R-universe search: longitudinal

philips-software

latrend:A Framework for Clustering Longitudinal Data

A framework for clustering longitudinal datasets in a standardized way. The package provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease. The 'akmedoids' package is available from <https://github.com/MAnalytics/akmedoids>.

Maintained by Niek Den Teuling. Last updated 2 months ago.

cluster-analysis clustering-evaluation clustering-methods data-science longitudinal-clustering longitudinal-data mixture-models time-series-analysis

49.8 match 30 stars 6.77 score 26 scripts

stan-dev

rstanarm:Bayesian Applied Regression Modeling via Stan

Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

Maintained by Ben Goodrich. Last updated 9 months ago.

bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics multilevel-models rstan rstanarm stan statistical-modeling cpp

15.6 match 393 stars 15.68 score 5.0k scripts 13 dependents

graemeleehickey

joineRML:Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes

Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).

Maintained by Graeme L. Hickey. Last updated 1 months ago.

armadillo biostatistics clinical-trials cox dynamic joint-models longitudinal-data multivariate-analysis multivariate-data multivariate-longitudinal-data prediction rcpp regression-models statistics survival openblas cpp openmp

22.8 match 30 stars 8.93 score 146 scripts 1 dependents

leifeld

btergm:Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.

Maintained by Philip Leifeld. Last updated 12 months ago.

complex-networks dynamic-analysis ergm estimation goodness-of-fit inference longitudinal-data network-analysis prediction tergm

29.8 match 17 stars 6.70 score 83 scripts 2 dependents

cran

longitudinal:Analysis of Multiple Time Course Data

Contains general data structures and functions for longitudinal data with multiple variables, repeated measurements, and irregularly spaced time points. Also implements a shrinkage estimator of dynamical correlation and dynamical covariance.

Maintained by Korbinian Strimmer. Last updated 3 years ago.

73.7 match 1 stars 2.70 score 24 scripts 7 dependents

veronica0206

nlpsem:Linear and Nonlinear Longitudinal Process in Structural Equation Modeling Framework

Provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Jin Liu (2023) <arXiv:2302.03237v2>.

Maintained by Jin Liu. Last updated 4 months ago.

28.6 match 145 stars 6.91 score 16 scripts

cran

frailtypack:Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints

The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of the joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time and/or longitudinal endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal two-part joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailty-copula models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. 13) Generalized shared and joint frailty models for recurrent and terminal events. Proportional hazards (PH), additive hazard (AH), proportional odds (PO) and probit models are available in a fully parametric framework. For PH and AH models, it is possible to consider type-varying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty models. 14) Competing Joint Frailty Model: A single type of recurrent event and two terminal events. 15) functions to compute power and sample size for four Gamma-frailty-based designs: Shared Frailty Models, Nested Frailty Models, Joint Frailty Models, and General Joint Frailty Models. Each design includes two primary functions: a power function, which computes power given a specified sample size; and a sample size function, which computes the required sample size to achieve a specified power. Moreover, the package can be used with its shiny application, in a local mode or by following the link below.

Maintained by Virginie Rondeau. Last updated 10 days ago.

fortran openmp

29.2 match 7 stars 5.56 score 1 dependents

cecileproust-lima

lcmm:Extended Mixed Models Using Latent Classes and Latent Processes

Estimation of various extensions of the mixed models including latent class mixed models, joint latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).

Maintained by Cecile Proust-Lima. Last updated 1 months ago.

fortran

14.0 match 62 stars 11.41 score 249 scripts 7 dependents

denisrustand

INLAjoint:Multivariate Joint Modeling for Longitudinal and Time-to-Event Outcomes with 'INLA'

Estimation of joint models for multivariate longitudinal markers (with various distributions available) and survival outcomes (possibly accounting for competing risks) with Integrated Nested Laplace Approximations (INLA). The flexible and user friendly function joint() facilitates the use of the fast and reliable inference technique implemented in the 'INLA' package for joint modeling. More details are given in the help page of the joint() function (accessible via ?joint in the R console) and the vignette associated to the joint() function (accessible via vignette("INLAjoint") in the R console).

Maintained by Denis Rustand. Last updated 24 days ago.

21.2 match 19 stars 7.36 score 40 scripts

graemeleehickey

joineR:Joint Modelling of Repeated Measurements and Time-to-Event Data

Analysis of repeated measurements and time-to-event data via random effects joint models. Fits the joint models proposed by Henderson and colleagues <doi:10.1093/biostatistics/1.4.465> (single event time) and by Williamson and colleagues (2008) <doi:10.1002/sim.3451> (competing risks events time) to a single continuous repeated measure. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-varying covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by a latent Gaussian process. The model is estimated using am Expectation Maximization algorithm. Some plotting functions and the variogram are also included. This project is funded by the Medical Research Council (Grant numbers G0400615 and MR/M013227/1).

Maintained by Graeme L. Hickey. Last updated 3 months ago.

biostatistics competing-risks cox joiner longitudinal-data repeated-measurements repeated-measures statisics statistical-methods survival survival-analysis time-to-event

22.6 match 18 stars 6.87 score 69 scripts

causal-lda

TrialEmulation:Causal Analysis of Observational Time-to-Event Data

Implements target trial emulation methods to apply randomized clinical trial design and analysis in an observational setting. Using marginal structural models, it can estimate intention-to-treat and per-protocol effects in emulated trials using electronic health records. A description and application of the method can be found in Danaei et al (2013) <doi:10.1177/0962280211403603>.

Maintained by Isaac Gravestock. Last updated 23 days ago.

causal-inference longitudinal-data survival-analysis cpp

17.8 match 25 stars 7.72 score 29 scripts

functionaldata

fdapace:Functional Data Analysis and Empirical Dynamics

A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

Maintained by Yidong Zhou. Last updated 9 months ago.

cpp

11.9 match 31 stars 11.46 score 474 scripts 25 dependents

njtierney

brolgar:Browse Over Longitudinal Data Graphically and Analytically in R

Provides a framework of tools to summarise, visualise, and explore longitudinal data. It builds upon the tidy time series data frames used in the 'tsibble' package, and is designed to integrate within the 'tidyverse', and 'tidyverts' (for time series) ecosystems. The methods implemented include calculating features for understanding longitudinal data, including calculating summary statistics such as quantiles, medians, and numeric ranges, sampling individual series, identifying individual series representative of a group, and extending the facet system in 'ggplot2' to facilitate exploration of samples of data. These methods are fully described in the paper "brolgar: An R package to Browse Over Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, Dianne Cook, Tania Prvan (2020) <doi:10.32614/RJ-2022-023>.

Maintained by Nicholas Tierney. Last updated 2 months ago.

15.5 match 109 stars 8.73 score 141 scripts

kgoldfeld

simstudy:Simulation of Study Data

Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).

Maintained by Keith Goldfeld. Last updated 8 months ago.

data-generation data-simulation simulation statistical-models cpp

12.0 match 82 stars 11.00 score 972 scripts 1 dependents

drizopoulos

JMbayes:Joint Modeling of Longitudinal and Time-to-Event Data under a Bayesian Approach

Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.

Maintained by Dimitris Rizopoulos. Last updated 4 years ago.

joint-models longitudinal-responses prediction-model survival-analysis openblas cpp openmp jags

17.8 match 60 stars 6.98 score 80 scripts

milanwiedemann

lcsm:Univariate and Bivariate Latent Change Score Modelling

Helper functions to implement univariate and bivariate latent change score models in R using the 'lavaan' package. For details about Latent Change Score Modeling (LCSM) see McArdle (2009) <doi:10.1146/annurev.psych.60.110707.163612> and Grimm, An, McArdle, Zonderman and Resnick (2012) <doi:10.1080/10705511.2012.659627>. The package automatically generates 'lavaan' syntax for different model specifications and varying timepoints. The 'lavaan' syntax generated by this package can be returned and further specifications can be added manually. Longitudinal plots as well as simplified path diagrams can be created to visualise data and model specifications. Estimated model parameters and fit statistics can be extracted as data frames. Data for different univariate and bivariate LCSM can be simulated by specifying estimates for model parameters to explore their effects. This package combines the strengths of other R packages like 'lavaan', 'broom', and 'semPlot' by generating 'lavaan' syntax that helps these packages work together.

Maintained by Milan Wiedemann. Last updated 2 years ago.

19.1 match 17 stars 6.34 score 43 scripts

drizopoulos

JMbayes2:Extended Joint Models for Longitudinal and Time-to-Event Data

Fit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864).

Maintained by Dimitris Rizopoulos. Last updated 11 days ago.

competing-risks longitudinal-analysis mixed-models multi-state personalized-medicine precision-medicine prediction-model survival-models openblas cpp openmp

14.6 match 84 stars 8.27 score 264 scripts 2 dependents

jtimonen

lgpr:Longitudinal Gaussian Process Regression

Interpretable nonparametric modeling of longitudinal data using additive Gaussian process regression. Contains functionality for inferring covariate effects and assessing covariate relevances. Models are specified using a convenient formula syntax, and can include shared, group-specific, non-stationary, heterogeneous and temporally uncertain effects. Bayesian inference for model parameters is performed using 'Stan'. The modeling approach and methods are described in detail in Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.

Maintained by Juho Timonen. Last updated 6 months ago.

bayesian-inference gaussian-processes longitudinal-data stan cpp

18.3 match 25 stars 5.94 score 69 scripts

refunders

refund:Regression with Functional Data

Methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.

Maintained by Julia Wrobel. Last updated 6 months ago.

10.3 match 41 stars 10.25 score 472 scripts 16 dependents

statnet

ergm:Fit, Simulate and Diagnose Exponential-Family Models for Networks

An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008) <doi:10.18637/jss.v024.i03> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.

Maintained by Pavel N. Krivitsky. Last updated 6 days ago.

6.8 match 100 stars 15.36 score 1.4k scripts 36 dependents

nt-williams

lmtp:Non-Parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies

Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; <doi:10.1007/s10985-023-09606-7>).

Maintained by Nicholas Williams. Last updated 8 days ago.

causal-inference censored-data longitudinal-data machine-learning modified-treatment-policy nonparametric-statistics precision-medicine robust-statistics statistics stochastic-interventions survival-analysis targeted-learning

16.0 match 64 stars 6.37 score 91 scripts

ngreifer

cobalt:Covariate Balance Tables and Plots

Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'WeightIt', 'MatchThem', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with multi-category, continuous, or longitudinal treatments.

Maintained by Noah Greifer. Last updated 11 months ago.

causal-inference propensity-scores

7.3 match 75 stars 12.98 score 1.0k scripts 8 dependents

grvanderploeg

parafac4microbiome:Parallel Factor Analysis Modelling of Longitudinal Microbiome Data

Creation and selection of PARAllel FACtor Analysis (PARAFAC) models of longitudinal microbiome data. You can import your own data with our import functions or use one of the example datasets to create your own PARAFAC models. Selection of the optimal number of components can be done using assessModelQuality() and assessModelStability(). The selected model can then be plotted using plotPARAFACmodel(). The Parallel Factor Analysis method was originally described by Caroll and Chang (1970) <doi:10.1007/BF02310791> and Harshman (1970) <https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.

Maintained by Geert Roelof van der Ploeg. Last updated 20 days ago.

dimensionality-reduction microbiome microbiome-data multiway multiway-algorithms parallel-factor-analysis

14.6 match 6 stars 6.31 score 13 scripts

nerler

JointAI:Joint Analysis and Imputation of Incomplete Data

Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data, as described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>. Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' <https://mcmc-jags.sourceforge.io/> with the help of the package 'rjags'.

Maintained by Nicole S. Erler. Last updated 12 months ago.

bayesian generalized-linear-models glm glmm imputation imputations jags joint-analysis linear-mixed-models linear-regression-models mcmc-sample mcmc-sampling missing-data missing-values survival cpp

12.5 match 28 stars 7.30 score 59 scripts 1 dependents

bioc

MetaboDynamics:Bayesian analysis of longitudinal metabolomics data

MetaboDynamics is an R-package that provides a framework of probabilistic models to analyze longitudinal metabolomics data. It enables robust estimation of mean concentrations despite varying spread between timepoints and reports differences between timepoints as well as metabolite specific dynamics profiles that can be used for identifying "dynamics clusters" of metabolites of similar dynamics. Provides probabilistic over-representation analysis of KEGG functional modules and pathways as well as comparison between clusters of different experimental conditions.

Maintained by Katja Danielzik. Last updated 1 days ago.

software metabolomics bayesian functionalprediction multiplecomparison kegg pathways dynamics functional-analysis longitudinal-analysis metabolomics-data metabolomics-pipeline cpp

17.3 match 5 stars 5.24 score 3 scripts

kkholst

lava:Latent Variable Models

A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.

Maintained by Klaus K. Holst. Last updated 2 months ago.

latent-variable-models simulation statistics structural-equation-models

7.0 match 33 stars 12.85 score 610 scripts 476 dependents

centerforassessment

SGPdata:Exemplar Data Sets for Student Growth Percentiles (SGP) Analyses

Data sets utilized by the 'SGP' package as exemplars for users to conduct their own student growth percentiles (SGP) analyses.

Maintained by Damian W. Betebenner. Last updated 3 months ago.

sgp sgp-analyses sgp-data student-growth-percentiles student-growth-projections

13.9 match 2 stars 5.75 score 36 scripts

stocnet

RSiena:Siena - Simulation Investigation for Empirical Network Analysis

The main purpose of this package is to perform simulation-based estimation of stochastic actor-oriented models for longitudinal network data collected as panel data. Dependent variables can be single or multivariate networks, which can be directed, non-directed, or two-mode; and associated actor variables. There are also functions for testing parameters and checking goodness of fit. An overview of these models is given in Snijders (2017), <doi:10.1146/annurev-statistics-060116-054035>.

