R-universe search: trial

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

722.2 match 125 stars 11.02 score 1.7k scripts 2 dependents

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.

103.7 match 1 stars 6.42 score 133 scripts

rfhb

ctrdata:Retrieve and Analyze Clinical Trials in Public Registers

A system for querying, retrieving and analyzing protocol- and results-related information on clinical trials from four public registers, the 'European Union Clinical Trials Register' ('EUCTR', <https://www.clinicaltrialsregister.eu/>), 'ClinicalTrials.gov' (<https://clinicaltrials.gov/> and also translating queries the retired classic interface), the 'ISRCTN' (<http://www.isrctn.com/>) and the 'European Union Clinical Trials Information System' ('CTIS', <https://euclinicaltrials.eu/>). Trial information is downloaded, converted and stored in a database ('PostgreSQL', 'SQLite', 'DuckDB' or 'MongoDB'; via package 'nodbi'). Documents in registers associated with trials can also be downloaded. Other functions implement trial concepts canonically across registers, identify deduplicated records, easily find and extract variables (fields) of interest even from complex nested data as used by the registers, merge variables and update queries. The package can be used for meta-analysis and trend-analysis of the design and conduct as well as of the results of clinical trials across registers.

Maintained by Ralf Herold. Last updated 8 hours ago.

clinical-data clinical-research clinical-studies clinical-trials ctgov database duckdb mongodb nodbi postgresql register sqlite studies trial

54.1 match 45 stars 7.92 score 32 scripts

thevaachandereng

bayesCT:Simulation and Analysis of Adaptive Bayesian Clinical Trials

Simulation and analysis of Bayesian adaptive clinical trials for binomial, continuous, and time-to-event data types, incorporates historical data and allows early stopping for futility or early success. The package uses novel and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up, and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data into the analysis via the power prior or non-informative priors.

Maintained by Thevaa Chandereng. Last updated 5 years ago.

adaptive bayesian-methods bayesian-trial clinical-trials statistical-power

54.3 match 14 stars 6.30 score 36 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

32.2 match 25 stars 7.72 score 29 scripts

nepem-ufsc

metan:Multi Environment Trials Analysis

Performs stability analysis of multi-environment trial data using parametric and non-parametric methods. Parametric methods includes Additive Main Effects and Multiplicative Interaction (AMMI) analysis by Gauch (2013) <doi:10.2135/cropsci2013.04.0241>, Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>, geometric adaptability index by Mohammadi & Amri (2008) <doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart & Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>, genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method, power law residuals (POLAR) statistics by Doring et al. (2015) <doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of variation by Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>, stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>, weighted average of absolute scores by Olivoto et al. (2019a) <doi:10.2134/agronj2019.03.0220>, and multi-trait stability index by Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>. Non-parametric methods includes superiority index by Lin & Binns (1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic stability by Huehn (1990) <doi:10.1007/BF00024241>, TOP third statistic by Fox et al. (1990) <doi:10.1007/BF00040364>. Functions for computing biometrical analysis such as path analysis, canonical correlation, partial correlation, clustering analysis, and tools for inspecting, manipulating, summarizing and plotting typical multi-environment trial data are also provided.

Maintained by Tiago Olivoto. Last updated 9 days ago.

25.7 match 2 stars 9.48 score 1.3k scripts 2 dependents

hta-pharma

maicplus:Matching Adjusted Indirect Comparison

Facilitates performing matching adjusted indirect comparison (MAIC) analysis where the endpoint of interest is either time-to-event (e.g. overall survival) or binary (e.g. objective tumor response). The method is described by Signorovitch et al (2012) <doi:10.1016/j.jval.2012.05.004>.

Maintained by Isaac Gravestock. Last updated 23 days ago.

31.8 match 5 stars 7.37 score 16 scripts

brockk

escalation:A Modular Approach to Dose-Finding Clinical Trials

Methods for working with dose-finding clinical trials. We provide implementations of many dose-finding clinical trial designs, including the continual reassessment method (CRM) by O'Quigley et al. (1990) <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs are implemented with a common interface. We also offer optional additional classes to tailor the behaviour of all designs, including avoiding skipping doses, stopping after n patients have been treated at the recommended dose, stopping when a toxicity condition is met, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these classes using the pipe operator from 'magrittr', it is simple to tailor the behaviour of a dose-finding design so it behaves how the trialist wants. Having provided a flexible interface for specifying designs, we then provide functions to run simulations and calculate dose-paths for future cohorts of patients.

Maintained by Kristian Brock. Last updated 2 months ago.

27.4 match 15 stars 7.91 score 67 scripts

medianasoft

MedianaDesigner:Power and Sample Size Calculations for Clinical Trials

Efficient simulation-based power and sample size calculations are supported for a broad class of late-stage clinical trials. The following modules are included in the package: Adaptive designs with data-driven sample size or event count re-estimation, Adaptive designs with data-driven treatment selection, Adaptive designs with data-driven population selection, Optimal selection of a futility stopping rule, Event prediction in event-driven trials, Adaptive trials with response-adaptive randomization (experimental module), Traditional trials with multiple objectives (experimental module). Traditional trials with cluster-randomized designs (experimental module).

Maintained by Alex Dmitrienko. Last updated 2 years ago.

cpp

55.3 match 20 stars 3.79 score 31 scripts

zxw834

BayesianPlatformDesignTimeTrend:Simulate and Analyse Bayesian Platform Trial with Time Trend

Simulating the sequential multi-arm multi-stage or platform trial with Bayesian approach using the 'rstan' package, which provides the R interface for the Stan. This package supports fixed ratio and Bayesian adaptive randomization approaches for randomization. Additionally, it allows for the study of time trend problems in platform trials. There are demos available for a multi-arm multi-stage trial with two different null scenarios, as well as for Bayesian trial cutoff screening. The Bayesian adaptive randomisation approaches are described in: Trippa et al. (2012) <doi:10.1200/JCO.2011.39.8420> and Wathen et al. (2017) <doi:10.1177/1740774517692302>. The randomisation algorithm is described in: Zhao W <doi:10.1016/j.cct.2015.06.008>. The analysis methods of time trend effect in platform trial are described in: Saville et al. (2022) <doi:10.1177/17407745221112013> and Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>.

Maintained by Ziyan Wang. Last updated 1 years ago.

analysis bayesian-adaptive-randomisation clinial-trial group-sequential-designs multiarm-multistage-trials platform-trials simulation cpp

44.8 match 4.38 score 12 scripts

cran

MASS:Support Functions and Datasets for Venables and Ripley's MASS

Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002).

Maintained by Brian Ripley. Last updated 16 days ago.

18.4 match 19 stars 10.53 score 11k dependents

rpact-com

rpact:Confirmatory Adaptive Clinical Trial Design and Analysis

Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2016) <doi:10.1007/978-3-319-32562-0>. This includes classical group sequential as well as multi-stage adaptive hypotheses tests that are based on the combination testing principle.

Maintained by Friedrich Pahlke. Last updated 10 days ago.

adaptive-design analysis clinical-trials count-data group-sequential-designs power-calculation sample-size-calculation simulation validated fortran cpp

24.0 match 25 stars 7.98 score 110 scripts 1 dependents

smartdata-analysis-and-statistics

SimTOST:Sample Size Estimation for Bio-Equivalence Trials Through Simulation

Sample size estimation for bio-equivalence trials is supported through a simulation-based approach that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) <doi:10.1007/BF01068419>, Mielke et al. (2018) <doi:10.1080/19466315.2017.1371071>, Shieh (2022) <doi:10.1371/journal.pone.0269128>, and Sozu et al. (2015) <doi:10.1007/978-3-319-22005-5>. Comprehensive documentation and vignettes guide users through implementation and interpretation of results.

