Various methods for targeted and semiparametric inference including augmented inverse probability weighted (AIPW) estimators for missing data and causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>), variable importance and conditional average treatment effects (CATE) (van der Laan (2006) <doi:10.2202/1557-4679.1008>), estimators for risk differences and relative risks (Richardson et al. (2017) <doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).

`BatchSVG` is a feature-based Quality Control (QC) to identify SVGs on spatial transcriptomics data with specific types of batch effect. Regarding to the spatial transcriptomics data experiments, the batch can be defined as "sample", "sex", and etc.The `BatchSVG` method is based on binomial deviance model (Townes et al, 2019) and applies cutoffs based on the number of standard deviation (nSD) of relative change in deviance and rank difference as the data-driven thresholding approach to detect the batch-biased outliers.

iSEEfier provides a set of functionality to quickly and intuitively create, inspect, and combine initial configuration objects. These can be conveniently passed in a straightforward manner to the function call to launch iSEE() with the specified configuration. This package currently works seamlessly with the sets of panels provided by the iSEE and iSEEu packages, but can be extended to accommodate the usage of any custom panel (e.g. from iSEEde, iSEEpathways, or any panel developed independently by the user).

This package calculates classic and/or bootstrap confidence intervals for many parameters such as the population mean, variance, interquartile range (IQR), median absolute deviation (MAD), skewness, kurtosis, Cramer's V, odds ratio, R-squared, quantiles (including median), proportions, different types of correlation measures, difference in means, quantiles and medians. Many of the classic confidence intervals are described in Smithson, M. (2003, ISBN: 978-0761924999). Bootstrap confidence intervals are calculated with the R package boot. Both one- and two-sided intervals are supported.

This package represents an implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization.

This package provides a collection of several utility functions related to resolvable and affine resolvable Partially Balanced Incomplete Block Designs (PBIBDs), have been developed. In the class of resolvable designs, affine resolvable designs are said to be optimal, Bailey (1995) <doi:10.2307/2337638>. Here, the package contains three functions to generate and study the characterization properties of these designs. Developed functions are named as PBIBD1(), PBIBD2() and PBIBD3(), in which first two functions are used to generate two new series of affine resolvable PBIBDs and last one is used to generate a new series of resolvable PBIBDs, respectively. In addition, these functions can also be used to generate design parameters (v, b, r and k), canonical efficiency factors, variance factor between associates and average variance factors of the generated designs. Here v is the number of treatments, b (= b1 + b2, in case of non-proper design) is the number of blocks, r is the number of replications and k (= k1 + k2; k1 is the size of b1 and k2 is the size of b2) is the block size.

We extend existing gene enrichment tests to perform adverse event enrichment analysis. Unlike the continuous gene expression data, adverse event data are counts. Therefore, adverse event data has many zeros and ties. We propose two enrichment tests. One is a modified Fisher's exact test based on pre-selected significant adverse events, while the other is based on a modified Kolmogorov-Smirnov statistic. We add Covariate adjustment to improve the analysis."Adverse event enrichment tests using VAERS" Shuoran Li, Lili Zhao (2020) <arXiv:2007.02266>.

This package provides a comprehensive system for selecting variables and weighting data to match the specifications of the American National Election Studies. The package includes methods for identifying discrepant variables, raking data, and assessing the effects of the raking algorithm. It also allows automated re-raking if target variables fall outside identified bounds and allows greater user specification than other available raking algorithms. A variety of simple weighted statistics that were previously in this package (version .55 and earlier) have been moved to the package weights.'.

This package contains data and functions that can be used to make actuarial life tables. Each function adds a column to the inputted dataset for each intermediate calculation between mortality rate and life expectancy. Users can run any of our functions to complete the life table until that step, or run lifetable() to output a full life table that can be customized to remove optional columns. Methods for creating lifetables are as described in Zedstatistics (2021) <https://www.youtube.com/watch?v=Dfe59glNXAQ>.

Power calculations are a critical component of any research study to determine the minimum sample size necessary to detect differences between multiple groups. Researchers often work with data taking the form of proportions that can be modeled with a beta distribution. Here we present an R package, BetaPASS', that perform power and sample size calculations for data following a beta distribution with comparative nonparametric output. This package allows flexibility with multiple options for link functions to fit the data and graphing functionality for visual comparisons.

Simulating and estimating peer effect models and network formation models. The class of peer effect models includes linear-in-means models (Lee, 2004; <doi:10.1111/j.1468-0262.2004.00558.x>), Tobit models (Xu and Lee, 2015; <doi:10.1016/j.jeconom.2015.05.004>), and discrete numerical data models (Houndetoungan, 2024; <doi:10.2139/ssrn.3721250>). The network formation models include pair-wise regressions with degree heterogeneity (Graham, 2017; <doi:10.3982/ECTA12679>) and exponential random graph models (Mele, 2017; <doi:10.3982/ECTA10400>).