Maintained by Tom A.B. Snijders. Last updated 1 months ago.

longitudinal-data rsiena social-network-analysis statistical-network-analysis statistics cpp

8.0 match 107 stars 9.93 score 346 scripts 1 dependents

danforthcenter

pcvr:Plant Phenotyping and Bayesian Statistics

Analyse common types of plant phenotyping data, provide a simplified interface to longitudinal growth modeling and select Bayesian statistics, and streamline use of 'PlantCV' output. Several Bayesian methods and reporting guidelines for Bayesian methods are described in Kruschke (2018) <doi:10.1177/2515245918771304>, Kruschke (2013) <doi:10.1037/a0029146>, and Kruschke (2021) <doi:10.1038/s41562-021-01177-7>.

Maintained by Josh Sumner. Last updated 4 days ago.

11.3 match 4 stars 6.99 score 39 scripts

jacob-long

panelr:Regression Models and Utilities for Repeated Measures and Panel Data

Provides an object type and associated tools for storing and wrangling panel data. Implements several methods for creating regression models that take advantage of the unique aspects of panel data. Among other capabilities, automates the "within-between" (also known as "between-within" and "hybrid") panel regression specification that combines the desirable aspects of both fixed effects and random effects econometric models and fits them as multilevel models (Allison, 2009 <doi:10.4135/9781412993869.d33>; Bell & Jones, 2015 <doi:10.1017/psrm.2014.7>). These models can also be estimated via generalized estimating equations (GEE; McNeish, 2019 <doi:10.1080/00273171.2019.1602504>) and Bayesian estimation is (optionally) supported via 'Stan'. Supports estimation of asymmetric effects models via first differences (Allison, 2019 <doi:10.1177/2378023119826441>) as well as a generalized linear model extension thereof using GEE.

Maintained by Jacob A. Long. Last updated 1 years ago.

social-science statistics

8.8 match 101 stars 8.76 score 181 scripts 1 dependents

zjg540066169

SBMTrees:Sequential Imputation with Bayesian Trees Mixed-Effects Models for Longitudinal Data

Implements a sequential imputation framework using Bayesian Mixed-Effects Trees ('SBMTrees') for handling missing data in longitudinal studies. The package supports a variety of models, including non-linear relationships and non-normal random effects and residuals, leveraging Dirichlet Process priors for increased flexibility. Key features include handling Missing at Random (MAR) longitudinal data, imputation of both covariates and outcomes, and generating posterior predictive samples for further analysis. The methodology is designed for applications in epidemiology, biostatistics, and other fields requiring robust handling of missing data in longitudinal settings.

Maintained by Jungang Zou. Last updated 3 months ago.

bayesian-machine-learning longitudinal-data missing-data-imputation openblas cpp

16.6 match 1 stars 4.40 score 10 scripts

mwheymans

psfmi:Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets

Pooling, backward and forward selection of linear, logistic and Cox regression models in multiply imputed datasets. Backward and forward selection can be done from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and the median p-values method. This is also possible for Mixed models. The models can contain continuous, dichotomous, categorical and restricted cubic spline predictors and interaction terms between all these type of predictors. The stability of the models can be evaluated using (cluster) bootstrapping. The package further contains functions to pool model performance measures as ROC/AUC, Reclassification, R-squared, scaled Brier score, H&L test and calibration plots for logistic regression models. Internal validation can be done across multiply imputed datasets with cross-validation or bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients can be obtained. Backward and forward selection as part of internal validation is possible. A function to externally validate logistic prediction models in multiple imputed datasets is available and a function to compare models. For Cox models a strata variable can be included. Eekhout (2017) <doi:10.1186/s12874-017-0404-7>. Wiel (2009) <doi:10.1093/biostatistics/kxp011>. Marshall (2009) <doi:10.1186/1471-2288-9-57>.

Maintained by Martijn Heymans. Last updated 2 years ago.

cox-regression imputation imputed-datasets logistic multiple-imputation pool predictor regression selection spline spline-predictors

10.0 match 10 stars 7.17 score 70 scripts

bioc

RolDE:RolDE: Robust longitudinal Differential Expression

RolDE detects longitudinal differential expression between two conditions in noisy high-troughput data. Suitable even for data with a moderate amount of missing values.RolDE is a composite method, consisting of three independent modules with different approaches to detecting longitudinal differential expression. The combination of these diverse modules allows RolDE to robustly detect varying differences in longitudinal trends and expression levels in diverse data types and experimental settings.

Maintained by Medical Bioinformatics Centre. Last updated 5 months ago.

statisticalmethod software timecourse regression proteomics differentialexpression

13.8 match 5 stars 5.18 score 1 scripts

tidymodels

multilevelmod:Model Wrappers for Multi-Level Models

Bindings for hierarchical regression models for use with the 'parsnip' package. Models include longitudinal generalized linear models (Liang and Zeger, 1986) <doi:10.1093/biomet/73.1.13>, and mixed-effect models (Pinheiro and Bates) <doi:10.1007/978-1-4419-0318-1_1>.

Maintained by Hannah Frick. Last updated 5 months ago.

8.3 match 74 stars 8.12 score 239 scripts

bioc

LACE:Longitudinal Analysis of Cancer Evolution (LACE)

LACE is an algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a weighed likelihood function computed on multiple time points.

Maintained by Davide Maspero. Last updated 5 months ago.

biomedicalinformatics singlecell somaticmutation

8.8 match 15 stars 7.65 score 3 scripts

growthcharts

brokenstick:Broken Stick Model for Irregular Longitudinal Data

Data on multiple individuals through time are often sampled at times that differ between persons. Irregular observation times can severely complicate the statistical analysis of the data. The broken stick model approximates each subject’s trajectory by one or more connected line segments. The times at which segments connect (breakpoints) are identical for all subjects and under control of the user. A well-fitting broken stick model effectively transforms individual measurements made at irregular times into regular trajectories with common observation times. Specification of the model requires three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The package contains functions for fitting the broken stick model to data, for predicting curves in new data and for plotting broken stick estimates. The package supports two optimization methods, and includes options to structure the variance-covariance matrix of the random effects. The analyst may use the software to smooth growth curves by a series of connected straight lines, to align irregularly observed curves to a common time grid, to create synthetic curves at a user-specified set of breakpoints, to estimate the time-to-time correlation matrix and to predict future observations. See <doi:10.18637/jss.v106.i07> for additional documentation on background, methodology and applications.

Maintained by Stef van Buuren. Last updated 2 years ago.

b-spline growth-curves linear-mixed-models longitudinal-data

12.2 match 9 stars 5.33 score 12 scripts

jamesmurray7

gmvjoint:Joint Models of Survival and Multivariate Longitudinal Data

Fit joint models of survival and multivariate longitudinal data. The longitudinal data is specified by generalised linear mixed models. The joint models are fit via maximum likelihood using an approximate expectation maximisation algorithm. Bernhardt (2015) <doi:10.1016/j.csda.2014.11.011>.

Maintained by James Murray. Last updated 5 months ago.

glmm joint-models longitudinal mixed-models model prediction survival survival-analysis openblas cpp openmp

17.1 match 3 stars 3.78 score 20 scripts

ouhscbbmc

REDCapR:Interaction Between R and REDCap

Encapsulates functions to streamline calls from R to the REDCap API. REDCap (Research Electronic Data CAPture) is a web application for building and managing online surveys and databases developed at Vanderbilt University. The Application Programming Interface (API) offers an avenue to access and modify data programmatically, improving the capacity for literate and reproducible programming.

Maintained by Will Beasley. Last updated 2 months ago.

redcap redcap-api

5.1 match 118 stars 12.36 score 438 scripts 6 dependents

toreopsahl

tnet:Weighted, Two-Mode, and Longitudinal Networks Analysis

Binary ties limit the richness of network analyses as relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties are dependent upon each other.

Maintained by Tore Opsahl. Last updated 5 years ago.

14.0 match 1 stars 4.48 score 145 scripts 6 dependents

ccy-dev

LongDat:A Tool for 'Covariate'-Sensitive Longitudinal Analysis on 'omics' Data

This tool takes longitudinal dataset as input and analyzes if there is significant change of the features over time (a proxy for treatments), while detects and controls for 'covariates' simultaneously. 'LongDat' is able to take in several data types as input, including count, proportion, binary, ordinal and continuous data. The output table contains p values, effect sizes and 'covariates' of each feature, making the downstream analysis easy.

Maintained by Chia-Yu Chen. Last updated 4 months ago.

13.2 match 4 stars 4.60 score 4 scripts

ngreifer

WeightIt:Weighting for Covariate Balance in Observational Studies

Generates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality of several R packages and providing in-house estimation methods. Available methods include those that rely on parametric modeling, optimization, and machine learning. Also allows for assessment of weights and checking of covariate balance by interfacing directly with the 'cobalt' package. Methods for estimating weighted regression models that take into account uncertainty in the estimation of the weights via M-estimation or bootstrapping are available. See the vignette "Installing Supporting Packages" for instructions on how to install any package 'WeightIt' uses, including those that may not be on CRAN.

Maintained by Noah Greifer. Last updated 5 days ago.

causal-inference inverse-probability-weights observational-study propensity-scores

5.1 match 112 stars 11.58 score 508 scripts 3 dependents

marclavielle

mlxR:Simulation of Longitudinal Data

Simulation and visualization of complex models for longitudinal data. The models are encoded using the model coding language 'Mlxtran' and automatically converted into C++ codes. That allows one to implement very easily complex ODE-based models and complex statistical models, including mixed effects models, for continuous, count, categorical, and time-to-event data.

Maintained by Marc Lavielle. Last updated 2 years ago.

9.3 match 19 stars 6.28 score 67 scripts

polkas

cat2cat:Handling an Inconsistently Coded Categorical Variable in a Longitudinal Dataset

Unifying an inconsistently coded categorical variable between two different time points in accordance with a mapping table. The main rule is to replicate the observation if it could be assigned to a few categories. Then using frequencies or statistical methods to approximate the probabilities of being assigned to each of them. This procedure was invented and implemented in the paper by Nasinski, Majchrowska, and Broniatowska (2020) <doi:10.24425/cejeme.2020.134747>.

Maintained by Maciej Nasinski. Last updated 1 years ago.

categories encoding encodings factor longitudinal mapping mappings panel transitions

12.9 match 4 stars 4.30 score 2 scripts

randcorporation

optic:Simulation Tool for Causal Inference Using Longitudinal Data

Implements a simulation study to assess the strengths and weaknesses of causal inference methods for estimating policy effects using panel data. See Griffin et al. (2021) <doi:10.1007/s10742-022-00284-w> and Griffin et al. (2022) <doi:10.1186/s12874-021-01471-y> for a description of our methods.

Maintained by Pedro Nascimento de Lima. Last updated 2 months ago.

causal-inference diff-in-diff longitudinal-data simulation

10.4 match 9 stars 5.26 score 6 scripts

usccana

netdiffuseR:Analysis of Diffusion and Contagion Processes on Networks

Empirical statistical analysis, visualization and simulation of diffusion and contagion processes on networks. The package implements algorithms for calculating network diffusion statistics such as transmission rate, hazard rates, exposure models, network threshold levels, infectiousness (contagion), and susceptibility. The package is inspired by work published in Valente, et al., (2015) <DOI:10.1016/j.socscimed.2015.10.001>; Valente (1995) <ISBN: 9781881303213>, Myers (2000) <DOI:10.1086/303110>, Iyengar and others (2011) <DOI:10.1287/mksc.1100.0566>, Burt (1987) <DOI:10.1086/228667>; among others.

Maintained by George Vega Yon. Last updated 3 months ago.

contagion diffusion-network network-analysis network-visualization openblas cpp openmp

6.1 match 88 stars 8.88 score 217 scripts

nlsy-links

NlsyLinks:Utilities and Kinship Information for Research with the NLSY

Utilities and kinship information for behavior genetics and developmental research using the National Longitudinal Survey of Youth (NLSY; <https://www.nlsinfo.org/>).

Maintained by S. Mason Garrison. Last updated 7 days ago.

behavior-genetics kinship-information national-longitudinal-survey nlsy

7.2 match 7 stars 7.49 score 185 scripts

joshuaschwab

ltmle:Longitudinal Targeted Maximum Likelihood Estimation

Targeted Maximum Likelihood Estimation ('TMLE') of treatment/censoring specific mean outcome or marginal structural model for point-treatment and longitudinal data. Petersen et al. (2014) <doi:10.1515/jci-2013-0007>

Maintained by Joshua Schwab. Last updated 2 years ago.

8.7 match 23 stars 6.15 score 207 scripts

mcanouil

eggla:Early Growth Genetics Longitudinal Analysis

Tools for longitudinal analysis within the EGG (Early Growth Genetics) Consortium (<http://egg-consortium.org/>).

Maintained by Mickaël Canouil. Last updated 1 months ago.

genetics growth-curves infancy longitudinal-analysis mixed-effects-models spline-regression

12.7 match 3 stars 4.15 score 19 scripts

bioc

timeOmics:Time-Course Multi-Omics data integration

timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step.