Maintained by Thomas Debray. Last updated 26 days ago.

mcmc multi-arm multiple-comparisons sample-size-calculation sample-size-estimation trial-simulation openblas cpp

29.3 match 2 stars 6.47 score 7 scripts

ethan-alt

hdbayes:Bayesian Analysis of Generalized Linear Models with Historical Data

User-friendly functions for leveraging (multiple) historical data set(s) for generalized linear models. The package contains functions for sampling from the posterior distribution of a generalized linear model using the prior induced by the Bayesian hierarchical model, power prior by Ibrahim and Chen (2000) <doi:10.1214/ss/1009212673>, normalized power prior by Duan et al. (2006) <doi:10.1002/env.752>, normalized asymptotic power prior by Ibrahim et al. (2015) <doi:10.1002/sim.6728>, commensurate prior by Hobbs et al. (2011) <doi:10.1111/j.1541-0420.2011.01564.x>, robust meta-analytic-predictive prior by Schmidli et al. (2014) <doi:10.1111/biom.12242>, the latent exchangeability prior by Alt et al. (2023) <doi:10.48550/arXiv.2303.05223>, and a normal (or half-normal) prior. Functions for computing the marginal log-likelihood under each of the implemented priors are also included. The package compiles all the 'CmdStan' models once during installation using the 'instantiate' package.

Maintained by Ethan M. Alt. Last updated 6 days ago.

30.2 match 4 stars 6.23 score 7 scripts

pavlakrotka

NCC:Simulation and Analysis of Platform Trials with Non-Concurrent Controls

Design and analysis of flexible platform trials with non-concurrent controls. Functions for data generation, analysis, visualization and running simulation studies are provided. The implemented analysis methods are described in: Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>, Saville et al. (2022) <doi:10.1177/17407745221112013> and Schmidli et al. (2014) <doi:10.1111/biom.12242>.

Maintained by Pavla Krotka. Last updated 6 days ago.

clinical-trials platform-trials simulation statistical-inference jags cpp

27.9 match 5 stars 6.64 score 29 scripts

keaven

gsDesign:Group Sequential Design

Derives group sequential clinical trial designs and describes their properties. Particular focus on time-to-event, binary, and continuous outcomes. Largely based on methods described in Jennison, Christopher and Turnbull, Bruce W., 2000, "Group Sequential Methods with Applications to Clinical Trials" ISBN: 0-8493-0316-8.

Maintained by Keaven Anderson. Last updated 11 days ago.

biostatistics boundaries clinical-trials design spending-functions

13.9 match 51 stars 13.05 score 338 scripts 5 dependents

ineelhere

clintrialx:Connect and Work with Clinical Trials Data Sources

Are you spending too much time fetching and managing clinical trial data? Struggling with complex queries and bulk data extraction? What if you could simplify this process with just a few lines of code? Introducing 'clintrialx' - Fetch clinical trial data from sources like 'ClinicalTrials.gov' <https://clinicaltrials.gov/> and the 'Clinical Trials Transformation Initiative - Access to Aggregate Content of ClinicalTrials.gov' database <https://aact.ctti-clinicaltrials.org/>, supporting pagination and bulk downloads. Also, you can generate HTML reports based on the data obtained from the sources!

Maintained by Indraneel Chakraborty. Last updated 4 days ago.

aact bioinformatics clinical-data clinical-trials clinicaltrialsgov ctti data data-management medical-informatics r-language trials

31.2 match 15 stars 5.76 score 11 scripts

matteo21q

dani:Design and Analysis of Non-Inferiority Trials

Provides tools to help with the design and analysis of non-inferiority trials. These include functions for doing sample size calculations and for analysing non-inferiority trials, using a variety of outcome types and population-level sumamry measures. It also features functions to make trials more resilient by using the concept of non-inferiority frontiers, as described in Quartagno et al. (2019) <arXiv:1905.00241>. Finally it includes function to design and analyse MAMS-ROCI (aka DURATIONS) trials.

Maintained by Matteo Quartagno. Last updated 7 months ago.

32.3 match 2 stars 5.33 score 27 scripts

insightsengineering

tern:Create Common TLGs Used in Clinical Trials

Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials.

Maintained by Joe Zhu. Last updated 2 months ago.

clinical-trials graphs listings nest outputs tables

13.6 match 79 stars 12.62 score 186 scripts 9 dependents

brockk

trialr:Clinical Trial Designs in 'rstan'

A collection of clinical trial designs and methods, implemented in 'rstan' and R, including: the Continual Reassessment Method by O'Quigley et al. (1990) <doi:10.2307/2531628>; EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the two-parameter logistic method of Neuenschwander, Branson & Sponer (2008) <doi:10.1002/sim.3230>; and the Augmented Binary method by Wason & Seaman (2013) <doi:10.1002/sim.5867>; and more. We provide functions to aid model-fitting and analysis. The 'rstan' implementations may also serve as a cookbook to anyone looking to extend or embellish these models. We hope that this package encourages the use of Bayesian methods in clinical trials. There is a preponderance of early phase trial designs because this is where Bayesian methods are used most. If there is a method you would like implemented, please get in touch.

Maintained by Kristian Brock. Last updated 1 years ago.

cpp

19.3 match 41 stars 8.55 score 106 scripts 3 dependents

inceptdk

adaptr:Adaptive Trial Simulator

Package that simulates adaptive (multi-arm, multi-stage) clinical trials using adaptive stopping, adaptive arm dropping, and/or adaptive randomisation. Developed as part of the INCEPT (Intensive Care Platform Trial) project (<https://incept.dk/>), primarily supported by a grant from Sygeforsikringen "danmark" (<https://www.sygeforsikring.dk/>).

Maintained by Anders Granholm. Last updated 11 months ago.

29.6 match 13 stars 5.44 score 14 scripts

merck

simtrial:Clinical Trial Simulation

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

Maintained by Yujie Zhao. Last updated 2 days ago.

cpp

17.6 match 21 stars 9.16 score 52 scripts

therneau

survival:Survival Analysis

Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.

Maintained by Terry M Therneau. Last updated 3 months ago.

7.5 match 400 stars 20.43 score 29k scripts 3.9k dependents

difm-brain

ofpetrial:Design on-Farm Precision Field Agronomic Trials

A comprehensive system for designing and implementing on-farm precision field agronomic trials. You provide field data, tell 'ofpetrial' how to design a trial, and get readily-usable trial design files and a report checks the validity and reliability of the trial design.

Maintained by Taro Mieno. Last updated 3 months ago.

27.7 match 3 stars 5.49 score 23 scripts

gpaux

Mediana:Clinical Trial Simulations

Provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation (CSE) approach. The package supports a broad class of data models (including clinical trials with continuous, binary, survival-type and count-type endpoints as well as multivariate outcomes that are based on combinations of different endpoints), analysis strategies and commonly used evaluation criteria.

Maintained by Gautier Paux. Last updated 4 years ago.

biostatistics clinical-trial-simulations clinical-trials simulations

23.2 match 28 stars 6.52 score 39 scripts

annaseffernick

BEAMR:Bootstrap Evaluation of Association Matrices

A bootstrap-based approach to integrate multiple forms of high dimensional genomic data with multiple clinical endpoints. This method is used to find clinically meaningful groups of genomic features, such as genes or pathways. A manuscript describing this method is in preparation.

Maintained by Anna Eames Seffernick. Last updated 8 months ago.

37.4 match 5 stars 4.00 score 1 scripts

lbau7

basksim:Simulation-Based Calculation of Basket Trial Operating Characteristics

Provides a unified syntax for the simulation-based comparison of different single-stage basket trial designs with a binary endpoint and equal sample sizes in all baskets. Methods include the designs by Baumann et al. (2024) <doi:10.48550/arXiv.2309.06988>, Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>, Berry et al. (2020) <doi:10.1177/1740774513497539>, Neuenschwander et al. (2016) <doi:10.1002/pst.1730> and Psioda et al. (2021) <doi:10.1093/biostatistics/kxz014>. For the latter three designs, the functions are mostly wrappers for functions provided by the packages 'bhmbasket' and 'bmabasket'.

