R-universe search: needs:scam

adibender

pammtools:Piece-Wise Exponential Additive Mixed Modeling Tools for Survival Analysis

The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) is a powerful model class for the analysis of survival (or time-to-event) data, based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2019)), as well as support for left-truncated data as well as competing risks, recurrent events and multi-state settings. pammtools provides tidy workflow for survival analysis with PAMMs, including data simulation, transformation and other functions for data preprocessing and model post-processing as well as visualization.

Maintained by Andreas Bender. Last updated 6 days ago.

additive-models pamm pammtools piece-wise-exponential survival-analysis

48 stars 9.32 score 310 scripts 8 dependents

mobiodiv

mobr:Measurement of Biodiversity

Functions for calculating metrics for the measurement biodiversity and its changes across scales, treatments, and gradients. The methods implemented in this package are described in: Chase, J.M., et al. (2018) <doi:10.1111/ele.13151>, McGlinn, D.J., et al. (2019) <doi:10.1111/2041-210X.13102>, McGlinn, D.J., et al. (2020) <doi:10.1101/851717>, and McGlinn, D.J., et al. (2023) <doi:10.1101/2023.09.19.558467>.

Maintained by Daniel McGlinn. Last updated 10 days ago.

biodiversity conservation ecology rarefaction species statistics

23 stars 8.65 score 93 scripts

bioc

spicyR:Spatial analysis of in situ cytometry data

The spicyR package provides a framework for performing inference on changes in spatial relationships between pairs of cell types for cell-resolution spatial omics technologies. spicyR consists of three primary steps: (i) summarizing the degree of spatial localization between pairs of cell types for each image; (ii) modelling the variability in localization summary statistics as a function of cell counts and (iii) testing for changes in spatial localizations associated with a response variable.

Maintained by Ellis Patrick. Last updated 26 days ago.

singlecell cellbasedassays spatial

9 stars 8.02 score 57 scripts 1 dependents

unina-sfere

funcharts:Functional Control Charts

Provides functional control charts for statistical process monitoring of functional data, using the methods of Capezza et al. (2020) <doi:10.1002/asmb.2507>, Centofanti et al. (2021) <doi:10.1080/00401706.2020.1753581>, Capezza et al. (2024) <doi:10.1080/00401706.2024.2327346>, Capezza et al. (2024) <doi:10.1080/00224065.2024.2383674>, Centofanti et al. (2022) <doi:10.48550/arXiv.2205.06256>. The package is thoroughly illustrated in the paper of Capezza et al (2023) <doi:10.1080/00224065.2023.2219012>.

Maintained by Christian Capezza. Last updated 13 days ago.

openblas cpp

2 stars 6.73 score 168 scripts

bioc

lisaClust:lisaClust: Clustering of Local Indicators of Spatial Association

lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution.

Maintained by Ellis Patrick. Last updated 4 months ago.

singlecell cellbasedassays spatial

3 stars 6.64 score 48 scripts

trackerproject

trackeR:Infrastructure for Running, Cycling and Swimming Data from GPS-Enabled Tracking Devices

Provides infrastructure for handling running, cycling and swimming data from GPS-enabled tracking devices within R. The package provides methods to extract, clean and organise workout and competition data into session-based and unit-aware data objects of class 'trackeRdata' (S3 class). The information can then be visualised, summarised, and analysed through flexible and extensible methods. Frick and Kosmidis (2017) <doi: 10.18637/jss.v082.i07>, which is updated and maintained as one of the vignettes, provides detailed descriptions of the package and its methods, and real-data demonstrations of the package functionality.

Maintained by Ioannis Kosmidis. Last updated 1 years ago.

90 stars 6.37 score 58 scripts 1 dependents

stc04003

reReg:Recurrent Event Regression

A comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, with or without the presence of a (possibly) informative terminal event described in Chiou et al. (2023) <doi:10.18637/jss.v105.i05>. The modeling framework is based on a joint frailty scale-change model, that includes models described in Wang et al. (2001) <doi:10.1198/016214501753209031>, Huang and Wang (2004) <doi:10.1198/016214504000001033>, Xu et al. (2017) <doi:10.1080/01621459.2016.1173557>, and Xu et al. (2019) <doi:10.5705/SS.202018.0224> as special cases. The implemented estimating procedure does not require any parametric assumption on the frailty distribution. The package also allows the users to specify different model forms for both the recurrent event process and the terminal event.

Maintained by Sy Han (Steven) Chiou. Last updated 3 months ago.

openblas cpp

23 stars 6.35 score 36 scripts 1 dependents

mharinga

insurancerating:Analytic Insurance Rating Techniques

Functions to build, evaluate, and visualize insurance rating models. It simplifies the process of modeling premiums, and allows to analyze insurance risk factors effectively. The package employs a data-driven strategy for constructing insurance tariff classes, drawing on the work of Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>.