An implementation of double generalized linear model (DGLM) building with variable selection procedures and handling of interaction terms and other complex situations. We also provide a method of handling convergence issues within the dglm() function. The package offers a simulation function for generating simulated data for testing purposes and utilizes the forward stepwise variable selection procedure in model-building. It also provides a new custom bootstrap function for mean and standard deviation estimation and functions for building crossplots and squareplots from a data set.

Provide simple functions to (i) compute a class of multi-functionality measures for a single ecosystem for given function weights, (ii) decompose gamma multi-functionality for pairs of ecosystems and K ecosystems (K can be greater than 2) into a within-ecosystem component (alpha multi-functionality) and an among-ecosystem component (beta multi-functionality). In each case, the correlation between functions can be corrected for. Based on biodiversity and ecosystem function data, this software also facilitates graphics for assessing biodiversity-ecosystem functioning relationships across scales.

This package creates sophisticated models of training data and validates the models with an independent test set, cross validation, or Out Of Bag (OOB) predictions on the training data. Create graphs and tables of the model validation results. Applies these models to GIS .img files of predictors to create detailed prediction surfaces. Handles large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data.

Due to Rstudio's status as open source software, we believe it will be utilized frequently for future data analysis by users whom lack formal training or experience with R'. The NMVANOVA (Novice Model Variation ANOVA) a streamlined variation of experimental design functions that allows novice Rstudio users to perform different model variations one-way analysis of variance without downloading multiple libraries or packages. Users can easily manipulate the data block, and needed inputs so that users only have to plugin the four designed variables/values.

Helps you determine the analysis window to use when analyzing densely-sampled time-series data, such as EEG data, using permutation testing (Maris & Oostenveld, 2007) <doi:10.1016/j.jneumeth.2007.03.024>. These permutation tests can help identify the timepoints where significance of an effect begins and ends, and the results can be plotted in various types of heatmap for reporting. Mixed-effects models are supported using an implementation of the approach by Lee & Braun (2012) <doi:10.1111/j.1541-0420.2011.01675.x>.

This is an interface for the Python package StepMix'. It is a Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods based on pseudolikelihood theory. Additional features include support for covariates and distal outcomes, various simulation utilities, and non-parametric bootstrapping, which allows inference in semi-supervised and unsupervised settings.

This package provides some basic routines for simulating a clinical trial. The primary intent is to provide some tools to generate trial simulations for trials with time to event outcomes. Piecewise exponential failure rates and piecewise constant enrollment rates are the underlying mechanism used to simulate a broad range of scenarios such as those presented in Lin et al. (2020) <doi:10.1080/19466315.2019.1697738>. However, the basic generation of data is done using pipes to allow maximum flexibility for users to meet different needs.

Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019) <doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in Hosszejni and Kastner (2021) <doi:10.18637/jss.v100.i12> and Kastner (2016) <doi:10.18637/jss.v069.i05> and the package examples.

Make it easy to deal with multiple cross-tables in data exploration, by creating them, manipulating them, and adding color helpers to highlight important informations (differences from totals, comparisons between lines or columns, contributions to variance, confidence intervals, odds ratios, etc.). All functions are pipe-friendly and render data frames which can be easily manipulated. In the same time, time-taking operations are done with data.table to go faster with big dataframes. Tables can be exported with formats and colors to Excel', plot and html.

This package provides a test to understand the stability of the underlying stochastic data. Helps the userâ s understand whether the random variable under consideration is stationary or non-stationary without any manual interpretation of the results. It further ensures to check all the prerequisites and assumptions which are underlying the unit root test statistics and if the underlying data is found to be non-stationary in all the 4 lags the function diagnoses the input data and returns with an optimised solution on the same.

This package performs nearest neighbor-based imputation using one or more alternative approaches to processing multivariate data. These include methods based on canonical correlation: analysis, canonical correspondence analysis, and a multivariate adaptation of the random forest classification and regression techniques of Leo Breiman and Adele Cutler. Additional methods are also offered. The package includes functions for comparing the results from running alternative techniques, detecting imputation targets that are notably distant from reference observations, detecting and correcting for bias, bootstrapping and building ensemble imputations, and mapping results.

UMI-4C is a technique that allows characterization of 3D chromatin interactions with a bait of interest, taking advantage of a sonication step to produce unique molecular identifiers (UMIs) that help remove duplication bias, thus allowing a better differential comparsion of chromatin interactions between conditions. This package allows processing of UMI-4C data, starting from FastQ files provided by the sequencing facility. It provides two statistical methods for detecting differential contacts and includes a visualization function to plot integrated information from a UMI-4C assay.

This package implements nested cross-validation applied to the glmnet and caret packages. With glmnet this includes cross-validation of elastic net alpha parameter. A number of feature selection filter functions (t-test, Wilcoxon test, ANOVA, Pearson/Spearman correlation, random forest, ReliefF) for feature selection are provided and can be embedded within the outer loop of the nested CV. Nested CV can be also be performed with the caret package giving access to the large number of prediction methods available in caret.