Maintained by Lukas Baumann. Last updated 11 months ago.

jags cpp

42.6 match 1 stars 3.45 score 19 scripts

yayayaoyaoyao

RARtrials:Response-Adaptive Randomization in Clinical Trials

Some response-adaptive randomization methods commonly found in literature are included in this package. These methods include the randomized play-the-winner rule for binary endpoint (Wei and Durham (1978) <doi:10.2307/2286290>), the doubly adaptive biased coin design with minimal variance strategy for binary endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>, Rosenberger and Lachin (2015) <doi:10.1002/9781118742112>) and maximal power strategy targeting Neyman allocation for binary endpoint (Tymofyeyev, Rosenberger, and Hu (2007) <doi:10.1198/016214506000000906>) and RSIHR allocation with each letter representing the first character of the names of the individuals who first proposed this rule (Youngsook and Hu (2010) <doi:10.1198/sbr.2009.0056>, Bello and Sabo (2016) <doi:10.1080/00949655.2015.1114116>), A-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), Aa-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), generalized RSIHR allocation for continuous endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>), Bayesian response-adaptive randomization with a control group using the Thall \& Wathen method for binary and continuous endpoints (Thall and Wathen (2007) <doi:10.1016/j.ejca.2007.01.006>) and the forward-looking Gittins index rule for binary and continuous endpoints (Villar, Wason, and Bowden (2015) <doi:10.1111/biom.12337>, Williamson and Villar (2019) <doi:10.1111/biom.13119>).

Maintained by Chuyao Xu. Last updated 2 months ago.

30.6 match 4.65 score

germaine86

eefAnalytics:Robust Analytical Methods for Evaluating Educational Interventions using Randomised Controlled Trials Designs

Analysing data from evaluations of educational interventions using a randomised controlled trial design. Various analytical tools to perform sensitivity analysis using different methods are supported (e.g. frequentist models with bootstrapping and permutations options, Bayesian models). The included commands can be used for simple randomised trials, cluster randomised trials and multisite trials. The methods can also be used more widely beyond education trials. This package can be used to evaluate other intervention designs using Frequentist and Bayesian multilevel models.

Maintained by Germaine Uwimpuhwe. Last updated 5 months ago.

jags cpp

39.2 match 3.58 score 19 scripts

djnavarro

jaysire:Build jsPsych Experiments in R

The jaysire package allows the user to build browser based behavioral experiments within R by providing an interface to the jsPsych javascript library.

Maintained by Danielle Navarro. Last updated 4 years ago.

32.7 match 45 stars 4.26 score 27 scripts

insightsengineering

teal:Exploratory Web Apps for Analyzing Clinical Trials Data

A 'shiny' based interactive exploration framework for analyzing clinical trials data. 'teal' currently provides a dynamic filtering facility and different data viewers. 'teal' 'shiny' applications are built using standard 'shiny' modules.

Maintained by Dawid Kaledkowski. Last updated 20 days ago.

clinical-trials nest shiny webapp

10.9 match 197 stars 12.68 score 176 scripts 5 dependents

rstudio

gt:Easily Create Presentation-Ready Display Tables

Build display tables from tabular data with an easy-to-use set of functions. With its progressive approach, we can construct display tables with a cohesive set of table parts. Table values can be formatted using any of the included formatting functions. Footnotes and cell styles can be precisely added through a location targeting system. The way in which 'gt' handles things for you means that you don't often have to worry about the fine details.

Maintained by Richard Iannone. Last updated 11 days ago.

docx easy-to-use html latex rtf summary-tables

7.0 match 2.1k stars 18.36 score 20k scripts 112 dependents

cran

epiR:Tools for the Analysis of Epidemiological Data

Tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses.

Maintained by Mark Stevenson. Last updated 2 months ago.

15.0 match 10 stars 8.18 score 10 dependents

openintrostat

openintro:Datasets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

Supplemental functions and data for 'OpenIntro' resources, which includes open-source textbooks and resources for introductory statistics (<https://www.openintro.org/>). The package contains datasets used in our open-source textbooks along with custom plotting functions for reproducing book figures. Note that many functions and examples include color transparency; some plotting elements may not show up properly (or at all) when run in some versions of Windows operating system.

Maintained by Mine Çetinkaya-Rundel. Last updated 2 months ago.

data openintro

10.6 match 240 stars 11.39 score 6.0k scripts

graemeleehickey

bayesDP:Implementation of the Bayesian Discount Prior Approach for Clinical Trials

Functions for data augmentation using the Bayesian discount prior method for single arm and two-arm clinical trials, as described in Haddad et al. (2017) <doi:10.1080/10543406.2017.1300907>. The discount power prior methodology was developed in collaboration with the The Medical Device Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group.

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

bayesian bayesian-inference bayesian-statistics clinical-trials mdic posterior-predictive posterior-probability prior-distribution openblas cpp

20.2 match 5.56 score 20 scripts 1 dependents

kaneplusplus

preference:2-Stage Preference Trial Design and Analysis

Design and analyze two-stage randomized trials with a continuous outcome measure. The package contains functions to compute the required sample size needed to detect a given preference, treatment, and selection effect; alternatively, the package contains functions that can report the study power given a fixed sample size. Finally, analysis functions are provided to test each effect using either summary data (i.e. means, variances) or raw study data. <doi:10.18637/jss.v094.c02> <doi:10.1002/sim.7830>

Maintained by Michael Kane. Last updated 5 years ago.

clinical-trial-design clinical-trials preference-design

34.1 match 3 stars 3.22 score 11 scripts

biometris

statgenSTA:Single Trial Analysis (STA) of Field Trials

Phenotypic analysis of field trials using mixed models with and without spatial components. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).

Maintained by Bart-Jan van Rossum. Last updated 5 months ago.

genetics single-trial-analysis

17.0 match 4 stars 6.30 score 14 scripts 3 dependents

thomasasmith

CRTspat:Workflow for Cluster Randomised Trials with Spillover

Design, workflow and statistical analysis of Cluster Randomised Trials of (health) interventions where there may be spillover between the arms (see <https://thomasasmith.github.io/index.html>).

Maintained by Thomas Smith. Last updated 21 days ago.

jags cpp

16.2 match 4 stars 6.54 score 24 scripts

ralmond

CPTtools:Tools for Creating Conditional Probability Tables

Provides support parameterized tables for Bayesian networks, particularly the IRT-like DiBello tables. Also, provides some tools for visualing the networks.

Maintained by Russell Almond. Last updated 3 months ago.

bayesian-network statistics

20.6 match 1 stars 5.05 score 21 scripts 4 dependents

pharmaverse

admiral:ADaM in R Asset Library

A toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>).

Maintained by Ben Straub. Last updated 4 days ago.

cdisc clinical-trials open-source

7.5 match 236 stars 13.89 score 486 scripts 4 dependents

el-meyer

CohortPlat:Simulation of Cohort Platform Trials for Combination Treatments

A collection of functions dedicated to simulating staggered entry platform trials whereby the treatment under investigation is a combination of two active compounds. In order to obtain approval for this combination therapy, superiority of the combination over the two active compounds and superiority of the two active compounds over placebo need to be demonstrated. A more detailed description of the design can be found in Meyer et al. <DOI:10.1002/pst.2194> and a manual in Meyer et al. <arXiv:2202.02182>.

Maintained by Elias Laurin Meyer. Last updated 1 years ago.

clinical-trials platform-trials simulations

28.0 match 3.70 score 3 scripts

brentkaplan

beezdiscounting:Behavioral Economic Easy Discounting

Facilitates some of the analyses performed in studies of behavioral economic discounting. The package supports scoring of the 27-Item Monetary Choice Questionnaire (see Kaplan et al., 2016; <doi:10.1007/s40614-016-0070-9>), calculating k values (Mazur's simple hyperbolic and exponential) using nonlinear regression, calculating various Area Under the Curve (AUC) measures, plotting regression curves for both fit-to-group and two-stage approaches, checking for unsystematic discounting (Johnson & Bickel, 2008; <doi:10.1037/1064-1297.16.3.264>) and scoring of the minute discounting task (see Koffarnus & Bickel, 2014; <doi:10.1037/a0035973>) using the Qualtrics 5-trial discounting template (see the Qualtrics Minute Discounting User Guide; <doi:10.13140/RG.2.2.26495.79527>), which is also available as a .qsf file in this package.

Maintained by Brent A. Kaplan. Last updated 2 months ago.

