R-universe search: exports:generate

bioc

CytoGLMM:Conditional Differential Analysis for Flow and Mass Cytometry Experiments

The CytoGLMM R package implements two multiple regression strategies: A bootstrapped generalized linear model (GLM) and a generalized linear mixed model (GLMM). Most current data analysis tools compare expressions across many computationally discovered cell types. CytoGLMM focuses on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. As a result, CytoGLMM finds differential proteins in flow and mass cytometry data while reducing biases arising from marker correlations and safeguarding against false discoveries induced by patient heterogeneity.

Maintained by Christof Seiler. Last updated 5 months ago.

flowcytometry proteomics singlecell cellbasedassays cellbiology immunooncology regression statisticalmethod software

2 stars 5.68 score 1 scripts 1 dependents

agi-lab

SPLICE:Synthetic Paid Loss and Incurred Cost Experience (SPLICE) Simulator

An extension to the individual claim simulator called 'SynthETIC' (on CRAN), to simulate the evolution of case estimates of incurred losses through the lifetime of an insurance claim. The transactional simulation output now comprises key dates, and both claim payments and revisions of estimated incurred losses. An initial set of test parameters, designed to mirror the experience of a real insurance portfolio, were set up and applied by default to generate a realistic test data set of incurred histories (see vignette). However, the distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator" <arXiv:2109.04058>.

Maintained by Melantha Wang. Last updated 1 years ago.

6 stars 5.58 score 14 scripts

openpharma

savvyr:Survival Analysis for AdVerse Events with VarYing Follow-Up Times

The SAVVY (Survival Analysis for AdVerse Events with VarYing Follow-Up Times) project is a consortium of academic and pharmaceutical industry partners that aims to improve the analyses of adverse event (AE) data in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events, see Stegherr, Schmoor, Beyersmann, et al. (2021) <doi:10.1186/s13063-021-05354-x>. Although statistical methodologies have advanced, in AE analyses often the incidence proportion, the incidence density or a non-parametric Kaplan-Meier estimator are used, which either ignore censoring or competing events. This package contains functions to easily conduct the proposed improved AE analyses.

Maintained by Thomas Kuenzel. Last updated 10 months ago.

4 stars 4.90 score 1 scripts

jkurle

robust2sls:Outlier Robust Two-Stage Least Squares Inference and Testing

An implementation of easy tools for outlier robust inference in two-stage least squares (2SLS) models. The user specifies a reference distribution against which observations are classified as outliers or not. After removing the outliers, adjusted standard errors are automatically provided. Furthermore, several statistical tests for the false outlier detection rate can be calculated. The outlier removing algorithm can be iterated a fixed number of times or until the procedure converges. The algorithms and robust inference are described in more detail in Jiao (2019) <https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.

Maintained by Jonas Kurle. Last updated 2 years ago.

1 stars 4.43 score 18 scripts

inbo

inlatools:Diagnostic Tools for INLA Models

Several functions which can be useful to choose sensible priors and diagnose the fitted model.

Maintained by Thierry Onkelinx. Last updated 6 months ago.

bayesian-statistics gplv3 inla mixed-models model-checking model-validation

4 stars 4.41 score 43 scripts

boxiang-wang

ARTtransfer:Adaptive and Robust Pipeline for Transfer Learning

Adaptive and Robust Transfer Learning (ART) is a flexible framework for transfer learning that integrates information from auxiliary data sources to improve model performance on primary tasks. It is designed to be robust against negative transfer by including the non-transfer model in the candidate pool, ensuring stable performance even when auxiliary datasets are less informative. See the paper, Wang, Wu, and Ye (2023) <doi:10.1002/sta4.582>.

Maintained by Boxiang Wang. Last updated 2 months ago.

4.40 score 1 scripts

glucodensities

biosensors.usc:Distributional Data Analysis Techniques for Biosensor Data

Unified and user-friendly framework for using new distributional representations of biosensors data in different statistical modeling tasks: regression models, hypothesis testing, cluster analysis, visualization, and descriptive analysis. Distributional representations are a functional extension of compositional time-range metrics and we have used them successfully so far in modeling glucose profiles and accelerometer data. However, these functional representations can be used to represent any biosensor data such as ECG or medical imaging such as fMRI. Matabuena M, Petersen A, Vidal JC, Gude F. "Glucodensities: A new representation of glucose profiles using distributional data analysis" (2021) <doi:10.1177/0962280221998064>.

