, starting from the criterion expected to have the highest weight to the least significant one. Additionally, users provide a numeric vector specifying the priority values for each criterion. The comparison is made with respect to the first-ranked (most significant) criterion. The function returns the optimized weights for each criterion (summing to 1), the comparative priority (Phi) values, the mathematical transitivity condition (w) value, and the minimum deviation from full consistency (DFC).

Estimates statistically significant marker combination values within which one immunologically distinctive group (i.e., disease case) is more associated than another group (i.e., healthy control), successively, using various combinations (i.e., "gates") of markers to examine features of cells that may be different between groups. For a two-group comparison, the gateR package uses the spatial relative risk function estimated using the sparr package. Details about the sparr package methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.

Fits a generalized linear density ratio model (GLDRM). A GLDRM is a semiparametric generalized linear model. In contrast to a GLM, which assumes a particular exponential family distribution, the GLDRM uses a semiparametric likelihood to estimate the reference distribution. The reference distribution may be any discrete, continuous, or mixed exponential family distribution. The model parameters, which include both the regression coefficients and the cdf of the unspecified reference distribution, are estimated by maximizing a semiparametric likelihood. Regression coefficients are estimated with no loss of efficiency, i.e. the asymptotic variance is the same as if the true exponential family distribution were known. Huang (2014) <doi:10.1080/01621459.2013.824892>. Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>. Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.

This package implements (1) panel cointegration rank tests, (2) estimators for panel vector autoregressive (VAR) models, and (3) identification methods for panel structural vector autoregressive (SVAR) models as described in the accompanying vignette. The implemented functions allow to account for cross-sectional dependence and for structural breaks in the deterministic terms of the VAR processes. Among the large set of functions, particularly noteworthy are those that implement (1) the correlation-augmented inverse normal test on the cointegration rank by Arsova and Oersal (2021, <doi:10.1016/j.ecosta.2020.05.002>), (2) the two-step estimator for pooled cointegrating vectors by Breitung (2005, <doi:10.1081/ETC-200067895>), and (3) the pooled identification based on independent component analysis by Herwartz and Wang (2024, <doi:10.1002/jae.3044>).

MAPFX is an end-to-end toolbox that pre-processes the raw data from MPC experiments (e.g., BioLegend's LEGENDScreen and BD Lyoplates assays), and further imputes the ‘missing’ infinity markers in the wells without those measurements. The pipeline starts by performing background correction on raw intensities to remove the noise from electronic baseline restoration and fluorescence compensation by adapting a normal-exponential convolution model. Unwanted technical variation, from sources such as well effects, is then removed using a log-normal model with plate, column, and row factors, after which infinity markers are imputed using the informative backbone markers as predictors. The completed dataset can then be used for clustering and other statistical analyses. Additionally, MAPFX can be used to normalise data from FFC assays as well.

Co-clustering of the rows and columns of a contingency or binary matrix, or double binary matrices and model selection for the number of row and column clusters. Three models are considered: the Poisson latent block model for contingency matrix, the binary latent block model for binary matrix and a new model we develop: the multiple latent block model for double binary matrices. A new procedure named bikm1 is implemented to investigate more efficiently the grid of numbers of clusters. Then, the studied model selection criteria are the integrated completed likelihood (ICL) and the Bayesian integrated likelihood (BIC). Finally, the co-clustering adjusted Rand index (CARI) to measure agreement between co-clustering partitions is implemented. Robert Valerie, Vasseur Yann, Brault Vincent (2021) <doi:10.1007/s00357-020-09379-w>.

Interactions between different biological entities are crucial for the function of biological systems. In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted. The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. However, such variations often occur locally and do not concern the whole network. To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation. Yousefi et al. (2023) <doi:10.1101/2023.01.22.525058>.

Providing new german-wide TapeR Models and functions for their evaluation. Included are the most common tree species in Germany (Norway spruce, Scots pine, European larch, Douglas fir, Silver fir as well as European beech, Common/Sessile oak and Red oak). Many other species are mapped to them so that 36 tree species / groups can be processed. Single trees are defined by species code, one or multiple diameters in arbitrary measuring height and tree height. The functions then provide information on diameters along the stem, bark thickness, height of diameters, volume of the total or parts of the trunk and total and component above-ground biomass. It is also possible to calculate assortments from the taper curves. Uncertainty information is provided for diameter, volume and component biomass estimation.

Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship.

This package provides a dynamic time warping (DTW) algorithm for stratigraphic alignment, translated into R from the original published MATLAB code by Hay et al. (2019) <doi:10.1130/G46019.1>. The DTW algorithm incorporates two geologically relevant parameters (g and edge) for augmenting the typical DTW cost matrix, allowing for a range of sedimentologic and chronologic conditions to be explored, as well as the generation of an alignment library (as opposed to a single alignment solution). The g parameter relates to the relative sediment accumulation rate between the two time series records, while the edge parameter relates to the amount of total shared time between the records. Note that this algorithm is used for all DTW alignments in the Align Shiny application, detailed in Hagen et al. (in review).