Maintained by Martin Haringa. Last updated 5 months ago.

actuarial actuarial-science insurance pricing

70 stars 5.89 score 28 scripts

bioc

smoppix:Analyze Single Molecule Spatial Omics Data Using the Probabilistic Index

Test for univariate and bivariate spatial patterns in spatial omics data with single-molecule resolution. The tests implemented allow for analysis of nested designs and are automatically calibrated to different biological specimens. Tests for aggregation, colocalization, gradients and vicinity to cell edge or centroid are provided.

Maintained by Stijn Hawinkel. Last updated 1 months ago.

transcriptomics spatial singlecell cpp

1 stars 5.10 score 4 scripts

l-a-yates

cpam:Changepoint Additive Models for Time Series Omics Data

Provides a comprehensive framework for time series omics analysis, integrating changepoint detection, smooth and shape-constrained trends, and uncertainty quantification. It supports gene- and transcript-level inferences, p-value aggregation for improved power, and both case-only and case-control designs. It includes an interactive 'shiny' interface. The methods are described in Yates et al. (2024) <doi:10.1101/2024.12.22.630003>.

Maintained by Luke Yates. Last updated 17 days ago.

1 stars 5.06 score

trackerproject

trackeRapp:Interface for the Analysis of Running, Cycling and Swimming Data from GPS-Enabled Tracking Devices

Provides an integrated user interface and workflow for the analysis of running, cycling and swimming data from GPS-enabled tracking devices through the 'trackeR' <https://CRAN.R-project.org/package=trackeR> R package.

Maintained by Ioannis Kosmidis. Last updated 3 years ago.

data-visualization shiny sports-app web-app web-development

32 stars 4.68 score 2 scripts

egeminiani

penfa:Single- And Multiple-Group Penalized Factor Analysis

Fits single- and multiple-group penalized factor analysis models via a trust-region algorithm with integrated automatic multiple tuning parameter selection (Geminiani et al., 2021 <doi:10.1007/s11336-021-09751-8>). Available penalties include lasso, adaptive lasso, scad, mcp, and ridge.

Maintained by Elena Geminiani. Last updated 4 years ago.

factor-analysis lasso latent-variables multiple-group optimization penalization psychometrics

3 stars 4.48 score 5 scripts

cran

relsurv:Relative Survival

Contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables. Work has been described in Pohar Perme, Pavlic (2018) <doi:10.18637/jss.v087.i08>.

Maintained by Damjan Manevski. Last updated 2 months ago.

openblas cpp

3 stars 4.25 score 4 dependents

giampmarra

GJRM:Generalised Joint Regression Modelling

Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.

Maintained by Giampiero Marra. Last updated 5 months ago.

4 stars 4.04 score 67 scripts 5 dependents

predictiveecology

scfmutils:Tools and Utilities for Working With the SCFM Wildfire Simulation Model

The original fire model is described by Cumming et al. (1998), and more accessibly in Armstrong and Cumming (2003). It has recently been implemented as a collection of 'SpaDES' modules by Cumming, McIntire, Eddy, and Chubaty, available from <https://github.com/PredictiveEcology/scfm>.

Maintained by Alex M Chubaty. Last updated 10 days ago.

3.95 score

nunompmoniz

IRon:Solving Imbalanced Regression Tasks

Imbalanced domain learning has almost exclusively focused on solving classification tasks, where the objective is to predict cases labelled with a rare class accurately. Such a well-defined approach for regression tasks lacked due to two main factors. First, standard regression tasks assume that each value is equally important to the user. Second, standard evaluation metrics focus on assessing the performance of the model on the most common cases. This package contains methods to tackle imbalanced domain learning problems in regression tasks, where the objective is to predict extreme (rare) values. The methods contained in this package are: 1) an automatic and non-parametric method to obtain such relevance functions; 2) visualisation tools; 3) suite of evaluation measures for optimisation/validation processes; 4) the squared-error relevance area measure, an evaluation metric tailored for imbalanced regression tasks. More information can be found in Ribeiro and Moniz (2020) <doi:10.1007/s10994-020-05900-9>.

Maintained by Nuno Moniz. Last updated 2 years ago.

evaluation-metrics imbalance-data imbalanced-learning machine-learning regression

19 stars 3.86 score 38 scripts

qyxxx

SLCARE:Semiparametric Latent Class Analysis of Recurrent Events

Efficient R package for latent class analysis of recurrent events, based on the semiparametric multiplicative intensity model by Zhao et al. (2022) <doi:10.1111/rssb.12499>. SLCARE returns estimates for non-functional model parameters along with the associated variance estimates and p-values. Visualization tools are provided to depict the estimated functional model parameters and related functional quantities of interest. SLCARE also delivers a model checking plot to help assess the adequacy of the fitted model.

Maintained by Qi Yu. Last updated 27 days ago.

3.40 score

pierremasselot

cgaim:Constrained Groupwise Additive Index Models

Fits constrained groupwise additive index models and provides functions for inference and interpretation of these models. The method is described in Masselot, Chebana, Campagna, Lavigne, Ouarda, Gosselin (2022) "Constrained groupwise additive index models" <doi:10.1093/biostatistics/kxac023>.

Maintained by Pierre Masselot. Last updated 1 months ago.