Maintained by Antoine Bodein. Last updated 5 months ago.

clustering featureextraction timecourse dimensionreduction software sequencing microarray metabolomics metagenomics proteomics classification regression immunooncology geneprediction multiplecomparison cluster integration multi-omics time-series

8.8 match 24 stars 5.98 score 10 scripts

luyouepiusf

DySS:Dynamic Screening Systems

In practice, we will encounter problems where the longitudinal performance of processes needs to be monitored over time. Dynamic screening systems (DySS) are methods that aim to identify and give signals to processes with poor performance as early as possible. This package is designed to implement dynamic screening systems and the related methods. References: Qiu, P. and Xiang, D. (2014) <doi:10.1080/00401706.2013.822423>; Qiu, P. and Xiang, D. (2015) <doi:10.1002/sim.6477>; Li, J. and Qiu, P. (2016) <doi:10.1080/0740817X.2016.1146423>; Li, J. and Qiu, P. (2017) <doi:10.1002/qre.2160>; You, L. and Qiu, P. (2019) <doi:10.1080/00949655.2018.1552273>; Qiu, P., Xia, Z., and You, L. (2020) <doi:10.1080/00401706.2019.1604434>; You, L., Qiu, A., Huang, B., and Qiu, P. (2020) <doi:10.1002/bimj.201900127>; You, L. and Qiu, P. (2021) <doi:10.1080/00224065.2020.1767006>.

Maintained by Lu You. Last updated 3 years ago.

fortran openblas cpp

24.3 match 2.00 score 6 scripts

mcdonohue

longpower:Sample Size Calculations for Longitudinal Data

Compute power and sample size for linear models of longitudinal data. Supported models include mixed-effects models and models fit by generalized least squares and generalized estimating equations. The package is described in Iddi and Donohue (2022) <DOI:10.32614/RJ-2022-022>. Relevant formulas are derived by Liu and Liang (1997) <DOI:10.2307/2533554>, Diggle et al (2002) <ISBN:9780199676750>, and Lu, Luo, and Chen (2008) <DOI:10.2202/1557-4679.1098>.

Maintained by Michael C. Donohue. Last updated 6 months ago.

7.9 match 3 stars 6.04 score 22 scripts 1 dependents

simsem

semTools:Useful Tools for Structural Equation Modeling

Provides miscellaneous tools for structural equation modeling, many of which extend the 'lavaan' package. For example, latent interactions can be estimated using product indicators (Lin et al., 2010, <doi:10.1080/10705511.2010.488999>) and simple effects probed; analytical power analyses can be conducted (Jak et al., 2021, <doi:10.3758/s13428-020-01479-0>); and scale reliability can be estimated based on estimated factor-model parameters.

Maintained by Terrence D. Jorgensen. Last updated 3 days ago.

3.4 match 79 stars 13.74 score 1.1k scripts 31 dependents

datalorax

equatiomatic:Transform Models into 'LaTeX' Equations

The goal of 'equatiomatic' is to reduce the pain associated with writing 'LaTeX' formulas from fitted models. The primary function of the package, extract_eq(), takes a fitted model object as its input and returns the corresponding 'LaTeX' code for the model.

Maintained by Philippe Grosjean. Last updated 7 days ago.

4.0 match 619 stars 11.75 score 424 scripts 5 dependents

haziqj

iprior:Regression Modelling using I-Priors

Provides methods to perform and analyse I-prior regression models. Estimation is done either via direct optimisation of the log-likelihood or an EM algorithm.

Maintained by Haziq Jamil. Last updated 12 months ago.

fisher-information functional gaussian-processes gpr hilbert kernel krein longitudinal multilevel priors random-effects regression reproducing rkhs rkks space cpp

10.0 match 1 stars 4.69 score 33 scripts

epullenayegum

IrregLong:Analysis of Longitudinal Data with Irregular Observation Times

Functions to help with analysis of longitudinal data featuring irregular observation times, where the observation times may be associated with the outcome process. There are functions to quantify the degree of irregularity, fit inverse-intensity weighted Generalized Estimating Equations (Lin H, Scharfstein DO, Rosenheck RA (2004) <doi:10.1111/j.1467-9868.2004.b5543.x>), perform multiple outputation (Pullenayegum EM (2016) <doi:10.1002/sim.6829>) and fit semi-parametric joint models (Liang Y (2009) <doi: 10.1111/j.1541-0420.2008.01104.x>).

Maintained by Eleanor Pullenayegum. Last updated 3 years ago.

11.4 match 2 stars 4.04 score 11 scripts

shanpengli

FastJM:Semi-Parametric Joint Modeling of Longitudinal and Survival Data

Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data applying customized linear scan algorithms, proposed by Li and colleagues (2022) <doi:10.1155/2022/1362913>. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by shared random effects. The model is estimated using an Expectation Maximization algorithm.

Maintained by Shanpeng Li. Last updated 30 days ago.

cpp cpp

9.3 match 5 stars 4.88 score 2 scripts 2 dependents

anestistouloumis

SimCorMultRes:Simulates Correlated Multinomial Responses

Simulates correlated multinomial responses conditional on a marginal model specification.

Maintained by Anestis Touloumis. Last updated 12 months ago.

binary longitudinal-studies multinomial simulation

7.5 match 7 stars 6.04 score 26 scripts 2 dependents

alexanderrobitzsch

sirt:Supplementary Item Response Theory Models

Supplementary functions for item response models aiming to complement existing R packages. The functionality includes among others multidimensional compensatory and noncompensatory IRT models (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>), MCMC for hierarchical IRT models and testlet models (Fox, 2010, <doi:10.1007/978-1-4419-0742-4>), NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>), Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>; Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>), faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011, <doi:10.1111/j.1745-3984.2011.00143.x>), ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), DETECT statistic (Stout, Habing, Douglas & Kim, 1996, <doi:10.1177/014662169602000403>), local structural equation modeling (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016, <doi:10.1080/00273171.2016.1142856>).

Maintained by Alexander Robitzsch. Last updated 3 months ago.

item-response-theory openblas cpp

4.5 match 23 stars 10.01 score 280 scripts 22 dependents

leifeld

xergm.common:Common Infrastructure for Extensions of Exponential Random Graph Models

Datasets and definitions of generic functions used in dependencies of the 'xergm' package.

Maintained by Philip Leifeld. Last updated 5 years ago.

22.3 match 2.01 score 34 scripts 1 dependents

gustavo039

Mmcsd:Modeling Complex Longitudinal Data in a Quick and Easy Way

Matching longitudinal methodology models with complex sampling design. It fits fixed and random effects models and covariance structured models so far. It also provides tools to perform statistical tests considering these specifications as described in : Pacheco, P. H. (2021). "Modeling complex longitudinal data in R: development of a statistical package." <https://repositorio.ufjf.br/jspui/bitstream/ufjf/13437/1/pedrohenriquedemesquitapacheco.pdf>.

Maintained by Pedro Pacheco. Last updated 2 years ago.

16.5 match 2.70 score 3 scripts

skent259

rsmatch:Matching Methods for Time-Varying Observational Studies

Implements popular methods for matching in time-varying observational studies. Matching is difficult in this scenario because participants can be treated at different times which may have an influence on the outcomes. The core methods include: "Balanced Risk Set Matching" from Li, Propert, and Rosenbaum (2011) <doi:10.1198/016214501753208573> and "Propensity Score Matching with Time-Dependent Covariates" from Lu (2005) <doi:10.1111/j.1541-0420.2005.00356.x>. Some functions use the 'Gurobi' optimization back-end to improve the optimization problem speed; the 'gurobi' R package and associated software can be downloaded from <https://www.gurobi.com> after obtaining a license.

Maintained by Sean Kent. Last updated 1 years ago.

causal-inference longitudinal-analysis matching observational-data time-varying

10.9 match 2 stars 4.00 score 5 scripts

drizopoulos

JM:Joint Modeling of Longitudinal and Survival Data

Shared parameter models for the joint modeling of longitudinal and time-to-event data.

Maintained by Dimitris Rizopoulos. Last updated 3 years ago.

8.8 match 2 stars 4.93 score 112 scripts 1 dependents

bioc

MSstatsQC:Longitudinal system suitability monitoring and quality control for proteomic experiments

MSstatsQC is an R package which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments.

Maintained by Eralp Dogu. Last updated 5 months ago.

software qualitycontrol proteomics massspectrometry

9.6 match 4.48 score 7 scripts 1 dependents

weiliang

powerMediation:Power/Sample Size Calculation for Mediation Analysis

Functions to calculate power and sample size for testing (1) mediation effects; (2) the slope in a simple linear regression; (3) odds ratio in a simple logistic regression; (4) mean change for longitudinal study with 2 time points; (5) interaction effect in 2-way ANOVA; and (6) the slope in a simple Poisson regression.

Maintained by Weiliang Qiu. Last updated 4 years ago.

10.6 match 3 stars 3.97 score 65 scripts 2 dependents

friendly

heplots:Visualizing Hypothesis Tests in Multivariate Linear Models

Provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). The related 'candisc' package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables.

Maintained by Michael Friendly. Last updated 8 days ago.

linear-hypotheses matrices multivariate-linear-models plot repeated-measure-designs visualizing-hypothesis-tests

3.6 match 9 stars 11.49 score 1.1k scripts 7 dependents

cran

stpm:Stochastic Process Model for Analysis of Longitudinal and Time-to-Event Outcomes

Utilities to estimate parameters of the models with survival functions induced by stochastic covariates. Miscellaneous functions for data preparation and simulation are also provided. For more information, see: (i)"Stochastic model for analysis of longitudinal data on aging and mortality" by Yashin A. et al. (2007), Mathematical Biosciences, 208(2), 538-551, <DOI:10.1016/j.mbs.2006.11.006>; (ii) "Health decline, aging and mortality: how are they related?" by Yashin A. et al. (2007), Biogerontology 8(3), 291(302), <DOI:10.1007/s10522-006-9073-3>.

Maintained by Ilya Y. Zhbannikov. Last updated 3 years ago.

openblas cpp openmp

15.3 match 2.70 score

thothorn

HSAUR3:A Handbook of Statistical Analyses Using R (3rd Edition)

Functions, data sets, analyses and examples from the third edition of the book ''A Handbook of Statistical Analyses Using R'' (Torsten Hothorn and Brian S. Everitt, Chapman & Hall/CRC, 2014). The first chapter of the book, which is entitled ''An Introduction to R'', is completely included in this package, for all other chapters, a vignette containing all data analyses is available. In addition, Sweave source code for slides of selected chapters is included in this package (see HSAUR3/inst/slides). The publishers web page is '<https://www.routledge.com/A-Handbook-of-Statistical-Analyses-using-R/Hothorn-Everitt/p/book/9781482204582>'.

Maintained by Torsten Hothorn. Last updated 7 months ago.

6.1 match 6 stars 6.72 score 120 scripts 2 dependents

bioc

metagenomeSeq:Statistical analysis for sparse high-throughput sequencing

metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.

Maintained by Joseph N. Paulson. Last updated 3 months ago.

immunooncology classification clustering geneticvariability differentialexpression microbiome metagenomics normalization visualization multiplecomparison sequencing software

3.4 match 69 stars 12.02 score 494 scripts 7 dependents

timbeechey

opa:An Implementation of Ordinal Pattern Analysis

Quantifies hypothesis to data fit for repeated measures and longitudinal data, as described by Thorngate (1987) <doi:10.1016/S0166-4115(08)60083-7> and Grice et al., (2015) <doi:10.1177/2158244015604192>. Hypothesis and data are encoded as pairwise relative orderings which are then compared to determine the percentage of orderings in the data that are matched by the hypothesis.

Maintained by Timothy Beechey. Last updated 1 years ago.

data-analysis hypothesis-testing longitudinal ordinal rcpp repeated-measures statistics cpp

10.5 match 1 stars 3.70 score 2 scripts

thothorn

HSAUR:A Handbook of Statistical Analyses Using R (1st Edition)

Functions, data sets, analyses and examples from the book ''A Handbook of Statistical Analyses Using R'' (Brian S. Everitt and Torsten Hothorn, Chapman & Hall/CRC, 2006). The first chapter of the book, which is entitled ''An Introduction to R'', is completely included in this package, for all other chapters, a vignette containing all data analyses is available.

Maintained by Torsten Hothorn. Last updated 3 years ago.

6.1 match 6.07 score 253 scripts 5 dependents

juanv66x

qvirus:Quantum Computing for Analyzing CD4 Lymphocytes and Antiretroviral Therapy

Resources, tutorials, and code snippets dedicated to exploring the intersection of quantum computing and artificial intelligence (AI) in the context of analyzing Cluster of Differentiation 4 (CD4) lymphocytes and optimizing antiretroviral therapy (ART) for human immunodeficiency virus (HIV). With the emergence of quantum artificial intelligence and the development of small-scale quantum computers, there's an unprecedented opportunity to revolutionize the understanding of HIV dynamics and treatment strategies. This project leverages the R package 'qsimulatR' (Ostmeyer and Urbach, 2023, <https://CRAN.R-project.org/package=qsimulatR>), a quantum computer simulator, to explore these applications in quantum computing techniques, addressing the challenges in studying CD4 lymphocytes and enhancing ART efficacy.

Maintained by Juan Pablo Acuña González. Last updated 11 days ago.

6.8 match 5.43 score 15 scripts

anthonydevaux

DynForest:Random Forest with Multivariate Longitudinal Predictors

Based on random forest principle, 'DynForest' is able to include multiple longitudinal predictors to provide individual predictions. Longitudinal predictors are modeled through the random forest. The methodology is fully described for a survival outcome in: Devaux, Helmer, Genuer & Proust-Lima (2023) <doi: 10.1177/09622802231206477>.