Maintained by Juan C. Vidal. Last updated 3 years ago.

openblas cpp openmp

4.18 score 3 scripts

jrvanderdoes

fChange:Functional Change Point Detection and Analysis

Analyze functional data and its change points. Includes functionality to store and process data, summarize and validate assumptions, characterize and perform inference of change points, and provide visualizations. Data is stored as discretely collected observations without requiring the selection of basis functions. For more details see chapter 8 of Horvath and Rice (2024) <doi:10.1007/978-3-031-51609-2>. Additional papers are forthcoming. Focused works are also included in the documentation of corresponding functions.

Maintained by Jeremy VanderDoes. Last updated 13 hours ago.

openblas cpp openmp

1 stars 4.04 score

tombjjj

silp:Conditional Process Analysis (CPA) via SEM Approach

Utilizes the Reliability-Adjusted Product Indicator (RAPI) method to estimate effects among latent variables, thus allowing for more precise definition and analysis of mediation and moderation models. Our simulation studies reveal that while 'silp' may exhibit instability with smaller sample sizes and lower reliability scores (e.g., N = 100, 'omega' = 0.7), implementing nearest positive definite matrix correction and bootstrap confidence interval estimation can significantly ameliorate this volatility. When these adjustments are applied, 'silp' achieves estimations akin in quality to those derived from LMS. In conclusion, the 'silp' package is a valuable tool for researchers seeking to explore complex relational structures between variables without resorting to commercial software. Cheung et al.(2021)<doi:10.1007/s10869-020-09717-0> Hsiao et al.(2018)<doi:10.1177/0013164416679877>.

Maintained by Yi-Hsuan Tseng. Last updated 18 days ago.

3.78 score 2 scripts

inbo

multimput:Using Multiple Imputation to Address Missing Data

Accompanying package for the paper: Working with population totals in the presence of missing data comparing imputation methods in terms of bias and precision. Published in 2017 in the Journal of Ornithology volume 158 page 603–615 (<doi:10.1007/s10336-016-1404-9>).

Maintained by Thierry Onkelinx. Last updated 30 days ago.

imputation imputation-model

1 stars 3.62 score 14 scripts 1 dependents

thomasferte

reservoirnet:Reservoir Computing and Echo State Networks

A simple user-friendly library based on the 'python' module 'reservoirpy'. It provides a flexible interface to implement efficient Reservoir Computing (RC) architectures with a particular focus on Echo State Networks (ESN). Some of its features are: offline and online training, parallel implementation, sparse matrix computation, fast spectral initialization, advanced learning rules (e.g. Intrinsic Plasticity) etc. It also makes possible to easily create complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts, and complex feedback loops. Moreover, graphical tools are included to easily explore hyperparameters. Finally, it includes several tutorials exploring time series forecasting, classification and hyperparameter tuning. For more information about 'reservoirpy', please see Trouvain et al. (2020) <doi:10.1007/978-3-030-61616-8_40>. This package was developed in the framework of the University of Bordeaux’s IdEx "Investments for the Future" program / RRI PHDS.

Maintained by Thomas Ferte. Last updated 2 years ago.

3.26 score 12 scripts

acsala

sRDA:Sparse Redundancy Analysis

Sparse redundancy analysis for high dimensional (biomedical) data. Directional multivariate analysis to express the maximum variance in the predicted data set by a linear combination of variables of the predictive data set. Implemented in a partial least squares framework, for more details see Csala et al. (2017) <doi:10.1093/bioinformatics/btx374>.

Maintained by Attila Csala. Last updated 11 months ago.

3 stars 3.18 score 5 scripts

cran

autoRasch:Semi-Automated Rasch Analysis

Performs Rasch analysis (semi-)automatically, which has been shown to be comparable with the standard Rasch analysis (Feri Wijayanto et al. (2021) <doi:10.1111/bmsp.12218>, Feri Wijayanto et al. (2022) <doi:10.3758/s13428-022-01947-9>, Feri Wijayanto et al. (2022) <doi:10.1177/01466216221125178>).