5-trial-discounting delay-discounting monetary-choice-questionnaire

27.1 match 2 stars 3.78 score 4 scripts

jrmccombs

RHPCBenchmark:Benchmarks for High-Performance Computing Environments

Microbenchmarks for determining the run time performance of aspects of the R programming environment and packages relevant to high-performance computation. The benchmarks are divided into three categories: dense matrix linear algebra kernels, sparse matrix linear algebra kernels, and machine learning functionality.

Maintained by James McCombs. Last updated 8 years ago.

33.3 match 3.02 score 21 scripts

melodyaowen

crt2power:Designing Cluster-Randomized Trials with Two Continuous Co-Primary Outcomes

Provides methods for powering cluster-randomized trials with two continuous co-primary outcomes using five key design techniques. Includes functions for calculating required sample size and statistical power. For more details on methodology, see Owen et al. (2025) <doi:10.1002/sim.70015>, Yang et al. (2022) <doi:10.1111/biom.13692>, Pocock et al. (1987) <doi:10.2307/2531989>, Vickerstaff et al. (2019) <doi:10.1186/s12874-019-0754-4>, and Li et al. (2020) <doi:10.1111/biom.13212>.

Maintained by Melody Owen. Last updated 2 days ago.

27.2 match 3.60 score 2 scripts

icarda-git

QBMS:Query the Breeding Management System(s)

This R package assists breeders in linking data systems with their analytic pipelines, a crucial step in digitizing breeding processes. It supports querying and retrieving phenotypic and genotypic data from systems like 'EBS' <https://ebs.excellenceinbreeding.org/>, 'BMS' <https://bmspro.io>, 'BreedBase' <https://breedbase.org>, and 'GIGWA' <https://github.com/SouthGreenPlatform/Gigwa2> (using 'BrAPI' <https://brapi.org> calls). Extra helper functions support environmental data sources, including 'TerraClimate' <https://www.climatologylab.org/terraclimate.html> and 'FAO' 'HWSDv2' <https://gaez.fao.org/pages/hwsd> soil database.

Maintained by Khaled Al-Shamaa. Last updated 6 months ago.

12.3 match 8 stars 7.85 score 33 scripts 1 dependents

powerandsamplesize

powertools:Power and Sample Size Tools

Power and sample size calculations for a variety of study designs and outcomes. Methods include t tests, ANOVA (including tests for interactions, simple effects and contrasts), proportions, categorical data (chi-square tests and proportional odds), linear, logistic and Poisson regression, alternative and coprimary endpoints, power for confidence intervals, correlation coefficient tests, cluster randomized trials, individually randomized group treatment trials, multisite trials, treatment-by-covariate interaction effects and nonparametric tests of location. Utilities are provided for computing various effect sizes. Companion package to the book "Power and Sample Size in R", Crespi (2025, ISBN:9781138591622).

Maintained by Catherine M. Crespi. Last updated 4 days ago.

26.3 match 3.65 score 9 scripts

elilillyco

rfacts:R Interface to 'FACTS' on Unix-Like Systems

The 'rfacts' package is an R interface to the Fixed and Adaptive Clinical Trial Simulator ('FACTS') on Unix-like systems. It programmatically invokes 'FACTS' to run clinical trial simulations, and it aggregates simulation output data into tidy data frames. These capabilities provide end-to-end automation for large-scale simulation pipelines, and they enhance computational reproducibility. For more information on 'FACTS' itself, please visit <https://www.berryconsultants.com/software/>.

Maintained by William Michael Landau. Last updated 3 years ago.

clinical-trials facts simulation

19.0 match 7 stars 5.02 score 10 scripts

sterniii3

drugdevelopR:Utility-Based Optimal Phase II/III Drug Development Planning

Plan optimal sample size allocation and go/no-go decision rules for phase II/III drug development programs with time-to-event, binary or normally distributed endpoints when assuming fixed treatment effects or a prior distribution for the treatment effect, using methods from Kirchner et al. (2016) <doi:10.1002/sim.6624> and Preussler (2020). Optimal is in the sense of maximal expected utility, where the utility is a function taking into account the expected cost and benefit of the program. It is possible to extend to more complex settings with bias correction (Preussler S et al. (2020) <doi:10.1186/s12874-020-01093-w>), multiple phase III trials (Preussler et al. (2019) <doi:10.1002/bimj.201700241>), multi-arm trials (Preussler et al. (2019) <doi:10.1080/19466315.2019.1702092>), and multiple endpoints (Kieser et al. (2018) <doi:10.1002/pst.1861>).

Maintained by Lukas D. Sauer. Last updated 2 months ago.

14.3 match 3 stars 6.39 score 15 scripts

kaneplusplus

basket:Basket Trial Analysis

Implementation of multisource exchangeability models for Bayesian analyses of prespecified subgroups arising in the context of basket trial design and monitoring. The R 'basket' package facilitates implementation of the binary, symmetric multi-source exchangeability model (MEM) with posterior inference arising from both exact computation and Markov chain Monte Carlo sampling. Analysis output includes full posterior samples as well as posterior probabilities, highest posterior density (HPD) interval boundaries, effective sample sizes (ESS), mean and median estimations, posterior exchangeability probability matrices, and maximum a posteriori MEMs. In addition to providing "basketwise" analyses, the package includes similar calculations for "clusterwise" analyses for which subgroups are combined into meta-baskets, or clusters, using graphical clustering algorithms that treat the posterior exchangeability probabilities as edge weights. In addition plotting tools are provided to visualize basket and cluster densities as well as their exchangeability. References include Hyman, D.M., Puzanov, I., Subbiah, V., Faris, J.E., Chau, I., Blay, J.Y., Wolf, J., Raje, N.S., Diamond, E.L., Hollebecque, A. and Gervais, R (2015) <doi:10.1056/NEJMoa1502309>; Hobbs, B.P. and Landin, R. (2018) <doi:10.1002/sim.7893>; Hobbs, B.P., Kane, M.J., Hong, D.S. and Landin, R. (2018) <doi:10.1093/annonc/mdy457>; and Kaizer, A.M., Koopmeiners, J.S. and Hobbs, B.P. (2017) <doi:10.1093/biostatistics/kxx031>.

Maintained by Michael J. Kane. Last updated 2 years ago.

16.7 match 4 stars 5.46 score 36 scripts

insightsengineering

chevron:Standard TLGs for Clinical Trials Reporting

Provide standard tables, listings, and graphs (TLGs) libraries used in clinical trials. This package implements a structure to reformat the data with 'dunlin', create reporting tables using 'rtables' and 'tern' with standardized input arguments to enable quick generation of standard outputs. In addition, it also provides comprehensive data checks and script generation functionality.

Maintained by Joe Zhu. Last updated 24 days ago.

clinical-trials graphs listings nest reporting tables

11.0 match 12 stars 8.24 score 12 scripts

insightsengineering

simIDM:Simulating Oncology Trials using an Illness-Death Model

Based on the illness-death model a large number of clinical trials with oncology endpoints progression-free survival (PFS) and overall survival (OS) can be simulated, see Meller, Beyersmann and Rufibach (2019) <doi:10.1002/sim.8295>. The simulation set-up allows for random and event-driven censoring, an arbitrary number of treatment arms, staggered study entry and drop-out. Exponentially, Weibull and piecewise exponentially distributed survival times can be generated. The correlation between PFS and OS can be calculated.

Maintained by Alexandra Erdmann. Last updated 1 years ago.

multistate-models simulation-engine

14.1 match 13 stars 6.26 score 9 scripts

tpatni719

gsMAMS:Group Sequential Designs of Multi-Arm Multi-Stage Trials

It provides functions to generate operating characteristics and to calculate Sequential Conditional Probability Ratio Tests(SCPRT) efficacy and futility boundary values along with sample/event size of Multi-Arm Multi-Stage(MAMS) trials for different outcomes. The package is based on Jianrong Wu, Yimei Li, Liang Zhu (2023) <doi:10.1002/sim.9682>, Jianrong Wu, Yimei Li (2023) "Group Sequential Multi-Arm Multi-Stage Survival Trial Design with Treatment Selection"(Manuscript accepted for publication) and Jianrong Wu, Yimei Li, Shengping Yang (2023) "Group Sequential Multi-Arm Multi-Stage Trial Design with Ordinal Endpoints"(In preparation).

Maintained by Tushar Patni. Last updated 10 months ago.