Maintained by Anthony Devaux. Last updated 5 months ago.

5.5 match 16 stars 6.38 score 8 scripts

francescobartolucci

LMest:Generalized Latent Markov Models

Latent Markov models for longitudinal continuous and categorical data. See Bartolucci, Pandolfi, Pennoni (2017)<doi:10.18637/jss.v081.i04>.

Maintained by Francesco Bartolucci. Last updated 2 months ago.

fortran openblas

7.7 match 3 stars 4.58 score 42 scripts

openpharma

DoseFinding:Planning and Analyzing Dose Finding Experiments

The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology (Pinheiro et al. (2014) <doi:10.1002/sim.6052>).

Maintained by Marius Thomas. Last updated 4 days ago.

openblas

3.4 match 8 stars 10.32 score 98 scripts 10 dependents

donaldrwilliams

BGGM:Bayesian Gaussian Graphical Models

Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) <doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

Maintained by Philippe Rast. Last updated 3 months ago.

bayes-factors bayesian-hypothesis-testing gaussian-graphical-models openblas cpp openmp

3.6 match 55 stars 9.64 score 102 scripts 1 dependents

cran

mixAK:Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering

Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models. The package is primarily related to the publications Komárek (2009, Comp. Stat. and Data Anal.) <doi:10.1016/j.csda.2009.05.006> and Komárek and Komárková (2014, J. of Stat. Soft.) <doi:10.18637/jss.v059.i12>. It also implements methods published in Komárek and Komárková (2013, Ann. of Appl. Stat.) <doi:10.1214/12-AOAS580>, Hughes, Komárek, Bonnett, Czanner, García-Fiñana (2017, Stat. in Med.) <doi:10.1002/sim.7397>, Jaspers, Komárek, Aerts (2018, Biom. J.) <doi:10.1002/bimj.201600253> and Hughes, Komárek, Czanner, García-Fiñana (2018, Stat. Meth. in Med. Res) <doi:10.1177/0962280216674496>.

Maintained by Arnošt Komárek. Last updated 6 months ago.

openblas

7.6 match 4 stars 4.54 score 108 scripts 3 dependents

bioc

microbiomeDASim:Microbiome Differential Abundance Simulation

A toolkit for simulating differential microbiome data designed for longitudinal analyses. Several functional forms may be specified for the mean trend. Observations are drawn from a multivariate normal model. The objective of this package is to be able to simulate data in order to accurately compare different longitudinal methods for differential abundance.

Maintained by Justin Williams. Last updated 5 months ago.

microbiome visualization software

7.7 match 3 stars 4.48 score 1 scripts

go-ski

clustra:Clustering Longitudinal Trajectories

Clusters longitudinal trajectories over time (can be unequally spaced, unequal length time series and/or partially overlapping series) on a common time axis. Performs k-means clustering on a single continuous variable measured over time, where each mean is defined by a thin plate spline fit to all points in a cluster. Distance is MSE across trajectory points to cluster spline. Provides graphs of derived cluster splines, silhouette plots, and Adjusted Rand Index evaluations of the number of clusters. Scales well to large data with multicore parallelism available to speed computation.

Maintained by George Ostrouchov. Last updated 3 months ago.

7.6 match 4.48 score 6 scripts

julianfaraway

faraway:Datasets and Functions for Books by Julian Faraway

Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014, 3rd Ed. February 2025 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated).

Maintained by Julian Faraway. Last updated 1 months ago.

data

3.6 match 29 stars 9.43 score 1.7k scripts 1 dependents

thothorn

HSAUR2:A Handbook of Statistical Analyses Using R (2nd Edition)

Functions, data sets, analyses and examples from the second edition of the book ''A Handbook of Statistical Analyses Using R'' (Brian S. Everitt and Torsten Hothorn, Chapman & Hall/CRC, 2008). The first chapter of the book, which is entitled ''An Introduction to R'', is completely included in this package, for all other chapters, a vignette containing all data analyses is available. In addition, the package contains Sweave code for producing slides for selected chapters (see HSAUR2/inst/slides).

Maintained by Torsten Hothorn. Last updated 2 years ago.

6.1 match 5.51 score 181 scripts 1 dependents

briencj

growthPheno:Functional Analysis of Phenotypic Growth Data to Smooth and Extract Traits

Assists in the plotting and functional smoothing of traits measured over time and the extraction of features from these traits, implementing the SET (Smoothing and Extraction of Traits) method described in Brien et al. (2020) Plant Methods, 16. Smoothing of growth trends for individual plants using natural cubic smoothing splines or P-splines is available for removing transient effects and segmented smoothing is available to deal with discontinuities in growth trends. There are graphical tools for assessing the adequacy of trait smoothing, both when using this and other packages, such as those that fit nonlinear growth models. A range of per-unit (plant, pot, plot) growth traits or features can be extracted from the data, including single time points, interval growth rates and other growth statistics, such as maximum growth or days to maximum growth. The package also has tools adapted to inputting data from high-throughput phenotyping facilities, such from a Lemna-Tec Scananalyzer 3D (see <https://www.youtube.com/watch?v=MRAF_mAEa7E/> for more information). The package 'growthPheno' can also be installed from <http://chris.brien.name/rpackages/>.

Maintained by Chris Brien. Last updated 2 months ago.

5.0 match 6 stars 6.53 score 42 scripts

cellmapslab

longmixr:Longitudinal Consensus Clustering with 'flexmix'

An adaption of the consensus clustering approach from 'ConsensusClusterPlus' for longitudinal data. The longitudinal data is clustered with flexible mixture models from 'flexmix', while the consensus matrices are hierarchically clustered as in 'ConsensusClusterPlus'. By using the flexibility from 'flexmix' and 'FactoMineR', one can use mixed data types for the clustering.

Maintained by Jonas Hagenberg. Last updated 1 years ago.

7.7 match 3 stars 4.18 score 7 scripts

tbates

umx:Structural Equation Modeling and Twin Modeling in R

Quickly create, run, and report structural equation models, and twin models. See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS. Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R. Twin Research and Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>.

Maintained by Timothy C. Bates. Last updated 1 days ago.

behavior-genetics genetics openmx psychology sem statistics structural-equation-modeling tutorials twin-models umx

3.4 match 44 stars 9.45 score 472 scripts

osofr

simcausal:Simulating Longitudinal Data with Causal Inference Applications

A flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples.

Maintained by Oleg Sofrygin. Last updated 8 months ago.

counterfactual-data sem simulated-network simulating-data structural-equations

5.2 match 67 stars 6.06 score 170 scripts

stocnet

manynet:Many Ways to Make, Modify, Map, Mark, and Measure Myriad Networks

Many tools for making, modifying, mapping, marking, measuring, and motifs and memberships of many different types of networks. All functions operate with matrices, edge lists, and 'igraph', 'network', and 'tidygraph' objects, and on one-mode, two-mode (bipartite), and sometimes three-mode networks. The package includes functions for importing and exporting, creating and generating networks, modifying networks and node and tie attributes, and describing and visualizing networks with sensible defaults.

Maintained by James Hollway. Last updated 3 months ago.

diffusion-models graphs network-analysis

4.9 match 13 stars 6.41 score 35 scripts 1 dependents

isobelbarrott

Landmarking:Analysis using Landmark Models

The landmark approach allows survival predictions to be updated dynamically as new measurements from an individual are recorded. The idea is to set predefined time points, known as "landmark times", and form a model at each landmark time using only the individuals in the risk set. This package allows the longitudinal data to be modelled either using the last observation carried forward or linear mixed effects modelling. There is also the option to model competing risks, either through cause-specific Cox regression or Fine-Gray regression. To find out more about the methods in this package, please see <https://isobelbarrott.github.io/Landmarking/articles/Landmarking>.

Maintained by Isobel Barrott. Last updated 2 years ago.

5.4 match 6 stars 5.72 score 44 scripts

vasileioskarapoulios

LDNN:Longitudinal Data Neural Network

This is a Neural Network regression model implementation using 'Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs.

Maintained by Vasileios Karapoulios. Last updated 4 years ago.

8.3 match 3.70 score 6 scripts

bioc

microbiomeExplorer:Microbiome Exploration App

The MicrobiomeExplorer R package is designed to facilitate the analysis and visualization of marker-gene survey feature data. It allows a user to perform and visualize typical microbiome analytical workflows either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows the application enables automated analysis report generation.

Maintained by Janina Reeder. Last updated 5 months ago.

classification clustering geneticvariability differentialexpression microbiome metagenomics normalization visualization multiplecomparison sequencing software immunooncology

7.7 match 4.00 score 8 scripts

johnlawrence1

SurvDisc:Discrete Time Survival and Longitudinal Data Analysis

Various functions for discrete time survival analysis and longitudinal analysis. SIMEX method for correcting for bias for errors-in-variables in a mixed effects model. Asymptotic mean and variance of different proportional hazards test statistics using different ties methods given two survival curves and censoring distributions. Score test and Wald test for regression analysis of grouped survival data. Calculation of survival curves for events defined by the response variable in a mixed effects model crossing a threshold with or without confirmation.

Maintained by John Lawrence. Last updated 7 years ago.

29.9 match 1.00 score 6 scripts

bgoodri

mi:Missing Data Imputation and Model Checking

The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.

Maintained by Ben Goodrich. Last updated 3 years ago.

3.6 match 2 stars 8.25 score 244 scripts 47 dependents

gasparrini

mixmeta:An Extended Mixed-Effects Framework for Meta-Analysis

A collection of functions to perform various meta-analytical models through a unified mixed-effects framework, including standard univariate fixed and random-effects meta-analysis and meta-regression, and non-standard extensions such as multivariate, multilevel, longitudinal, and dose-response models.

Maintained by Antonio Gasparrini. Last updated 3 years ago.

4.3 match 13 stars 6.96 score 63 scripts 13 dependents

leoniecourcoul

FlexVarJM:Estimate Joint Models with Subject-Specific Variance

Estimation of mixed models including a subject-specific variance which can be time and covariate dependent. In the joint model framework, the package handles left truncation and allows a flexible dependence structure between the competing events and the longitudinal marker. The estimation is performed under the frequentist framework, using the Marquardt-Levenberg algorithm. (Courcoul, Tzourio, Woodward, Barbieri, Jacqmin-Gadda (2023) <arXiv:2306.16785>).

Maintained by Léonie Courcoul. Last updated 6 months ago.

openblas cpp

8.0 match 3.70 score 1 scripts

rjacobucci

longRPart2:Recursive Partitioning of Longitudinal Data

Performs recursive partitioning of linear and nonlinear mixed effects models, specifically for longitudinal data. The package is an extension of the original 'longRPart' package by Stewart and Abdolell (2013) <https://cran.r-project.org/package=longRPart>.

Maintained by Ross Jacobucci. Last updated 2 years ago.

11.4 match 2 stars 2.58 score 19 scripts

janoleko

LaMa:Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.

Maintained by Jan-Ole Koslik. Last updated 1 days ago.

openblas cpp openmp

3.8 match 9 stars 7.84 score 42 scripts

zhiwent

BCClong:Bayesian Consensus Clustering for Multiple Longitudinal Features

It is very common nowadays for a study to collect multiple features and appropriately integrating multiple longitudinal features simultaneously for defining individual clusters becomes increasingly crucial to understanding population heterogeneity and predicting future outcomes. 'BCClong' implements a Bayesian consensus clustering (BCC) model for multiple longitudinal features via a generalized linear mixed model. Compared to existing packages, several key features make the 'BCClong' package appealing: (a) it allows simultaneous clustering of mixed-type (e.g., continuous, discrete and categorical) longitudinal features, (b) it allows each longitudinal feature to be collected from different sources with measurements taken at distinct sets of time points (known as irregularly sampled longitudinal data), (c) it relaxes the assumption that all features have the same clustering structure by estimating the feature-specific (local) clusterings and consensus (global) clustering.

Maintained by Zhiwen Tan. Last updated 9 months ago.

openblas cpp openmp

5.6 match 4 stars 5.20 score 10 scripts

alexanderrobitzsch

STARTS:Functions for the STARTS Model

Contains functions for estimating the STARTS model of Kenny and Zautra (1995, 2001) <DOI:10.1037/0022-006X.63.1.52>, <DOI:10.1037/10409-008>. Penalized maximum likelihood estimation and Markov Chain Monte Carlo estimation are also provided, see Luedtke, Robitzsch and Wagner (2018) <DOI:10.1037/met0000155>.

Maintained by Alexander Robitzsch. Last updated 12 months ago.

longitudinal-data structural-equation-modeling cpp

7.5 match 2 stars 3.85 score 14 scripts

gasparrini

mvmeta:Multivariate and Univariate Meta-Analysis and Meta-Regression

Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.

Maintained by Antonio Gasparrini. Last updated 5 years ago.

3.8 match 6 stars 7.29 score 151 scripts 10 dependents

sistm

LongituRF:Random Forests for Longitudinal Data

Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data. In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) <doi:10.1177/0962280220946080> Random forests for high-dimensional longitudinal data.

Maintained by Louis Capitaine. Last updated 3 years ago.

7.8 match 11 stars 3.42 score 24 scripts

kunfa

mixedBayes:Bayesian Longitudinal Regularized Quantile Mixed Model

In longitudinal studies, the same subjects are measured repeatedly over time, leading to correlations among the repeated measurements. Properly accounting for the intra-cluster correlations in the presence of data heterogeneity and long tailed distributions of the disease phenotype is challenging, especially in the context of high dimensional regressions. In this package, we developed a Bayesian quantile mixed effects model with spike- and -slab priors to dissect important gene - environment interactions under longitudinal genomics studies. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in 'C++'. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.