Maintained by Feri Wijayanto. Last updated 2 years ago.

openblas cpp openmp

3.00 score

chongwu-biostat

GLMaSPU:An Adaptive Test on High Dimensional Parameters in Generalized Linear Models

Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called aSPU for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem.

Maintained by Chong Wu. Last updated 8 years ago.

openblas cpp

1 stars 2.70 score 5 scripts

antcalcagni

ssMousetrack:Bayesian State-Space Modeling of Mouse-Tracking Experiments via Stan

Estimates previously compiled state-space modeling for mouse-tracking experiments using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation.

Maintained by Antonio Calcagnì. Last updated 13 days ago.

bayesian-data-analysis mouse-tracking state-space-model cpp

2.70 score 8 scripts

skonigor

CIEE:Estimating and Testing Direct Effects in Directed Acyclic Graphs using Estimating Equations

In many studies across different disciplines, detailed measures of the variables of interest are available. If assumptions can be made regarding the direction of effects between the assessed variables, this has to be considered in the analysis. The functions in this package implement the novel approach CIEE (causal inference using estimating equations; Konigorski et al., 2018, <DOI:10.1002/gepi.22107>) for estimating and testing the direct effect of an exposure variable on a primary outcome, while adjusting for indirect effects of the exposure on the primary outcome through a secondary intermediate outcome and potential factors influencing the secondary outcome. The underlying directed acyclic graph (DAG) of this considered model is described in the vignette. CIEE can be applied to studies in many different fields, and it is implemented here for the analysis of a continuous primary outcome and a time-to-event primary outcome subject to censoring. CIEE uses estimating equations to obtain estimates of the direct effect and robust sandwich standard error estimates. Then, a large-sample Wald-type test statistic is computed for testing the absence of the direct effect. Additionally, standard multiple regression, regression of residuals, and the structural equation modeling approach are implemented for comparison.

Maintained by Stefan Konigorski. Last updated 7 years ago.

2.52 score 11 scripts

hjboonstra

mcmcsae:Markov Chain Monte Carlo Small Area Estimation

Fit multi-level models with possibly correlated random effects using Markov Chain Monte Carlo simulation. Such models allow smoothing over space and time and are useful in, for example, small area estimation.

Maintained by Harm Jan Boonstra. Last updated 4 months ago.

cpp

2.48 score 8 scripts

skylarliu0529

IDLFM:Individual Dynamic Latent Factor Model

A personalized dynamic latent factor model (Zhang et al. (2024) <doi:10.1093/biomet/asae015>) for irregular multi-resolution time series data, to interpolate unsampled measurements from low-resolution time series.

Maintained by Siyang Liu. Last updated 4 months ago.

2.30 score 1 scripts

tctsung

npcs:Neyman-Pearson Classification via Cost-Sensitive Learning

We connect the multi-class Neyman-Pearson classification (NP) problem to the cost-sensitive learning (CS) problem, and propose two algorithms (NPMC-CX and NPMC-ER) to solve the multi-class NP problem through cost-sensitive learning tools. Under certain conditions, the two algorithms are shown to satisfy multi-class NP properties. More details are available in the paper "Neyman-Pearson Multi-class Classification via Cost-sensitive Learning" (Ye Tian and Yang Feng, 2021).

Maintained by Ching-Tsung Tsai. Last updated 2 years ago.

2.23 score 17 scripts

cran

Umatrix:Visualization of Structures in High-Dimensional Data

By gaining the property of emergence through self-organization, the enhancement of SOMs(self organizing maps) is called Emergent SOM (ESOM). The result of the projection by ESOM is a grid of neurons which can be visualised as a three dimensional landscape in form of the Umatrix. Further details can be found in the referenced publications (see url). This package offers tools for calculating and visualising the ESOM as well as Umatrix, Pmatrix and UStarMatrix. All the functionality is also available through graphical user interfaces implemented in 'shiny'. Based on the recognized data structures, the method can be used to generate new data.