19.0 match 4.60 score 8 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

5.6 match 393 stars 15.68 score 5.0k scripts 13 dependents

boehringer-ingelheim

BPrinStratTTE:Causal Effects in Principal Strata Defined by Antidrug Antibodies

Bayesian models to estimate causal effects of biological treatments on time-to-event endpoints in clinical trials with principal strata defined by the occurrence of antidrug antibodies. The methodology is based on Frangakis and Rubin (2002) <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>, and here adapted to a specific time-to-event setting.

Maintained by Christian Stock. Last updated 11 months ago.

bayesian-methods causal-inference clinical-trial estimand mcmc-methods pharmaceutical-development principal-stratification simulation stan time-to-event cpp

27.2 match 3.18 score

cran

iAdapt:Two-Stage Adaptive Dose-Finding Clinical Trial Design

Simulate and implement early phase two-stage adaptive dose-finding design for binary and quasi-continuous toxicity endpoints. See Chiuzan et al. (2018) for further reading <DOI:10.1080/19466315.2018.1462727>.

Maintained by Alyssa Vanderbeek. Last updated 4 years ago.

27.1 match 3.18 score 7 scripts

bnaras

ASSISTant:Adaptive Subgroup Selection in Group Sequential Trials

Clinical trial design for subgroup selection in three-stage group sequential trial. Includes facilities for design, exploration and analysis of such trials. An implementation of the initial DEFUSE-3 trial is also provided as a vignette.

Maintained by Balasubramanian Narasimhan. Last updated 5 years ago.

18.6 match 4.54 score 23 scripts

insightsengineering

teal.modules.clinical:'teal' Modules for Standard Clinical Outputs

Provides user-friendly tools for creating and customizing clinical trial reports. By leveraging the 'teal' framework, this package provides 'teal' modules to easily create an interactive panel that allows for seamless adjustments to data presentation, thereby streamlining the creation of detailed and accurate reports.

Maintained by Dawid Kaledkowski. Last updated 16 days ago.

clinical-trials modules nest outputs shiny

8.0 match 34 stars 10.25 score 149 scripts

veseshan

clinfun:Clinical Trial Design and Data Analysis Functions

Utilities to make your clinical collaborations easier if not fun. It contains functions for designing studies such as Simon 2-stage and group sequential designs and for data analysis such as Jonckheere-Terpstra test and estimating survival quantiles.

Maintained by Venkatraman E. Seshan. Last updated 1 years ago.

fortran

10.2 match 5 stars 7.86 score 124 scripts 8 dependents

graemeleehickey

goldilocks:Goldilocks Adaptive Trial Designs for Time-to-Event Endpoints

Implements the Goldilocks adaptive trial design for a time to event outcome using a piecewise exponential model and conjugate Gamma prior distributions. The method closely follows the article by Broglio and colleagues <doi:10.1080/10543406.2014.888569>, which allows users to explore the operating characteristics of different trial designs.

Maintained by Graeme L. Hickey. Last updated 2 months ago.

adaptive bayesian bayesian-statistics clinical-trials statistics cpp

16.1 match 7 stars 4.85 score 4 scripts

rcarragh

c212:Methods for Detecting Safety Signals in Clinical Trials Using Body-Systems (System Organ Classes)

Provides a self-contained set of methods to aid clinical trial safety investigators, statisticians and researchers, in the early detection of adverse events using groupings by body-system or system organ class. This work was supported by the Engineering and Physical Sciences Research Council (UK) (EPSRC) [award reference 1521741] and Frontier Science (Scotland) Ltd. The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12.

Maintained by Raymond Carragher. Last updated 4 months ago.

cpp

19.1 match 4.06 score 57 scripts

higgi13425

medicaldata:Data Package for Medical Datasets

Provides access to well-documented medical datasets for teaching. Featuring several from the Teaching of Statistics in the Health Sciences website <https://www.causeweb.org/tshs/category/dataset/>, a few reconstructed datasets of historical significance in medical research, some reformatted and extended from existing R packages, and some data donations.

Maintained by Peter Higgins. Last updated 2 years ago.

datasets

10.4 match 48 stars 7.43 score 317 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

11.1 match 18 stars 6.87 score 69 scripts

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.

10.9 match 13 stars 6.96 score 63 scripts 13 dependents

friendly

HistData:Data Sets from the History of Statistics and Data Visualization

The 'HistData' package provides a collection of small data sets that are interesting and important in the history of statistics and data visualization. The goal of the package is to make these available, both for instructional use and for historical research. Some of these present interesting challenges for graphics or analysis in R.

Maintained by Michael Friendly. Last updated 10 months ago.

graphics historical-data

8.3 match 63 stars 9.19 score 732 scripts 2 dependents

merck

metalite.ae:Adverse Events Analysis Using 'metalite'

Analyzes adverse events in clinical trials using the 'metalite' data structure. The package simplifies the workflow to create production-ready tables, listings, and figures discussed in the adverse events analysis chapters of "R for Clinical Study Reports and Submission" by Zhang et al. (2022) <https://r4csr.org/>.

Maintained by Yujie Zhao. Last updated 1 months ago.

cdisc clinical-trials metadata

8.0 match 18 stars 9.45 score 31 scripts 2 dependents

bioc

RImmPort:RImmPort: Enabling Ready-for-analysis Immunology Research Data

The RImmPort package simplifies access to ImmPort data for analysis in the R environment. It provides a standards-based interface to the ImmPort study data that is in a proprietary format.

Maintained by Zicheng Hu. Last updated 5 months ago.

biomedicalinformatics dataimport datarepresentation

17.2 match 4.33 score 27 scripts

john-harrold

ruminate:A Pharmacometrics Data Transformation and Analysis Tool

Exploration of pharmacometrics data involves both general tools (transformation and plotting) and specific techniques (non-compartmental analysis). This kind of exploration is generally accomplished by utilizing different packages. The purpose of 'ruminate' is to create a 'shiny' interface to make these tools more broadly available while creating reproducible results.

Maintained by John Harrold. Last updated 6 days ago.

10.5 match 2 stars 7.06 score 84 scripts

batss-dev

BATSS:Bayesian Adaptive Trial Simulator Software (BATSS) for Generalised Linear Models

Defines operating characteristics of Bayesian Adaptive Trials considering a generalised linear model response via Monte Carlo simulations of Bayesian GLM fitted via integrated Laplace approximations (INLA).

Maintained by Dominique-Laurent Couturier. Last updated 6 months ago.

17.8 match 2 stars 4.15 score

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.

13.3 match 8 stars 5.52 score 128 scripts

eagerai

kerastuneR:Interface to 'Keras Tuner'

'Keras Tuner' <https://keras-team.github.io/keras-tuner/> is a hypertuning framework made for humans. It aims at making the life of AI practitioners, hypertuner algorithm creators and model designers as simple as possible by providing them with a clean and easy to use API for hypertuning. 'Keras Tuner' makes moving from a base model to a hypertuned one quick and easy by only requiring you to change a few lines of code.

Maintained by Turgut Abdullayev. Last updated 11 months ago.

hyperparameter-tuning hypertuning keras keras-tuner tensorflow trial

11.0 match 34 stars 6.61 score 48 scripts

bxc147

Epi:Statistical Analysis in Epidemiology

Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data. In particular representation, manipulation, rate estimation and simulation for multistate data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages. Contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data and some useful functions for tabulation and plotting, as well as a number of epidemiological data sets.

Maintained by Bendix Carstensen. Last updated 2 months ago.

7.2 match 4 stars 9.65 score 708 scripts 11 dependents

el-meyer

cats:Cohort Platform Trial Simulation

Cohort plAtform Trial Simulation whereby every cohort consists of two arms, control and experimental treatment. Endpoints are co-primary binary endpoints and decisions are made using either Bayesian or frequentist decision rules. Realistic trial trajectories are simulated and the operating characteristics of the designs are calculated.