Maintained by Kun Fan. Last updated 26 days ago.

openblas cpp openmp

7.1 match 3.74 score 4 scripts

patriciamar

ShinyItemAnalysis:Test and Item Analysis via Shiny

Package including functions and interactive shiny application for the psychometric analysis of educational tests, psychological assessments, health-related and other types of multi-item measurements, or ratings from multiple raters.

Maintained by Patricia Martinkova. Last updated 1 months ago.

assessment differential-item-functioning item-analysis item-response-theory psychometrics shiny

3.4 match 44 stars 7.88 score 105 scripts 3 dependents

american-institutes-for-research

EdSurvey:Analysis of NCES Education Survey and Assessment Data

Read in and analyze functions for education survey and assessment data from the National Center for Education Statistics (NCES) <https://nces.ed.gov/>, including National Assessment of Educational Progress (NAEP) data <https://nces.ed.gov/nationsreportcard/> and data from the International Assessment Database: Organisation for Economic Co-operation and Development (OECD) <https://www.oecd.org/en/about/directorates/directorate-for-education-and-skills.html>, including Programme for International Student Assessment (PISA), Teaching and Learning International Survey (TALIS), Programme for the International Assessment of Adult Competencies (PIAAC), and International Association for the Evaluation of Educational Achievement (IEA) <https://www.iea.nl/>, including Trends in International Mathematics and Science Study (TIMSS), TIMSS Advanced, Progress in International Reading Literacy Study (PIRLS), International Civic and Citizenship Study (ICCS), International Computer and Information Literacy Study (ICILS), and Civic Education Study (CivEd).

Maintained by Paul Bailey. Last updated 15 days ago.

3.4 match 10 stars 7.86 score 139 scripts 1 dependents

kenaho1

asbio:A Collection of Statistical Tools for Biologists

Contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R. CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.

Maintained by Ken Aho. Last updated 2 months ago.

3.5 match 5 stars 7.32 score 310 scripts 3 dependents

lcbc-uio

galamm:Generalized Additive Latent and Mixed Models

Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. (2023) <doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and Skrondal (2004)'s unifying framework for multilevel latent variable modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to 'lme4' (Bates et al. (2015) <doi:10.18637/jss.v067.i01>) and 'PLmixed' (Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>).

Maintained by Øystein Sørensen. Last updated 6 months ago.

generalized-additive-models hierarchical-models item-response-theory latent-variable-models structural-equation-models cpp

3.4 match 29 stars 7.33 score 41 scripts

pennchopmicrobiomeprogram

ZIBR:A Zero-Inflated Beta Random Effect Model

A two-part zero-inflated Beta regression model with random effects (ZIBR) for testing the association between microbial abundance and clinical covariates for longitudinal microbiome data. Eric Z. Chen and Hongzhe Li (2016) <doi:10.1093/bioinformatics/btw308>.

Maintained by Charlie Bushman. Last updated 1 years ago.

4.3 match 30 stars 5.86 score 24 scripts

insightsengineering

teal.goshawk:Longitudinal Visualization `teal` Modules

Modules that produce web interfaces through which longitudinal visualizations can be dynamically modified and displayed. These included box plot, correlation plot, density distribution plot, line plot, scatter plot and spaghetti plot with accompanying summary. Data are expected in ADaM structure. Requires analysis subject level (ADSL) and analysis laboratory (ADLB) data sets. Beyond core variables, Limit of Quantification flag variable (LOQFL) is expected with levels 'Y', 'N' or NA.

Maintained by Nick Paszty. Last updated 19 days ago.

modules nest

3.6 match 3 stars 6.59 score 2 scripts

npmldabook

npmlda:Non-Parametric Models for Longitudinal Data Analysis

Support the book: Wu CO and Tian X (2018). Nonparametric Models for Longitudinal Data: With Implementation in R. (Chapman & Hall/CRC Monographs on Statistics & Applied Probability); Present global and local smoothing methods for the conditional-mean and conditional-distribution based nonparametric models with longitudinal Data.

Maintained by Xin Tian. Last updated 6 years ago.

8.7 match 2.70 score 8 scripts

kunfa

Blend:Robust Bayesian Longitudinal Regularized Semiparametric Mixed Models

Our recently developed fully robust Bayesian semiparametric mixed-effect model for high-dimensional longitudinal studies with heterogeneous observations can be implemented through this package. This model can distinguish between time-varying interactions and constant-effect-only cases to avoid model misspecifications. Facilitated by spike-and-slab priors, this model leads to superior performance in estimation, identification and statistical inference. In particular, robust Bayesian inferences in terms of valid Bayesian credible intervals on both parametric and nonparametric effects can be validated on finite samples. The Markov chain Monte Carlo algorithms of the proposed and alternative models are efficiently implemented in 'C++'.

Maintained by Kun Fan. Last updated 26 days ago.

openblas cpp openmp

6.7 match 3.48 score 3 scripts

jamgreen

lehdr:Grab Longitudinal Employer-Household Dynamics (LEHD) Flat Files

Designed to query Longitudinal Employer-Household Dynamics (LEHD) workplace/residential association and origin-destination flat files and optionally aggregate Census block-level data to block group, tract, county, or state. Data comes from the LODES FTP server <https://lehd.ces.census.gov/data/lodes/LODES8/>.

Maintained by Jamaal Green. Last updated 4 months ago.

3.3 match 62 stars 7.05 score 90 scripts

tomrohmer

UpDown:Detecting Group Disturbances from Longitudinal Observations

Provides an algorithm to detect and characterize disturbances (start, end dates, intensity) that can occur at different hierarchical levels by studying the dynamics of longitudinal observations at the unit level and group level based on Nadaraya-Watson's smoothing curves, but also a shiny app which allows to visualize the observations and the detected disturbances. Finally the package provides a dataframe mimicking a pig farming system subsected to disturbances simulated according to Le et al.(2022) <doi:10.1016/j.animal.2022.100496>.

Maintained by Tom Rohmer. Last updated 1 years ago.

8.5 match 2.70 score 1 scripts

feizhoustat

springer:Sparse Group Variable Selection for Gene-Environment Interactions in the Longitudinal Study

Recently, regularized variable selection has emerged as a powerful tool to identify and dissect gene-environment interactions. Nevertheless, in longitudinal studies with high dimensional genetic factors, regularization methods for G×E interactions have not been systematically developed. In this package, we provide the implementation of sparse group variable selection, based on both the quadratic inference function (QIF) and generalized estimating equation (GEE), to accommodate the bi-level selection for longitudinal G×E studies with high dimensional genomic features. Alternative methods conducting only the group or individual level selection have also been included. The core modules of the package have been developed in C++.

Maintained by Fei Zhou. Last updated 1 years ago.

openblas cpp openmp

6.9 match 4 stars 3.30 score 2 scripts

ropensci

dynamite:Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data

Easy-to-use and efficient interface for Bayesian inference of complex panel (time series) data using dynamic multivariate panel models by Helske and Tikka (2024) <doi:10.1016/j.alcr.2024.100617>. The package supports joint modeling of multiple measurements per individual, time-varying and time-invariant effects, and a wide range of discrete and continuous distributions. Estimation of these dynamic multivariate panel models is carried out via 'Stan'. For an in-depth tutorial of the package, see (Tikka and Helske, 2024) <doi:10.48550/arXiv.2302.01607>.

Maintained by Santtu Tikka. Last updated 19 days ago.

bayesian-inference panel-data stan statistical-models

2.9 match 29 stars 7.92 score 20 scripts

insightsengineering

goshawk:Longitudinal Visualization Functions

Functions that plot and summarize biomarkers/labs of interest. Visualizations include: box plot, correlation plot, density distribution, line plot and spaghetti plot. Data are expected in ADaM structure. Requires analysis subject level (ADSL) and analysis laboratory (ADLB) data sets. Beyond core variables, Limit of Quantification flag variable (LOQFL) is expected with levels 'Y', 'N' or NA.

Maintained by Nick Paszty. Last updated 20 days ago.

plot summarization

3.3 match 5 stars 6.67 score 1 dependents

cran

BayesRGMM:Bayesian Robust Generalized Mixed Models for Longitudinal Data

To perform model estimation using MCMC algorithms with Bayesian methods for incomplete longitudinal studies on binary and ordinal outcomes that are measured repeatedly on subjects over time with drop-outs. Details about the method can be found in the vignette or <https://sites.google.com/view/kuojunglee/r-packages/bayesrgmm>.

Maintained by Kuo-Jung Lee. Last updated 3 years ago.

openblas cpp openmp

9.6 match 2.30 score 20 scripts

florianstijven

Surrogate:Evaluation of Surrogate Endpoints in Clinical Trials

In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration (Grant Agreement no 602552), the Special Research Fund (BOF) of Hasselt University (BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland Mandaat (HBC.2022.0145), and Johnson & Johnson Innovative Medicine.

Maintained by Wim Van Der Elst. Last updated 17 days ago.

3.4 match 1 stars 6.42 score 133 scripts

csblatvia

vardpoor:Variance Estimation for Sample Surveys by the Ultimate Cluster Method

Generation of domain variables, linearization of several non-linear population statistics (the ratio of two totals, weighted income percentile, relative median income ratio, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, Sample Survey Methods And Theory, vol. I: Methods and Applications; vol. II: Theory. 1953, New York: John Wiley and Sons), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y. G., 2015, <doi:10.1111/rssa.12116>). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.

Maintained by Martins Liberts. Last updated 3 years ago.

sampling statistics variance

3.8 match 10 stars 5.78 score 240 scripts

emerykevin

seqimpute:Imputation of Missing Data in Sequence Analysis

Multiple imputation of missing data in a dataset using MICT or MICT-timing methods. The core idea of the algorithms is to fill gaps of missing data, which is the typical form of missing data in a longitudinal setting, recursively from their edges. Prediction is based on either a multinomial or random forest regression model. Covariates and time-dependent covariates can be included in the model.

Maintained by Kevin Emery. Last updated 2 months ago.

5.8 match 3.78 score 1 scripts

famuvie

breedR:Statistical Methods for Forest Genetic Resources Analysts

Statistical tools to build predictive models for the breeders community. It aims to assess the genetic value of individuals under a number of situations, including spatial autocorrelation, genetic/environment interaction and competition. It is under active development as part of the Trees4Future project, particularly developed having forest genetic trials in mind. But can be used for animals or other situations as well.

Maintained by Facundo Muñoz. Last updated 8 months ago.

4.0 match 33 stars 5.44 score 24 scripts

profandyfield

adventr:Interactive R Tutorials to Accompany Field (2016), "An Adventure in Statistics"

Interactive 'R' tutorials written using 'learnr' for Field (2016), "An Adventure in Statistics", <ISBN:9781446210451>. Topics include general workflow in 'R' and 'Rstudio', the 'R' environment and 'tidyverse', summarizing data, model fitting, central tendency, visualising data using 'ggplot2', inferential statistics and robust estimation, hypothesis testing, the general linear model, comparing means, repeated measures designs, factorial designs, multilevel models, growth models, and generalized linear models (logistic regression).

Maintained by Andy Field. Last updated 4 years ago.

3.8 match 36 stars 5.79 score 34 scripts

arne-henningsen

sampleSelection:Sample Selection Models

Two-step and maximum likelihood estimation of Heckman-type sample selection models: standard sample selection models (Tobit-2), endogenous switching regression models (Tobit-5), sample selection models with binary dependent outcome variable, interval regression with sample selection (only ML estimation), and endogenous treatment effects models. These methods are described in the three vignettes that are included in this package and in econometric textbooks such as Greene (2011, Econometric Analysis, 7th edition, Pearson).

Maintained by Arne Henningsen. Last updated 4 years ago.

3.5 match 6.10 score 311 scripts 5 dependents

ucl

RefBasedMI:Reference-Based Imputation for Longitudinal Clinical Trials with Protocol Deviation

Imputation of missing numerical outcomes for a longitudinal trial with protocol deviations. The package uses distinct treatment arm-based assumptions for the unobserved data, following the general algorithm of Carpenter, Roger, and Kenward (2013) <doi:10.1080/10543406.2013.834911>, and the causal model of White, Royes and Best (2020) <doi:10.1080/10543406.2019.1684308>. Sensitivity analyses to departures from these assumptions can be done by the Delta method of Roger. The program uses the same algorithm as the 'mimix' 'Stata' package written by Suzie Cro, with additional coding for the causal model and delta method. The reference-based methods are jump to reference (J2R), copy increments in reference (CIR), copy reference (CR), and the causal model, all of which must specify the reference treatment arm. Other methods are missing at random (MAR) and the last mean carried forward (LMCF). Individual-specific imputation methods (and their reference groups) can be specified.

Maintained by Matteo Quartagno. Last updated 6 months ago.

fortran

5.1 match 3 stars 4.13 score 3 scripts

centerforassessment

SGP:Student Growth Percentiles & Percentile Growth Trajectories

An analytic framework for the calculation of norm- and criterion-referenced academic growth estimates using large scale, longitudinal education assessment data as developed in Betebenner (2009) <doi:10.1111/j.1745-3992.2009.00161.x>.