3.26 score 12 scripts

vaudigier

micemd:Multiple Imputation by Chained Equations with Multilevel Data

Addons for the 'mice' package to perform multiple imputation using chained equations with two-level data. Includes imputation methods dedicated to sporadically and systematically missing values. Imputation of continuous, binary or count variables are available. Following the recommendations of Audigier, V. et al (2018) <doi:10.1214/18-STS646>, the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation and overimputation for 'mice'.

Maintained by Vincent Audigier. Last updated 1 years ago.

1 stars 3.08 score 80 scripts 1 dependents

vaudigier

clusterMI:Cluster Analysis with Missing Values by Multiple Imputation

Allows clustering of incomplete observations by addressing missing values using multiple imputation. For achieving this goal, the methodology consists in three steps, following Audigier and Niang 2022 <doi:10.1007/s11634-022-00519-1>. I) Missing data imputation using dedicated models. Four multiple imputation methods are proposed, two are based on joint modelling and two are fully sequential methods, as discussed in Audigier et al. (2021) <doi:10.48550/arXiv.2106.04424>. II) cluster analysis of imputed data sets. Six clustering methods are available (distances-based or model-based), but custom methods can also be easily used. III) Partition pooling. The set of partitions is aggregated using Non-negative Matrix Factorization based method. An associated instability measure is computed by bootstrap (see Fang, Y. and Wang, J., 2012 <doi:10.1016/j.csda.2011.09.003>). Among applications, this instability measure can be used to choose a number of clusters with missing values. The package also proposes several diagnostic tools to tune the number of imputed data sets, to tune the number of iterations in fully sequential imputation, to check the fit of imputation models, etc.

Maintained by Vincent Audigier. Last updated 1 months ago.

openblas cpp

2.90 score

cran

zetadiv:Functions to Compute Compositional Turnover Using Zeta Diversity

Functions to compute compositional turnover using zeta-diversity, the number of species shared by multiple assemblages. The package includes functions to compute zeta-diversity for a specific number of assemblages and to compute zeta-diversity for a range of numbers of assemblages. It also includes functions to explain how zeta-diversity varies with distance and with differences in environmental variables between assemblages, using generalised linear models, linear models with negative constraints, generalised additive models,shape constrained additive models, and I-splines.

Maintained by Guillaume Latombe. Last updated 3 years ago.

3 stars 2.08 score

cran

PoweREST:A Bootstrap-Based Power Estimation Tool for Spatial Transcriptomics

Power estimation and sample size calculation for 10X Visium Spatial Transcriptomics data to detect differential expressed genes between two conditions based on bootstrap resampling. See Shui et al. (2024) <doi:10.1101/2024.08.30.610564> for method details.

Maintained by Lan Shui. Last updated 7 months ago.

2.00 score

handcock

sspse:Estimating Hidden Population Size using Respondent Driven Sampling Data

Estimate the size of a networked population based on respondent-driven sampling data. The package is part of the "RDS Analyst" suite of packages for the analysis of respondent-driven sampling data. See Handcock, Gile and Mar (2014) <doi:10.1214/14-EJS923>, Handcock, Gile and Mar (2015) <doi:10.1111/biom.12255>, Kim and Handcock (2021) <doi:10.1093/jssam/smz055>, and McLaughlin, et. al. (2023) <doi:10.1214/23-AOAS1807>.

Maintained by Mark S. Handcock. Last updated 7 months ago.

1.86 score 18 scripts

cran

flexmsm:A General Framework for Flexible Multi-State Survival Modelling

A general estimation framework for multi-state Markov processes with flexible specification of the transition intensities. The log-transition intensities can be specified through Generalised Additive Models which allow for virtually any type of covariate effect. Elementary specifications such as time-homogeneous processes and simple parametric forms are also supported. There are no limitations on the type of process one can assume, with both forward and backward transitions allowed and virtually any number of states.

Maintained by Alessia Eletti. Last updated 8 months ago.

1.00 score

spiegele

FlexGAM:Generalized Additive Models with Flexible Response Functions

Standard generalized additive models assume a response function, which induces an assumption on the shape of the distribution of the response. However, miss-specifying the response function results in biased estimates. Therefore in Spiegel et al. (2017) <doi:10.1007/s11222-017-9799-6> we propose to estimate the response function jointly with the covariate effects. This package provides the underlying functions to estimate these generalized additive models with flexible response functions. The estimation is based on an iterative algorithm. In the outer loop the response function is estimated, while in the inner loop the covariate effects are determined. For the response function a strictly monotone P-spline is used while the covariate effects are estimated based on a modified Fisher-Scoring algorithm. Overall the estimation relies on the 'mgcv'-package.

Maintained by Elmar Spiegel. Last updated 5 years ago.

1.00 score 8 scripts

casua1statistician

BRBVS:Variable Selection and Ranking in Copula Survival Models Affected by General Censoring Scheme

Performs variable selection and ranking based on several measures for the class of copula survival model(s) in high dimensional domain. The package is based on the class of copula survival model(s) implemented in the 'GJRM' package.

Maintained by Danilo Petti. Last updated 9 months ago.

1.00 score