Maintained by Elias Laurin Meyer. Last updated 1 years ago.

clinical-trials platform-trials simulations

24.4 match 2.85 score 14 scripts

apariciojohan

agriutilities:Utilities for Data Analysis in Agriculture

Utilities designed to make the analysis of field trials easier and more accessible for everyone working in plant breeding. It provides a simple and intuitive interface for conducting single and multi-environmental trial analysis, with minimal coding required. Whether you're a beginner or an experienced user, 'agriutilities' will help you quickly and easily carry out complex analyses with confidence. With built-in functions for fitting Linear Mixed Models, 'agriutilities' is the ideal choice for anyone who wants to save time and focus on interpreting their results. Some of the functions require the R package 'asreml' for the 'ASReml' software, this can be obtained upon purchase from 'VSN' international <https://vsni.co.uk/software/asreml-r/>.

Maintained by Johan Aparicio. Last updated 2 months ago.

lmm met-analysis plant-breeding

9.2 match 18 stars 7.46 score 88 scripts 1 dependents

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

7.2 match 29 stars 9.43 score 1.7k scripts 1 dependents

merck

metalite:ADaM Metadata Structure

A metadata structure for clinical data analysis and reporting based on Analysis Data Model (ADaM) datasets. The package simplifies clinical analysis and reporting tool development by defining standardized inputs, outputs, and workflow. The package can be used to create analysis and reporting planning grid, mock table, and validated analysis and reporting results based on consistent inputs.

Maintained by Yujie Zhao. Last updated 7 months ago.

cdisc clinical-trials metadata

7.5 match 15 stars 9.01 score 57 scripts 5 dependents

cran

flexmix:Flexible Mixture Modeling

A general framework for finite mixtures of regression models using the EM algorithm is implemented. The E-step and all data handling are provided, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.

Maintained by Bettina Gruen. Last updated 16 days ago.

8.3 match 5 stars 8.19 score 113 dependents

openpharma

crmPack:Object-Oriented Implementation of CRM Designs

Implements a wide range of model-based dose escalation designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. The focus is on Bayesian inference, making it very easy to setup a new design with its own JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanes Bove et al. (2019) <doi:10.18637/jss.v089.i10>.

Maintained by Daniel Sabanes Bove. Last updated 2 months ago.

jags cpp

8.6 match 21 stars 7.79 score 208 scripts

graemeleehickey

adaptDiag:Bayesian Adaptive Designs for Diagnostic Trials

Simulate clinical trials for diagnostic test devices and evaluate the operating characteristics under an adaptive design with futility assessment determined via the posterior predictive probabilities.

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

adaptive bayesian bayesian-statistics clinical-trials diagnostic-tests diagnostics statistics

14.6 match 4 stars 4.60 score 5 scripts

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

7.5 match 30 stars 8.93 score 146 scripts 1 dependents

lightbluetitan

MedDataSets:Comprehensive Medical, Disease, Treatment, and Drug Datasets

Provides an extensive collection of datasets related to medicine, diseases, treatments, drugs, and public health. This package covers topics such as drug effectiveness, vaccine trials, survival rates, infectious disease outbreaks, and medical treatments. The included datasets span various health conditions, including AIDS, cancer, bacterial infections, and COVID-19, along with information on pharmaceuticals and vaccines. These datasets are sourced from the R ecosystem and other R packages, remaining unaltered to ensure data integrity. This package serves as a valuable resource for researchers, analysts, and healthcare professionals interested in conducting medical and public health data analysis in R.

Maintained by Renzo Caceres Rossi. Last updated 5 months ago.

11.4 match 8 stars 5.68 score 60 scripts

samhforbes

eyetrackingR:Eye-Tracking Data Analysis

Addresses tasks along the pipeline from raw data to analysis and visualization for eye-tracking data. Offers several popular types of analyses, including linear and growth curve time analyses, onset-contingent reaction time analyses, as well as several non-parametric bootstrapping approaches. For references to the approach see Mirman, Dixon & Magnuson (2008) <doi:10.1016/j.jml.2007.11.006>, and Barr (2008) <doi:10.1016/j.jml.2007.09.002>.

Maintained by Samuel Forbes. Last updated 2 years ago.

8.2 match 22 stars 7.84 score 60 scripts

crwerner

FieldSimR:Simulation of Plot Errors and Phenotypes in Plant Breeding Field Trials

Simulates plot data in multi-environment field trials with one or more traits. Its core function generates plot errors that capture spatial trend, random error (noise), and extraneous variation, which are combined at a user-defined ratio. Phenotypes can be generated by combining the plot errors with simulated genetic values that capture genotype-by-environment (GxE) interaction using wrapper functions for the R package `AlphaSimR`.

Maintained by Christian Werner. Last updated 2 days ago.

9.1 match 9 stars 7.13 score 62 scripts

prise6

aVirtualTwins:Adaptation of Virtual Twins Method from Jared Foster

Research of subgroups in random clinical trials with binary outcome and two treatments groups. This is an adaptation of the Jared Foster method (<https://www.ncbi.nlm.nih.gov/pubmed/21815180>).

Maintained by Francois Vieille. Last updated 7 years ago.

subgroup-identification trials

14.3 match 4 stars 4.51 score 16 scripts

cran

CFO:CFO-Type Designs in Phase I/II Clinical Trials

In phase I clinical trials, the primary objective is to ascertain the maximum tolerated dose (MTD) corresponding to a specified target toxicity rate. The subsequent phase II trials are designed to examine the potential efficacy of the drug based on the MTD obtained from the phase I trials, with the aim of identifying the optimal biological dose (OBD). The 'CFO' package facilitates the implementation of dose-finding trials by utilizing calibration-free odds type (CFO-type) designs. Specifically, it encompasses the calibration-free odds (CFO) (Jin and Yin (2022) <doi:10.1177/09622802221079353>), randomized CFO (rCFO), precision CFO (pCFO), two-dimensional CFO (2dCFO) (Wang et al. (2023) <doi:10.3389/fonc.2023.1294258>), time-to-event CFO (TITE-CFO) (Jin and Yin (2023) <doi:10.1002/pst.2304>), fractional CFO (fCFO), accumulative CFO (aCFO), TITE-aCFO, and f-aCFO (Fang and Yin (2024) <doi: 10.1002/sim.10127>). It supports phase I/II trials for the CFO design and only phase I trials for the other CFO-type designs. The ‘CFO' package accommodates diverse CFO-type designs, allowing users to tailor the approach based on factors such as dose information inclusion, handling of late-onset toxicity, and the nature of the target drug (single-drug or drug-combination). The functionalities embedded in 'CFO' package include the determination of the dose level for the next cohort, the selection of the MTD for a real trial, and the execution of single or multiple simulations to obtain operating characteristics. Moreover, these functions are equipped with early stopping and dose elimination rules to address safety considerations. Users have the flexibility to choose different distributions, thresholds, and cohort sizes among others for their specific needs. The output of the 'CFO' package can be summary statistics as well as various plots for better visualization. An interactive web application for CFO is available at the provided URL.

Maintained by Jialu Fang. Last updated 4 months ago.

33.6 match 1.90 score

tomdingbiostat

ClinTrialPredict:Predicting and Simulating Clinical Trial with Time-to-Event Endpoint

Predict the course of clinical trial with a time-to-event endpoint for both two-arm and single-arm design. Each of the four primary study design parameters (the expected number of observed events, the number of subjects enrolled, the observation time, and the censoring parameter) can be derived analytically given the other three parameters. And the simulation datasets can be generated based on the design settings.

Maintained by Yang Ding. Last updated 4 months ago.

15.3 match 1 stars 4.18 score

wiesenfa

BDP2:Bayesian Adaptive Designs for Phase II Trials with Binary Endpoint

Tools and workflow to choose design parameters in Bayesian adaptive single-arm phase II trial designs with binary endpoint (response, success) with possible stopping for efficacy and futility at interim analyses. Also contains routines to determine and visualize operating characteristics. See Kopp-Schneider et al. (2018) <doi:10.1002/bimj.201700209>.

Maintained by Manuel Wiesenfarth. Last updated 4 years ago.

16.0 match 1 stars 3.93 score 17 scripts

deepayan

lattice:Trellis Graphics for R

A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements. See ?Lattice for an introduction.

Maintained by Deepayan Sarkar. Last updated 11 months ago.