Maintained by Damian W. Betebenner. Last updated 2 months ago.

percentile-growth-projections quantile-regression sgp sgp-analyses student-growth-percentiles student-growth-projections

2.1 match 20 stars 9.69 score 88 scripts

atanubhattacharjee

longit:High Dimensional Longitudinal Data Analysis Using MCMC

High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). Currently support mixed effect regression with or without missing observations by considering covariance structures. It provides estimates by missing at random and missing not at random assumptions. In this R package, we present Bayesian approaches that statisticians and clinical researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.

Maintained by Atanu Bhattacharjee. Last updated 4 years ago.

jags cpp

20.6 match 1.00 score

rich-payne

dreamer:Dose Response Models for Bayesian Model Averaging

Fits dose-response models utilizing a Bayesian model averaging approach as outlined in Gould (2019) <doi:10.1002/bimj.201700211> for both continuous and binary responses. Longitudinal dose-response modeling is also supported in a Bayesian model averaging framework as outlined in Payne, Ray, and Thomann (2024) <doi:10.1080/10543406.2023.2292214>. Functions for plotting and calculating various posterior quantities (e.g. posterior mean, quantiles, probability of minimum efficacious dose, etc.) are also implemented. Copyright Eli Lilly and Company (2019).

Maintained by Richard Daniel Payne. Last updated 3 months ago.

bayesian dose-response-modeling jags cpp

3.9 match 9 stars 5.26 score 5 scripts

rahmarid

stablespec:Stable Specification Search in Structural Equation Models

An exploratory and heuristic approach for specification search in Structural Equation Modeling. The basic idea is to subsample the original data and then search for optimal models on each subset. Optimality is defined through two objectives: model fit and parsimony. As these objectives are conflicting, we apply a multi-objective optimization methods, specifically NSGA-II, to obtain optimal models for the whole range of model complexities. From these optimal models, we consider only the relevant model specifications (structures), i.e., those that are both stable (occur frequently) and parsimonious and use those to infer a causal model.

Maintained by Ridho Rahmadi. Last updated 8 years ago.

6.0 match 5 stars 3.40 score 7 scripts

cddesja

profileR:Profile Analysis of Multivariate Data in R

A suite of multivariate methods and data visualization tools to implement profile analysis and cross-validation techniques described in Davison & Davenport (2002) <DOI: 10.1037/1082-989X.7.4.468>, Bulut (2013), and other published and unpublished resources. The package includes routines to perform criterion-related profile analysis, profile analysis via multidimensional scaling, moderated profile analysis, generalizability theory, profile analysis by group, and a within-person factor model to derive score profiles.

Maintained by Christopher David Desjardins. Last updated 2 years ago.

3.6 match 3 stars 5.65 score 50 scripts

helenxu

JSM:Semiparametric Joint Modeling of Survival and Longitudinal Data

Maximum likelihood estimation for the semiparametric joint modeling of survival and longitudinal data. Refer to the Journal of Statistical Software article: <doi:10.18637/jss.v093.i02>.

Maintained by Cong Xu. Last updated 5 years ago.

cpp

10.2 match 1.99 score 14 scripts

rudjer

REBayes:Empirical Bayes Estimation and Inference

Kiefer-Wolfowitz maximum likelihood estimation for mixture models and some other density estimation and regression methods based on convex optimization. See Koenker and Gu (2017) REBayes: An R Package for Empirical Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26, <DOI:10.18637/jss.v082.i08>.

Maintained by Roger Koenker. Last updated 9 months ago.

5.2 match 3 stars 3.90 score 27 scripts 1 dependents

christophe314

kml:K-Means for Longitudinal Data

An implementation of k-means specifically design to cluster longitudinal data. It provides facilities to deal with missing value, compute several quality criterion (Calinski and Harabatz, Ray and Turie, Davies and Bouldin, BIC, ...) and propose a graphical interface for choosing the 'best' number of clusters.

Maintained by Christophe Genolini. Last updated 5 months ago.

5.4 match 3.65 score 110 scripts 1 dependents

vdblab

FLORAL:Fit Log-Ratio Lasso Regression for Compositional Data

Log-ratio Lasso regression for continuous, binary, and survival outcomes with (longitudinal) compositional features. See Fei and others (2024) <doi:10.1016/j.crmeth.2024.100899>.

Maintained by Teng Fei. Last updated 27 days ago.

openblas cpp openmp

3.4 match 12 stars 5.85 score 13 scripts

pitakakariki

simr:Power Analysis for Generalised Linear Mixed Models by Simulation

Calculate power for generalised linear mixed models, using simulation. Designed to work with models fit using the 'lme4' package. Described in Green and MacLeod, 2016 <doi:10.1111/2041-210X.12504>.

Maintained by Peter Green. Last updated 2 years ago.

2.0 match 71 stars 9.87 score 756 scripts

bdj34

cloneRate:Estimate Growth Rates from Phylogenetic Trees

Quickly estimate the net growth rate of a population or clone whose growth can be approximated by a birth-death branching process. Input should be phylogenetic tree(s) of clone(s) with edge lengths corresponding to either time or mutations. Based on coalescent results in Johnson et al. (2023) <doi:10.1093/bioinformatics/btad561>. Simulation techniques as well as growth rate methods build on prior work from Lambert A. (2018) <doi:10.1016/j.tpb.2018.04.005> and Stadler T. (2009) <doi:10.1016/j.jtbi.2009.07.018>.

Maintained by Brian Johnson. Last updated 11 months ago.

cpp

4.0 match 4 stars 4.90 score 8 scripts

insightsengineering

rbmi:Reference Based Multiple Imputation

Implements standard and reference based multiple imputation methods for continuous longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular, this package supports deterministic conditional mean imputation and jackknifing as described in Wolbers et al. (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.

Maintained by Isaac Gravestock. Last updated 23 days ago.

2.2 match 18 stars 8.78 score 33 scripts 1 dependents

bfontez

DrBats:Data Representation: Bayesian Approach That's Sparse

Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.

Maintained by Benedicte Fontez. Last updated 3 years ago.

5.5 match 1 stars 3.51 score 13 scripts

r-forge

PST:Probabilistic Suffix Trees and Variable Length Markov Chains

Provides a framework for analysing state sequences with probabilistic suffix trees (PST), the construction that stores variable length Markov chains (VLMC). Besides functions for learning and optimizing VLMC models, the PST library includes many additional tools to analyse sequence data with these models: visualization tools, functions for sequence prediction and artificial sequences generation, as well as for context and pattern mining. The package is specifically adapted to the field of social sciences by allowing to learn VLMC models from sets of individual sequences possibly containing missing values, and by accounting for case weights. The library also allows to compute probabilistic divergence between two models, and to fit segmented VLMC, where sub-models fitted to distinct strata of the learning sample are stored in a single PST. This software results from research work executed within the framework of the Swiss National Centre of Competence in Research LIVES, which is financed by the Swiss National Science Foundation. The authors are grateful to the Swiss National Science Foundation for its financial support.

Maintained by Alexis Gabadinho. Last updated 4 years ago.

5.4 match 3.56 score 36 scripts

cran

spass:Study Planning and Adaptation of Sample Size

Sample size estimation and blinded sample size reestimation in Adaptive Study Design.

Maintained by Marius Placzek. Last updated 4 years ago.

cpp

14.7 match 1.30 score

anna-neufeld

splinetree:Longitudinal Regression Trees and Forests

Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu and Lambert (1999) <doi:10.1080/10618600.1999.10474847>. This method allows trees and forests to be built while considering either level and shape or only shape of response trajectories.

Maintained by Anna Neufeld. Last updated 6 years ago.

3.6 match 4 stars 5.24 score 29 scripts

jiezhou-2

lglasso:Longitudinal Graphical Lasso

For high-dimensional correlated observations, this package carries out the L_1 penalized maximum likelihood estimation of the precision matrix (network) and the correlation parameters. The correlated data can be longitudinal data (may be irregularly spaced) with dampening correlation or clustered data with uniform correlation. For the details of the algorithms, please see the paper Jie Zhou et al. Identifying Microbial Interaction Networks Based on Irregularly Spaced Longitudinal 16S rRNA sequence data <doi:10.1101/2021.11.26.470159>.

Maintained by Jie Zhou. Last updated 1 years ago.

6.0 match 1 stars 3.18 score 5 scripts

manuelneumann

MNLpred:Simulated Predicted Probabilities for Multinomial Logit Models

Functions to easily return simulated predicted probabilities and first differences for multinomial logit models. It takes a specified scenario and a multinomial model to predict probabilities with a set of coefficients, drawn from a simulated sampling distribution. The simulated predictions allow for meaningful plots with means and confidence intervals. The methodological approach is based on the principles laid out by King, Tomz, and Wittenberg (2000) <doi:10.2307/2669316> and Hanmer and Ozan Kalkan (2016) <doi:10.1111/j.1540-5907.2012.00602.x>.

Maintained by Manuel Neumann. Last updated 4 years ago.

3.8 match 12 stars 5.03 score 18 scripts

angabrio

missingHE:Missing Outcome Data in Health Economic Evaluation

Contains a suite of functions for health economic evaluations with missing outcome data. The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models. In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. The methods implemented are described in Mason (2018) <doi:10.1002/hec.3793>, Molenberghs (2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019) <doi:10.1002/sim.8045>.

Maintained by Andrea Gabrio. Last updated 2 years ago.

cost-effectiveness-analysis health-economic-evaluation individual-level-data jags missing-data parametric-modelling sensitivity-analysis cpp

3.3 match 5 stars 5.38 score 24 scripts

nifu-no

saros.base:Base Tools for Semi-Automatic Reporting of Ordinary Surveys

Scaffold an entire web-based report using template chunks, based on a small chapter overview and a dataset. Highly adaptable with prefixes, suffixes, translations, etc. Also contains tools for password-protecting, e.g. for each organization's report on a website. Developed for the common case of a survey across multiple organizations/sites where each organization wants to obtain results for their organization compared with everyone else. See 'saros' (<https://CRAN.R-project.org/package=saros>) for tools used for authors in the drafted reports.

Maintained by Stephan Daus. Last updated 1 months ago.

3.0 match 1 stars 5.98 score 7 scripts

tchouangue

traj:Trajectory Analysis

Implements the three-step procedure proposed by Leffondree et al. (2004) to identify clusters of individual longitudinal trajectories. The procedure involves (1) calculating 24 measures describing the features of the trajectories; (2) using factor analysis to select a subset of the 24 measures and (3) using cluster analysis to identify clusters of trajectories, and classify each individual trajectory in one of the clusters.

Maintained by Gillis Delmas TCHOUANGUE DINKOU. Last updated 2 years ago.

3.3 match 2 stars 5.28 score 21 scripts

shanpengli

JMH:Joint Model of Heterogeneous Repeated Measures and Survival Data

Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data in the presence of heterogeneous within-subject variability, proposed by Li and colleagues (2023) <arXiv:2301.06584>. The proposed method models the within-subject variability of the biomarker and associates it with the risk of the competing risks event. The time-to-event data is modeled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal outcome is modeled using a mixed-effects location and scale model. The association is captured by shared random effects. The model is estimated using an Expectation Maximization algorithm.

Maintained by Shanpeng Li. Last updated 1 months ago.

cpp

4.8 match 3 stars 3.65 score 4 scripts

jiahui1902

MCM:Estimating and Testing Intergenerational Social Mobility Effect

Estimate and test inter-generational social mobility effect on an outcome with cross-sectional or longitudinal data.

Maintained by Jiahui Xu. Last updated 2 years ago.

5.7 match 3.00 score 6 scripts

syksy

hamlet:Hierarchical Optimal Matching and Machine Learning Toolbox

Various functions and algorithms are provided here for solving optimal matching tasks in the context of preclinical cancer studies. Further, various helper and plotting functions are provided for unsupervised and supervised machine learning as well as longitudinal mixed-effects modeling of tumor growth response patterns.

Maintained by Teemu Daniel Laajala. Last updated 2 years ago.

4.0 match 4.18 score 25 scripts 2 dependents

bioc

dcGSA:Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles

Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes.

Maintained by Jiehuan sun. Last updated 5 months ago.

immunooncology genesetenrichment microarray statisticalmethod sequencing rnaseq geneexpression

7.2 match 2.30 score 1 scripts

christophe314

longitudinalData:Longitudinal Data

Tools for longitudinal data and joint longitudinal data (used by packages kml and kml3d).

Maintained by Christophe Genolini. Last updated 5 months ago.

4.7 match 1 stars 3.55 score 65 scripts 11 dependents

briencj

imageData:Aids in Processing and Plotting Data from a Lemna-Tec Scananalyzer

Note that 'imageData' has been superseded by 'growthPheno'. The package 'growthPheno' incorporates all the functionality of 'imageData' and has functionality not available in 'imageData', but some 'imageData' functions have been renamed. The 'imageData' package is no longer maintained, but is retained for legacy purposes.

Maintained by Chris Brien. Last updated 2 years ago.

5.1 match 3.19 score 39 scripts

straussed

DynaRankR:Inferring Longitudinal Dominance Hierarchies

Provides functions for inferring longitudinal dominance hierarchies, which describe dominance relationships and their dynamics in a single latent hierarchy over time. Strauss & Holekamp (in press).

Maintained by Eli D. Strauss. Last updated 5 years ago.

5.4 match 2 stars 3.04 score 11 scripts

sangkyustat

vcPB:Longitudinal PB Varying-Coefficient Groupwise Disparity Model

Estimating the disparity between two groups based on the extended model of the Peters-Belson (PB) method. Our model is the first work on the longitudinal data, and also can set a varying variable to find the complicated association between other variables and the varying variable. Our work is an extension of the Peters-Belson method which was originally published in Peters (1941)<doi:10.1080/00220671.1941.10881036> and Belson (1956)<doi:10.2307/2985420>.