Maintained by Jorn Lotsch. Last updated 7 months ago.

cpp

1 stars 2.08 score

valentinian22

BCT:Bayesian Context Trees for Discrete Time Series

An implementation of a collection of tools for exact Bayesian inference with discrete times series. This package contains functions that can be used for prediction, model selection, estimation, segmentation/change-point detection and other statistical tasks. Specifically, the functions provided can be used for the exact computation of the prior predictive likelihood of the data, for the identification of the a posteriori most likely (MAP) variable-memory Markov models, for calculating the exact posterior probabilities and the AIC and BIC scores of these models, for prediction with respect to log-loss and 0-1 loss and segmentation/change-point detection. Example data sets from finance, genetics, animal communication and meteorology are also provided. Detailed descriptions of the underlying theory and algorithms can be found in [Kontoyiannis et al. 'Bayesian Context Trees: Modelling and exact inference for discrete time series.' Journal of the Royal Statistical Society: Series B (Statistical Methodology), April 2022. Available at: <arXiv:2007.14900> [stat.ME], July 2020] and [Lungu et al. 'Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees' <arXiv:2203.04341> [stat.ME], March 2022].

Maintained by Valentinian Mihai Lungu. Last updated 3 years ago.

cpp

2.00 score

cran

kko:Kernel Knockoffs Selection for Nonparametric Additive Models

A variable selection procedure, dubbed KKO, for nonparametric additive model with finite-sample false discovery rate control guarantee. The method integrates three key components: knockoffs, subsampling for stability, and random feature mapping for nonparametric function approximation. For more information, see the accompanying paper: Dai, X., Lyu, X., & Li, L. (2021). “Kernel Knockoffs Selection for Nonparametric Additive Models”. arXiv preprint <arXiv:2105.11659>.

Maintained by Xiang Lyu. Last updated 3 years ago.

1 stars 2.00 score

zzz1990771

gplsim:Spline Estimation for GPLSIM

We provides functions that employ splines to estimate generalized partially linear single index models (GPLSIM), which extend the generalized linear models to include nonlinear effect for some predictors. Please see Y. (2017) at <doi:10.1007/s11222-016-9639-0> and Y., and R. (2002) at <doi:10.1198/016214502388618861> for more details.

Maintained by Tianhai Zu. Last updated 2 years ago.

1.70 score 6 scripts

junyuchen-econ

ablasso:Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models

Implements the Arellano-Bond estimation method combined with LASSO for dynamic linear panel models. See Chernozhukov et al. (2024) "Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models". arXiv preprint <doi:10.48550/arXiv.2402.00584>.

Maintained by Junyu Chen. Last updated 2 months ago.

1 stars 1.30 score 1 scripts

vy-du

EMLI:Computationally Efficient Maximum Likelihood Identification of Linear Dynamical Systems

Provides implementations of computationally efficient maximum likelihood parameter estimation algorithms for models that represent linear dynamical systems. Currently, one such algorithm is implemented for the one-dimensional cumulative structural equation model with shock-error output measurement equation and assumptions of normality and independence. The corresponding scientific paper is yet to be published, therefore the relevant reference will be provided later.

Maintained by Vytautas Dulskis. Last updated 2 years ago.

1.00 score

cran

corTest:Robust Tests for Equal Correlation

There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. The score statistic U is proportion to difference of two correlations based on different types of correlation in 6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation. ST5() and ST6() used two different ways to combine ST3() and ST4(). We highly recommend ST5() according to the article titled ''New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs'' published in Scientific Reports. Please see the reference: Yu et al. (2019) <doi:10.1038/s41598-019-40167-8>.

Maintained by Danyang Yu. Last updated 5 years ago.

1.00 score

cran

transmdl:Semiparametric Transformation Models

To make the semiparametric transformation models easier to apply in real studies, we introduce this R package, in which the MLE in transformation models via an EM algorithm proposed by Zeng D, Lin DY(2007) <doi:10.1111/j.1369-7412.2007.00606.x> and adaptive lasso method in transformation models proposed by Liu XX, Zeng D(2013) <doi:10.1093/biomet/ast029> are implemented. C++ functions are used to compute complex loops. The coefficient vector and cumulative baseline hazard function can be estimated, along with the corresponding standard errors and P values.

Maintained by Fengyu Wen. Last updated 3 years ago.

cpp

1.00 score

wheelerb

PRIMEplus:Study Design for Immunotherapy Clinical Trials

Perform sample size, power calculation and subsequent analysis for Immuno-oncology (IO) trials composed of responders and non-responders.

Maintained by Bill Wheeler. Last updated 1 years ago.

1.00 score