3.6 match 68 stars 17.33 score 27k scripts 13k 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

14.6 match 3 stars 4.13 score 3 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

10.8 match 7 stars 5.56 score 1 dependents

jessicaweiss

swimplot:Tools for Creating Swimmers Plots using 'ggplot2'

Used for creating swimmers plots with functions to customize the bars, add points, add lines, add text, and add arrows.

Maintained by Jessica Weiss. Last updated 4 years ago.

15.8 match 2 stars 3.76 score 29 scripts

cran

epiDisplay:Epidemiological Data Display Package

Package for data exploration and result presentation. Full 'epicalc' package with data management functions is available at '<https://medipe.psu.ac.th/epicalc/>'.

Maintained by Virasakdi Chongsuvivatwong. Last updated 3 years ago.

10.8 match 1 stars 5.44 score 758 scripts 2 dependents

openpharma

simaerep:Find Clinical Trial Sites Under-Reporting Adverse Events

Monitoring of Adverse Event (AE) reporting in clinical trials is important for patient safety. Sites that are under-reporting AEs can be detected using Bootstrap-based simulations that simulate overall AE reporting. Based on the simulation an AE under-reporting probability is assigned to each site in a given trial (Koneswarakantha 2021 <doi:10.1007/s40264-020-01011-5>).

Maintained by Bjoern Koneswarakantha. Last updated 2 months ago.

ae-reporting clinical-trials

11.1 match 22 stars 5.22 score 25 scripts

mjuraska

seqDesign:Simulation and Group-Sequential Monitoring of Randomized Treatment Efficacy Trials with Time-to-Event Endpoints

A broad spectrum of both event-driven and fixed follow-up preventive vaccine efficacy trial designs, including designs of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases), are implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accommodates the following features: (1) the possibility that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given, (2) hazard ratio and cumulative incidence-based treatment/vaccine efficacy parameters and multiple estimation/hypothesis testing procedures are available, (3) interim/group-sequential monitoring of each treatment group for potential harm, non-efficacy (lack of benefit), efficacy (benefit), and high efficacy, (3) arbitrary alpha spending functions for different monitoring outcomes, (4) arbitrary timing of interim looks, separate for each monitoring outcome, in terms of either event accrual or calendar time, (5) flexible analysis cohort characterization (intention-to-treat vs. per-protocol/as-treated; counting only events for analysis that occur after a specific point in study time), and (6) division of the trial into two stages of time periods where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including a description of monitoring boundaries on multiple scales for the different outcomes; event accrual since trial initiation; probabilities of stopping early for potential harm, non-efficacy, etc.; an unconditional power for intention-to-treat and per-protocol analyses; calendar time to crossing a monitoring boundary or reaching the target number of endpoints if no boundary is crossed; trial duration; unconditional power for comparing treatment efficacies; and the distribution of the number of endpoints within an arbitrary study time interval (e.g., events occurring after the treatments/vaccinations are given), useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design.

Maintained by Michal Juraska. Last updated 2 years ago.

12.6 match 2 stars 4.60 score 7 scripts

easystats

effectsize:Indices of Effect Size

Provide utilities to work with indices of effect size for a wide variety of models and hypothesis tests (see list of supported models using the function 'insight::supported_models()'), allowing computation of and conversion between indices such as Cohen's d, r, odds, etc. References: Ben-Shachar et al. (2020) <doi:10.21105/joss.02815>.

Maintained by Mattan S. Ben-Shachar. Last updated 1 months ago.

anova cohens-d compute conversion correlation effect-size effectsize hacktoberfest hedges-g interpretation standardization standardized statistics

3.5 match 344 stars 16.38 score 1.8k scripts 29 dependents

duolajiang

RCTrep:Validation of Estimates of Treatment Effects in Observational Data

Validates estimates of (conditional) average treatment effects obtained using observational data by a) making it easy to obtain and visualize estimates derived using a large variety of methods (G-computation, inverse propensity score weighting, etc.), and b) ensuring that estimates are easily compared to a gold standard (i.e., estimates derived from randomized controlled trials). 'RCTrep' offers a generic protocol for treatment effect validation based on four simple steps, namely, set-selection, estimation, diagnosis, and validation. 'RCTrep' provides a simple dashboard to review the obtained results. The validation approach is introduced by Shen, L., Geleijnse, G. and Kaptein, M. (2023) <doi:10.21203/rs.3.rs-2559287/v1>.

Maintained by Lingjie Shen. Last updated 2 years ago.

11.6 match 8 stars 4.68 score 12 scripts

massimoaria

dimensionsR:Gathering Bibliographic Records from 'Digital Science Dimensions' Using 'DSL' API

A set of tools to extract bibliographic content from 'Digital Science Dimensions' using 'DSL' API <https://www.dimensions.ai/dimensions-apis/>.

Maintained by Massimo Aria. Last updated 1 years ago.

bibliographic-database bibliography bibliometrics bibliometrix clinical-trials ds-dimensions dsl-api gathering-bibliographic-records grant grants grants-search patents policy-documents publications publications-search

7.5 match 42 stars 7.23 score 7 scripts 3 dependents

distancedevelopment

mrds:Mark-Recapture Distance Sampling

Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.

Maintained by Laura Marshall. Last updated 2 months ago.

6.7 match 4 stars 8.05 score 78 scripts 7 dependents

biometris

statgenGxE:Genotype by Environment (GxE) Analysis

Analysis of multi environment data of plant breeding experiments following the analyses described in Malosetti, Ribaut, and van Eeuwijk (2013), <doi:10.3389/fphys.2013.00044>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).

Maintained by Bart-Jan van Rossum. Last updated 6 months ago.

genetics gxe gxe-modelling multi-trial-analysis

9.7 match 10 stars 5.53 score 17 scripts

alexander-pastukhov

saccadr:Extract Saccades via an Ensemble of Methods Approach

A modular and extendable approach to extract (micro)saccades from gaze samples via an ensemble of methods. Although there is an agreement about a general definition of a saccade, the more specific details are harder to agree upon. Therefore, there are numerous algorithms that extract saccades based on various heuristics, which differ in the assumptions about velocity, acceleration, etc. The package uses three methods (Engbert and Kliegl (2003) <doi:10.1016/S0042-6989(03)00084-1>, Otero-Millan et al. (2014)<doi:10.1167/14.2.18>, and Nyström and Holmqvist (2010) <doi:10.3758/BRM.42.1.188>) to label individual samples and then applies a majority vote approach to identify saccades. The package includes three methods but can be extended via custom functions. It also uses a modular approach to compute velocity and acceleration from noisy samples. Finally, you can obtain methods votes per gaze sample instead of saccades.

Maintained by Alexander Pastukhov. Last updated 2 years ago.

cpp

10.9 match 4 stars 4.90 score 8 scripts

boehringer-ingelheim

tipmap:Tipping Point Analysis for Bayesian Dynamic Borrowing

Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. Further functions facilitate expert elicitation of a primary weight of the informative component of the robust MAP prior and computation of operating characteristics. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider.

Maintained by Christian Stock. Last updated 12 months ago.

bayesian-borrowing bayesian-methods clinical-trial evidence-synthesis extrapolation pediatrics pharmaceutical-development prior-elicitation tipping-point weighting

12.2 match 2 stars 4.38 score 12 scripts

gunhanb

MetaStan:Bayesian Meta-Analysis via 'Stan'

Performs Bayesian meta-analysis, meta-regression and model-based meta-analysis using 'Stan'. Includes binomial-normal hierarchical models and option to use weakly informative priors for the heterogeneity parameter and the treatment effect parameter which are described in Guenhan, Roever, and Friede (2020) <doi:10.1002/jrsm.1370>.

Maintained by Burak Kuersad Guenhan. Last updated 2 years ago.

cpp

10.5 match 8 stars 5.08 score 7 scripts

cran

nlme:Linear and Nonlinear Mixed Effects Models

Fit and compare Gaussian linear and nonlinear mixed-effects models.