Maintained by Sang Kyu Lee. Last updated 11 months ago.

5.1 match 3.18 score

jinsong-chen

LAWBL:Latent (Variable) Analysis with Bayesian Learning

A variety of models to analyze latent variables based on Bayesian learning: the partially CFA (Chen, Guo, Zhang, & Pan, 2020) <DOI: 10.1037/met0000293>; generalized PCFA; partially confirmatory IRM (Chen, 2020) <DOI: 10.1007/s11336-020-09724-3>; Bayesian regularized EFA <DOI: 10.1080/10705511.2020.1854763>; Fully and partially EFA.

Maintained by Jinsong Chen. Last updated 3 years ago.

3.6 match 6 stars 4.48 score 5 scripts

atanubhattacharjee

jmBIG:Joint Longitudinal and Survival Model for Big Data

Provides analysis tools for big data where the sample size is very large. It offers a suite of functions for fitting and predicting joint models, which allow for the simultaneous analysis of longitudinal and time-to-event data. This statistical methodology is particularly useful in medical research where there is often interest in understanding the relationship between a longitudinal biomarker and a clinical outcome, such as survival or disease progression. This can be particularly useful in a clinical setting where it is important to be able to predict how a patient's health status may change over time. Overall, this package provides a comprehensive set of tools for joint modeling of BIG data obtained as survival and longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility and flexibility make it a valuable resource for researchers in many different fields, particularly in the medical and health sciences.

Maintained by Atanu Bhattacharjee. Last updated 2 months ago.

12.3 match 1.30 score

jjr1234

BClustLonG:A Dirichlet Process Mixture Model for Clustering Longitudinal Gene Expression Data

Many clustering methods have been proposed, but most of them cannot work for longitudinal gene expression data. 'BClustLonG' is a package that allows us to perform clustering analysis for longitudinal gene expression data. It adopts a linear-mixed effects framework to model the trajectory of genes over time, while clustering is jointly conducted based on the regression coefficients obtained from all genes. To account for the correlations among genes and alleviate the high dimensionality challenges, factor analysis models are adopted for the regression coefficients. The Dirichlet process prior distribution is utilized for the means of the regression coefficients to induce clustering. This package allows users to specify which variables to use for clustering (intercepts or slopes or both) and whether a factor analysis model is desired. More details about this method can be found in Jiehuan Sun, et al. (2017) <doi:10.1002/sim.7374>.

Maintained by Jiehuan Sun. Last updated 5 years ago.

openblas cpp

7.9 match 2.00 score 3 scripts

maudewagner

nlive:Automated Estimation of Sigmoidal and Piecewise Linear Mixed Models

Estimation of relatively complex nonlinear mixed-effects models, including the Sigmoidal Mixed Model and the Piecewise Linear Mixed Model with abrupt or smooth transition, through a single intuitive line of code and with automated generation of starting values.

Maintained by Maude Wagner. Last updated 10 months ago.

5.3 match 2 stars 3.00 score

cran

slim:Singular Linear Models for Longitudinal Data

Fits singular linear models to longitudinal data. Singular linear models are useful when the number, or timing, of longitudinal observations may be informative about the observations themselves. They are described in Farewell (2010) <doi:10.1093/biomet/asp068>, and are extensions of the linear increments model <doi:10.1111/j.1467-9876.2007.00590.x> to general longitudinal data.

Maintained by Daniel Farewell. Last updated 8 years ago.

5.7 match 2.70 score

sth1402

AsynchLong:Regression Analysis of Sparse Asynchronous Longitudinal Data

Estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent response and covariates are mismatched and observed intermittently within subjects. Kernel weighted estimating equations are used for generalized linear models with either time-invariant or time-dependent coefficients. Cao, H., Li, J., and Fine, J. P. (2016) <doi:10.1214/16-EJS1141>. Cao, H., Zeng, D., and Fine, J. P. (2015) <doi:10.1111/rssb.12086>.

Maintained by Shannon T. Holloway. Last updated 1 years ago.

15.5 match 1.00 score 10 scripts

cmmr

rbiom:Read/Write, Analyze, and Visualize 'BIOM' Data

A toolkit for working with Biological Observation Matrix ('BIOM') files. Read/write all 'BIOM' formats. Compute rarefaction, alpha diversity, and beta diversity (including 'UniFrac'). Summarize counts by taxonomic level. Subset based on metadata. Generate visualizations and statistical analyses. CPU intensive operations are coded in C for speed.

Maintained by Daniel P. Smith. Last updated 6 days ago.

1.7 match 15 stars 9.02 score 117 scripts 6 dependents

milanwiedemann

suddengains:Identify Sudden Gains in Longitudinal Data

Identify sudden gains based on the three criteria outlined by Tang and DeRubeis (1999) <doi:10.1037/0022-006X.67.6.894> to a selection of repeated measures. Sudden losses, defined as the opposite of sudden gains can also be identified. Two different datasets can be created, one including all sudden gains/losses and one including one selected sudden gain/loss for each case. It can extract scores around sudden gains/losses. It can plot the average change around sudden gains/losses and trajectories of individual cases.

Maintained by Milan Wiedemann. Last updated 2 years ago.

change-detection sudden-gains sudden-losses

3.0 match 7 stars 5.15 score 10 scripts

prof-thiagooliveira

lcc:Longitudinal Concordance Correlation

Estimates the longitudinal concordance correlation to access the longitudinal agreement profile. The estimation approach implemented is variance components approach based on polynomial mixed effects regression model, as proposed by Oliveira, Hinde and Zocchi (2018) <doi:10.1007/s13253-018-0321-1>. In addition, non-parametric confidence intervals were implemented using percentile method or normal-approximation based on Fisher Z-transformation.

Maintained by Thiago de Paula Oliveira. Last updated 1 years ago.

5.7 match 2.70 score 7 scripts

ecomets

saemix:Stochastic Approximation Expectation Maximization (SAEM) Algorithm

The 'saemix' package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for 'saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.

Maintained by Emmanuelle Comets. Last updated 1 years ago.

3.6 match 1 stars 4.07 score 165 scripts 6 dependents

gefeizhang

statVisual:Statistical Visualization Tools

Visualization functions in the applications of translational medicine (TM) and biomarker (BM) development to compare groups by statistically visualizing data and/or results of analyses, such as visualizing data by displaying in one figure different groups' histograms, boxplots, densities, scatter plots, error-bar plots, or trajectory plots, by displaying scatter plots of top principal components or dendrograms with data points colored based on group information, or visualizing volcano plots to check the results of whole genome analyses for gene differential expression.

Maintained by Wenfei Zhang. Last updated 5 years ago.

4.9 match 3.00 score 3 scripts

um-kevinhe

pprof:Modeling, Standardization and Testing for Provider Profiling

Implements linear and generalized linear models for provider profiling, incorporating both fixed and random effects. For large-scale providers, the linear profiled-based method and the SerBIN method for binary data reduce the computational burden. Provides post-modeling features, such as indirect and direct standardization measures, hypothesis testing, confidence intervals, and post-estimation visualization. For more information, see Wu et al. (2022) <doi:10.1002/sim.9387>.

Maintained by Xiaohan Liu. Last updated 5 days ago.

openblas cpp openmp

3.6 match 4.08 score 3 scripts

bkeller2

mlmpower:Power Analysis and Data Simulation for Multilevel Models

A declarative language for specifying multilevel models, solving for population parameters based on specified variance-explained effect size measures, generating data, and conducting power analyses to determine sample size recommendations. The specification allows for any number of within-cluster effects, between-cluster effects, covariate effects at either level, and random coefficients. Moreover, the models do not assume orthogonal effects, and predictors can correlate at either level and accommodate models with multiple interaction effects.

Maintained by Brian T. Keller. Last updated 5 months ago.

3.0 match 3 stars 4.88 score 3 scripts

agdamsbo

REDCapCAST:REDCap Metadata Casting and Castellated Data Handling

Casting metadata for REDCap database creation and handling of castellated data using repeated instruments and longitudinal projects in 'REDCap'. Keeps a focused data export approach, by allowing to only export required data from the database. Also for casting new REDCap databases based on datasets from other sources. Originally forked from the R part of 'REDCapRITS' by Paul Egeler. See <https://github.com/pegeler/REDCapRITS>. 'REDCap' (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources (Harris et al (2009) <doi:10.1016/j.jbi.2008.08.010>; Harris et al (2019) <doi:10.1016/j.jbi.2019.103208>).

Maintained by Andreas Gammelgaard Damsbo. Last updated 6 days ago.

2.5 match 1 stars 5.84 score 12 scripts

aallignol

lgtdl:A Set of Methods for Longitudinal Data Objects

A very simple implementation of a class for longitudinal data.

Maintained by Arthur Allignol. Last updated 7 years ago.

9.7 match 1.48 score 5 scripts 1 dependents

luisgarcez11

long2lstmarray:Longitudinal Dataframes into Arrays for Machine Learning Training

An easy tool to transform 2D longitudinal data into 3D arrays suitable for Long short-term memory neural networks training. The array output can be used by the 'keras' package. Long short-term memory neural networks are described in: Hochreiter, S., & Schmidhuber, J. (1997) <doi:10.1162/neco.1997.9.8.1735>.

Maintained by Luis Garcez. Last updated 2 years ago.

3.4 match 3 stars 4.18 score 5 scripts

egenn

rtemis:Machine Learning and Visualization

Advanced Machine Learning and Visualization. Unsupervised Learning (Clustering, Decomposition), Supervised Learning (Classification, Regression), Cross-Decomposition, Bagging, Boosting, Meta-models. Static and interactive graphics.

Maintained by E.D. Gennatas. Last updated 1 months ago.

data-science data-visualization machine-learning machine-learning-library visualization

2.0 match 145 stars 7.09 score 50 scripts 2 dependents

r-forge

copulaData:Data Sets for Copula Modeling

Data sets used for copula modeling in addition to those in the R package 'copula'. These include a random subsample from the US National Education Longitudinal Study (NELS) of 1988 and nursing home data from Wisconsin.

Maintained by Marius Hofert. Last updated 11 days ago.

4.1 match 3.43 score 2 scripts

vivianephilipps

marqLevAlg:A Parallelized General-Purpose Optimization Based on Marquardt-Levenberg Algorithm

This algorithm provides a numerical solution to the problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than the Gauss-Newton-like algorithm when starting from points very far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2021 <doi:10.32614/RJ-2021-089>.

Maintained by Viviane Philipps. Last updated 1 years ago.

fortran

2.2 match 7 stars 6.52 score 12 scripts 10 dependents

vivianephilipps

weightQuant:Weights for Incomplete Longitudinal Data and Quantile Regression

Estimation of observation-specific weights for incomplete longitudinal data and bootstrap procedure for weighted quantile regressions. See Jacqmin-Gadda, Rouanet, Mba, Philipps, Dartigues (2020) for details <doi:10.1177/0962280220909986>.

Maintained by Viviane Philipps. Last updated 3 years ago.

5.2 match 1 stars 2.70 score 3 scripts

yelleknek

MBESS:The MBESS R Package

Implements methods that are useful in designing research studies and analyzing data, with particular emphasis on methods that are developed for or used within the behavioral, educational, and social sciences (broadly defined). That being said, many of the methods implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a suite of functions for a variety of related topics, such as effect sizes, confidence intervals for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and minimum-risk point estimation perspectives), mediation analysis, various properties of distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but MBESS became more general and now contains methods applicable and used in a wide variety of fields and is an orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has greatly benefited from others, see <https://www3.nd.edu/~kkelley/site/MBESS.html> for a detailed list of those that have contributed and other details.

Maintained by Ken Kelley. Last updated 1 years ago.

1.7 match 2 stars 8.21 score 274 scripts 23 dependents

jpan928

bayesassurance:Bayesian Assurance Computation

Computes Bayesian assurance under various settings characterized by different assumptions and objectives, including precision-based conditions, credible intervals, and goal functions. All simulation-based functions included in this package rely on a two-stage Bayesian method that assigns two distinct priors to evaluate the probability of observing a positive outcome, which addresses subtle limitations that take place when using the standard single-prior approach. For more information, please refer to Pan and Banerjee (2021) <arXiv:2112.03509>.

Maintained by Jane Pan. Last updated 3 years ago.

3.0 match 1 stars 4.54 score 23 scripts

skyebend

networkDynamicData:Dynamic (Longitudinal) Network Datasets

A collection of dynamic network data sets from various sources and multiple authors represented as 'networkDynamic'-formatted objects.

Maintained by Skye Bender-deMoll. Last updated 9 years ago.

6.6 match 3 stars 2.07 score 39 scripts

minatonakazawa

fmsb:Functions for Medical Statistics Book with some Demographic Data

Several utility functions for the book entitled "Practices of Medical and Health Data Analysis using R" (Pearson Education Japan, 2007) with Japanese demographic data and some demographic analysis related functions.

Maintained by Minato Nakazawa. Last updated 1 years ago.

1.8 match 3 stars 7.74 score 1.9k scripts 23 dependents

tmsalab

edmdata:Data Sets for Psychometric Modeling

Collection of data sets from various assessments that can be used to evaluate psychometric models. These data sets have been analyzed in the following papers that introduced new methodology as part of the application section: Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023) <doi:10.1111/bmsp.12307>, Culpepper, S. A., & Balamuta, J. J. (2021) <doi:10.1080/00273171.2021.1985949>, Yinghan Chen et al. (2021) <doi:10.1007/s11336-021-09750-9>, Yinyin Chen et al. (2020) <doi:10.1007/s11336-019-09693-2>, Culpepper, S. A. (2019a) <doi:10.1007/s11336-019-09683-4>, Culpepper, S. A. (2019b) <doi:10.1007/s11336-018-9643-8>, Culpepper, S. A., & Chen, Y. (2019) <doi:10.3102/1076998618791306>, Culpepper, S. A., & Balamuta, J. J. (2017) <doi:10.1007/s11336-015-9484-7>, and Culpepper, S. A. (2015) <doi:10.3102/1076998615595403>.

Maintained by James Joseph Balamuta. Last updated 6 months ago.

cognitive-diagnostic-models data edm

3.2 match 5 stars 4.18 score 7 scripts 1 dependents

bioc

MSstatsQCgui:A graphical user interface for MSstatsQC package

MSstatsQCgui is a Shiny app which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments.

Maintained by Eralp Dogu. Last updated 5 months ago.

software qualitycontrol proteomics massspectrometry gui

3.4 match 4.00 score 1 scripts

brianaronson

easyPSID:Reading, Formatting, and Organizing the Panel Study of Income Dynamics (PSID)

Provides various functions for reading and preparing the Panel Study of Income Dynamics (PSID) for longitudinal analysis, including functions that read the PSID's fixed width format files directly into R, rename all of the PSID's longitudinal variables so that recurring variables have consistent names across years, simplify assembling longitudinal datasets from cross sections of the PSID Family Files, and export the resulting PSID files into file formats common among other statistical programming languages ('SAS', 'STATA', and 'SPSS').

Maintained by Brian Aronson. Last updated 3 years ago.

7.9 match 1.70 score 1 scripts

khusmann

mxmmod:Measurement Model of Derivatives in 'OpenMx'

Provides a convenient interface in 'OpenMx' for building Estabrook's (2015) <doi:10.1037/a0034523> Measurement Model of Derivatives (MMOD).

Maintained by Kyle D. Husmann. Last updated 4 years ago.

3.6 match 3.70 score 3 scripts

molevolepid

SEEPS:Sequence evolution and epidemiological process simulator

A modular, modern simulation suite and toolkit for simulating transmission networks, phylogenies, and evolutionary pairwise distance matrices under different models and assumptions for viral/sequence evolution. While intially developed for HIV, SEEPS offers modular utilities for custom workflows for extension beyond HIV.

Maintained by Michael Kupperman. Last updated 2 months ago.

biological-sequences epidemiology evolution hiv simulation-framework

3.3 match 1 stars 3.95 score 6 scripts

noahhaber

longitudinalcascade:Longitudinal Cascade

Creates a series of sets of graphics and statistics related to the longitudinal cascade, all included in a single object. The longitudinal cascade inputs longitudinal data to identify gaps in the HIV and related cascades by observing differences using time to event and survival methods. The stage definitions are set by the user, with default standard options. Outputs include graphics, datasets, and formal statistical tests.

Maintained by Noah Haber. Last updated 2 years ago.

6.6 match 2.00 score 4 scripts

cran

REEMtree:Regression Trees with Random Effects for Longitudinal (Panel) Data

A data mining approach for longitudinal and clustered data, which combines the structure of mixed effects model with tree-based estimation methods. See Sela, R.J. and Simonoff, J.S. (2012) RE-EM trees: a data mining approach for longitudinal and clustered data <doi:10.1007/s10994-011-5258-3>.

Maintained by Wenbo Jing. Last updated 1 years ago.

5.4 match 2 stars 2.43 score 67 scripts

ecomets

npde:Normalised Prediction Distribution Errors for Nonlinear Mixed-Effect Models

Provides routines to compute normalised prediction distribution errors, a metric designed to evaluate non-linear mixed effect models such as those used in pharmacokinetics and pharmacodynamics.

Maintained by Emmanuelle Comets. Last updated 1 years ago.

3.5 match 3.70 score 119 scripts 7 dependents

pixushi

tempted:Temporal Tensor Decomposition, a Dimensionality Reduction Tool for Longitudinal Multivariate Data

TEMPoral TEnsor Decomposition (TEMPTED), is a dimension reduction method for multivariate longitudinal data with varying temporal sampling. It formats the data into a temporal tensor and decomposes it into a summation of low-dimensional components, each consisting of a subject loading vector, a feature loading vector, and a continuous temporal loading function. These loadings provide a low-dimensional representation of subjects or samples and can be used to identify features associated with clusters of subjects or samples. TEMPTED provides the flexibility of allowing subjects to have different temporal sampling, so time points do not need to be binned, and missing time points do not need to be imputed.

Maintained by Pixu Shi. Last updated 10 months ago.

3.3 match 14 stars 3.92 score 12 scripts

johnnyzhz

WebPower:Basic and Advanced Statistical Power Analysis

This is a collection of tools for conducting both basic and advanced statistical power analysis including correlation, proportion, t-test, one-way ANOVA, two-way ANOVA, linear regression, logistic regression, Poisson regression, mediation analysis, longitudinal data analysis, structural equation modeling and multilevel modeling. It also serves as the engine for conducting power analysis online at <https://webpower.psychstat.org>.

Maintained by Zhiyong Zhang. Last updated 6 months ago.

2.3 match 8 stars 5.52 score 128 scripts

bioc

timecourse:Statistical Analysis for Developmental Microarray Time Course Data

Functions for data analysis and graphical displays for developmental microarray time course data.

Maintained by Yu Chuan Tai. Last updated 5 months ago.

microarray timecourse differentialexpression

3.3 match 3.90 score 7 scripts

cran

phoenics:Pathways Longitudinal and Differential Analysis in Metabolomics

Perform a differential analysis at pathway level based on metabolite quantifications and information on pathway metabolite composition. The method is based on a Principal Component Analysis step and on a linear mixed model. Automatic query of metabolic pathways is also implemented.

Maintained by Camille Guilmineau. Last updated 2 months ago.

4.7 match 2.70 score

donaldrwilliams

GGMnonreg:Non-Regularized Gaussian Graphical Models

Estimate non-regularized Gaussian graphical models, Ising models, and mixed graphical models. The current methods consist of multiple regression, a non-parametric bootstrap <doi:10.1080/00273171.2019.1575716>, and Fisher z transformed partial correlations <doi:10.1111/bmsp.12173>. Parameter uncertainty, predictability, and network replicability <doi:10.31234/osf.io/fb4sa> are also implemented.

Maintained by Donald Williams. Last updated 3 years ago.

3.6 match 6 stars 3.48 score 4 scripts

indenkun

MissMech:Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random

To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR). For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2014). "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)", Journal of Statistical Software, 56(6), 1-31. <https://www.jstatsoft.org/v56/i06/> <doi:10.18637/jss.v056.i06>.

Maintained by Mao Kobayashi. Last updated 1 years ago.

missing-data

3.5 match 3.54 score 54 scripts

cran

CIMPLE:Analysis of Longitudinal Electronic Health Record (EHR) Data with Possibly Informative Observational Time

Analyzes longitudinal Electronic Health Record (EHR) data with possibly informative observational time. These methods are grouped into two classes depending on the inferential task. One group focuses on estimating the effect of an exposure on a longitudinal biomarker while the other group assesses the impact of a longitudinal biomarker on time-to-diagnosis outcomes. The accompanying paper is Du et al (2024) <doi:10.48550/arXiv.2410.13113>.

Maintained by Howard Baik. Last updated 4 months ago.

7.3 match 1 stars 1.70 score

cran

qrcm:Quantile Regression Coefficients Modeling

Parametric modeling of quantile regression coefficient functions.

Maintained by Paolo Frumento. Last updated 1 years ago.

6.9 match 1.78 score 2 dependents

pmamouris

ImputeLongiCovs:Longitudinal Imputation of Categorical Variables via a Joint Transition Model

Imputation of longitudinal categorical covariates. We use a methodological framework which ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. See Mamouris (2023) <doi:10.1002/sim.9919> for an overview.

Maintained by Pavlos Mamouris. Last updated 1 years ago.

6.1 match 2.00 score 2 scripts

cran

nparLD:Nonparametric Analysis of Longitudinal Data in Factorial Experiments

Performs nonparametric analysis of longitudinal data in factorial experiments. Longitudinal data are those which are collected from the same subjects over time, and they frequently arise in biological sciences. Nonparametric methods do not require distributional assumptions, and are applicable to a variety of data types (continuous, discrete, purely ordinal, and dichotomous). Such methods are also robust with respect to outliers and for small sample sizes.

Maintained by Frank Konietschke. Last updated 3 years ago.

3.7 match 4 stars 3.31 score 51 scripts

zzz1990771

geeVerse:A Comprehensive Analysis of High Dimensional Longitudinal Data

To provide a comprehensive analysis of high dimensional longitudinal data,this package provides analysis for any combination of 1) simultaneous variable selection and estimation, 2) mean regression or quantile regression for heterogeneous data, 3) cross-sectional or longitudinal data, 4) balanced or imbalanced data, 5) moderate, high or even ultra-high dimensional data, via computationally efficient implementations of penalized generalized estimating equations.

Maintained by Tianhai Zu. Last updated 4 months ago.

cpp

3.7 match 3.30 score 5 scripts

atanubhattacharjee

ILRCM:Convert Irregular Longitudinal Data to Regular Intervals and Perform Clustering

Convert irregularly spaced longitudinal data into regular intervals for further analysis, and perform clustering using advanced machine learning techniques. The package is designed for handling complex longitudinal datasets, optimizing them for research in healthcare, demography, and other fields requiring temporal data modeling.

Maintained by Atanu Bhattacharjee. Last updated 3 months ago.

12.2 match 1.00 score

chop-cgtinformatics

REDCapTidieR:Extract 'REDCap' Databases into Tidy 'Tibble's

Convert 'REDCap' exports into tidy tables for easy handling of 'REDCap' repeat instruments and event arms.

Maintained by Richard Hanna. Last updated 1 months ago.

redcap redcap-api tidy-data

1.5 match 35 stars 8.08 score 36 scripts

salilkoner

fPASS:Power and Sample Size for Projection Test under Repeated Measures

Computes the power and sample size (PASS) required to test for the difference in the mean function between two groups under a repeatedly measured longitudinal or sparse functional design. See the manuscript by Koner and Luo (2023) <https://salilkoner.github.io/assets/PASS_manuscript.pdf> for details of the PASS formula and computational details. The details of the testing procedure for univariate and multivariate response are presented in Wang (2021) <doi:10.1214/21-EJS1802> and Koner and Luo (2023) <arXiv:2302.05612> respectively.

Maintained by Salil Koner. Last updated 2 years ago.

3.2 match 3.70 score 3 scripts

kwstat

agridat:Agricultural Datasets

Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more.

Maintained by Kevin Wright. Last updated 27 days ago.

data

1.1 match 125 stars 11.02 score 1.7k scripts 2 dependents

jaromilfrossard

permuco:Permutation Tests for Regression, (Repeated Measures) ANOVA/ANCOVA and Comparison of Signals

Functions to compute p-values based on permutation tests. Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) <doi:10.1016/j.csda.2010.02.015> ; Kherad-Pajouh, S., & Renaud, O. (2014) <doi:10.1007/s00362-014-0617-3> ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) <doi:10.1016/j.neuroimage.2014.01.060>). An extension for the comparison of signals issued from experimental conditions (e.g. EEG/ERP signals) is provided. Several corrections for multiple testing are possible, including the cluster-mass statistic (Maris, E., & Oostenveld, R. (2007) <doi:10.1016/j.jneumeth.2007.03.024>) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) <doi:10.1016/j.neuroimage.2008.03.061>).

Maintained by Jaromil Frossard. Last updated 7 months ago.

cpp

1.8 match 13 stars 6.57 score 81 scripts

m-signo

ptmixed:Poisson-Tweedie Generalized Linear Mixed Model

Fits the Poisson-Tweedie generalized linear mixed model described in Signorelli et al. (2021, <doi:10.1177/1471082X20936017>). Likelihood approximation based on adaptive Gauss Hermite quadrature rule.

Maintained by Mirko Signorelli. Last updated 3 years ago.

5.5 match 2.15 score 14 scripts

adzafirov

FREEtree:Tree Method for High Dimensional Longitudinal Data

A tree-based method for high dimensional longitudinal data with correlated features. 'FREEtree' deals with longitudinal data by using a piecewise random effect model. It also exploits the network structure of the features, by first clustering them using Weighted Gene Co-expression Network Analysis ('WGCNA'). It then conducts a screening step within each cluster of features and a selecting step among the surviving features, which provides a relatively unbiased way to do feature selection. By using dominant principle components as regression variables at each leaf and the original features as splitting variables at splitting nodes, 'FREEtree' maintains 'interpretability' and improves computational efficiency.

Maintained by Athanasse Zafirov. Last updated 5 years ago.

3.7 match 3 stars 3.18 score 2 scripts

christophe314

kml3d:K-Means for Joint Longitudinal Data

An implementation of k-means specifically design to cluster joint trajectories (longitudinal data on several variable-trajectories). Like 'kml', it provides facilities to deal with missing value, compute several quality criterion (Calinski and Harabatz, Ray and Turie, Davies and Bouldin, BIC,...) and propose a graphical interface for choosing the 'best' number of clusters. In addition, the 3D graph representing the mean joint-trajectories of each cluster can be exported through LaTeX in a 3D dynamic rotating PDF graph.

Maintained by Christophe Genolini. Last updated 5 months ago.

5.2 match 2.23 score 34 scripts