Maintained by R Core Team. Last updated 2 months ago.

fortran

4.0 match 6 stars 13.00 score 13k scripts 8.7k dependents

annelyng

RTSA:'Trial Sequential Analysis' for Error Control and Inference in Sequential Meta-Analyses

Frequentist sequential meta-analysis based on 'Trial Sequential Analysis' (TSA) in programmed in Java by the Copenhagen Trial Unit (CTU). The primary function is the calculation of group sequential designs for meta-analysis to be used for planning and analysis of both prospective and retrospective sequential meta-analyses to preserve type-I-error control under sequential testing. 'RTSA' includes tools for sample size and trial size calculation for meta-analysis and core meta-analyses methods such as fixed-effect and random-effects models and forest plots. TSA is described in Wetterslev et. al (2008) <doi:10.1016/j.jclinepi.2007.03.013>. The methods for deriving the group sequential designs are based on Jennison and Turnbull (1999, ISBN:9780849303166).

Maintained by Anne Lyngholm Soerensen. Last updated 7 months ago.

cpp

15.4 match 1 stars 3.32 score 21 scripts

johnaponte

survobj:Objects to Simulate Survival Times

Generate objects that simulate survival times. Random values for the distributions are generated using the method described by Bender (2003) <https://epub.ub.uni-muenchen.de/id/eprint/1716> and Leemis (1987) in Operations Research, 35(6), 892–894.

Maintained by Aponte John. Last updated 7 months ago.

10.8 match 1 stars 4.74 score 11 scripts

merck

pkglite:Compact Package Representations

A tool, grammar, and standard to represent and exchange R package source code as text files. Converts one or more source packages to a text file and restores the package structures from the file.

Maintained by Nan Xiao. Last updated 4 months ago.

clinical-trials ectd packaging-tool pharmaverse

7.5 match 29 stars 6.75 score 12 scripts

dtkaplan

LSTbook:Data and Software for "Lessons in Statistical Thinking"

"Lessons in Statistical Thinking" D.T. Kaplan (2014) <https://dtkaplan.github.io/Lessons-in-statistical-thinking/> is a textbook for a first or second course in statistics that embraces data wrangling, causal reasoning, modeling, statistical adjustment, and simulation. 'LSTbook' supports the student-centered, tidy, pipeline-oriented computing style featured in the book.

Maintained by Daniel Kaplan. Last updated 14 hours ago.

8.0 match 4 stars 6.29 score 27 scripts

sidiwang

snSMART:Small N Sequential Multiple Assignment Randomized Trial Methods

Consolidated data simulation, sample size calculation and analysis functions for several snSMART (small sample sequential, multiple assignment, randomized trial) designs under one library. See Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732 <doi:10.1002/sim.7900>.

Maintained by Michael Kleinsasser. Last updated 5 months ago.

bayesian-analysis clinical-trials rare-disease small-sample jags cpp

12.7 match 1 stars 3.90 score 3 scripts

simnph

SimNPH:Simulate Non-Proportional Hazards

A toolkit for simulation studies concerning time-to-event endpoints with non-proportional hazards. 'SimNPH' encompasses functions for simulating time-to-event data in various scenarios, simulating different trial designs like fixed-followup, event-driven, and group sequential designs. The package provides functions to calculate the true values of common summary statistics for the implemented scenarios and offers common analysis methods for time-to-event data. Helper functions for running simulations with the 'SimDesign' package and for aggregating and presenting the results are also included. Results of the conducted simulation study are available in the paper: "A Comparison of Statistical Methods for Time-To-Event Analyses in Randomized Controlled Trials Under Non-Proportional Hazards", Klinglmüller et al. (2025) <doi:10.1002/sim.70019>.

Maintained by Tobias Fellinger. Last updated 12 days ago.

clinical-trial-simulations non-proportional-hazards statistical-simulation statistics survival-analysis

7.4 match 6 stars 6.63 score 43 scripts

mikejseo

bnma:Bayesian Network Meta-Analysis using 'JAGS'

Network meta-analyses using Bayesian framework following Dias et al. (2013) <DOI:10.1177/0272989X12458724>. Based on the data input, creates prior, model file, and initial values needed to run models in 'rjags'. Able to handle binomial, normal and multinomial arm-level data. Can handle multi-arm trials and includes methods to incorporate covariate and baseline risk effects. Includes standard diagnostics and visualization tools to evaluate the results.

Maintained by Michael Seo. Last updated 1 years ago.

jags cpp

10.6 match 7 stars 4.54 score 7 scripts

lbau7

baskexact:Analytical Calculation of Basket Trial Operating Characteristics

Analytically calculates the operating characteristics of single-stage and two-stage basket trials with equal sample sizes using the power prior design by Baumann et al. (2024) <doi:10.48550/arXiv.2309.06988> and the design by Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>.

Maintained by Lukas Baumann. Last updated 7 months ago.

openblas cpp

9.2 match 2 stars 5.22 score 11 scripts

zabore

ppseq:Design Clinical Trials using Sequential Predictive Probability Monitoring

Functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs.

Maintained by Emily C. Zabor. Last updated 6 months ago.

7.7 match 5 stars 6.23 score 28 scripts

ericsleifer

factorial2x2:Design and Analysis of a 2x2 Factorial Trial

Used for the design and analysis of a 2x2 factorial trial for a time-to-event endpoint. It performs power calculations and significance testing as well as providing estimates of the relevant hazard ratios and the corresponding 95% confidence intervals. Important reference papers include Slud EV. (1994) <https://www.ncbi.nlm.nih.gov/pubmed/8086609> Lin DY, Gong J, Gallo P, Bunn PH, Couper D. (2016) <DOI:10.1111/biom.12507> Leifer ES, Troendle JF, Kolecki A, Follmann DA. (2020) <https://github.com/EricSLeifer/factorial2x2/blob/master/Leifer%20et%20al.%20paper.pdf>.

Maintained by Eric Leifer. Last updated 5 years ago.

17.1 match 2.70 score

r-forge

Sleuth2:Data Sets from Ramsey and Schafer's "Statistical Sleuth (2nd Ed)"

Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed)", Duxbury.

Maintained by Berwin A Turlach. Last updated 1 years ago.

7.8 match 5.70 score 191 scripts

el-meyer

airship:Visualization of Simulated Datasets with Multiple Simulation Input Dimensions

Plots simulation results of clinical trials. Its main feature is allowing users to simultaneously investigate the impact of several simulation input dimensions through dynamic filtering of the simulation results. A more detailed description of the app can be found in Meyer et al. <DOI:10.1016/j.softx.2023.101347> or the vignettes on 'GitHub'.

Maintained by Elias Laurin Meyer. Last updated 3 months ago.

clinical-trials ggplot2 interactive interactive-visualizations simulation visualization

8.0 match 3 stars 5.48 score 2 scripts

boehringer-ingelheim

BayesianMCPMod:Simulate, Evaluate, and Analyze Dose Finding Trials with Bayesian MCPMod

Bayesian MCPMod (Fleischer et al. (2022) <doi:10.1002/pst.2193>) is an innovative method that improves the traditional MCPMod by systematically incorporating historical data, such as previous placebo group data. This R package offers functions for simulating, analyzing, and evaluating Bayesian MCPMod trials with normally distributed endpoints. It enables the assessment of trial designs incorporating historical data across various true dose-response relationships and sample sizes. Robust mixture prior distributions, such as those derived with the Meta-Analytic-Predictive approach (Schmidli et al. (2014) <doi:10.1111/biom.12242>), can be specified for each dose group. Resulting mixture posterior distributions are used in the Bayesian Multiple Comparison Procedure and modeling steps. The modeling step also includes a weighted model averaging approach (Pinheiro et al. (2014) <doi:10.1002/sim.6052>). Estimated dose-response relationships can be bootstrapped and visualized.

Maintained by Stephan Wojciekowski. Last updated 8 days ago.

6.3 match 9 stars 6.94 score 4 scripts

optimal-learning-lab

LKT:Logistic Knowledge Tracing

Computes Logistic Knowledge Tracing ('LKT') which is a general method for tracking human learning in an educational software system. Please see Pavlik, Eglington, and Harrel-Williams (2021) <https://ieeexplore.ieee.org/document/9616435>. 'LKT' is a method to compute features of student data that are used as predictors of subsequent performance. 'LKT' allows great flexibility in the choice of predictive components and features computed for these predictive components. The system is built on top of 'LiblineaR', which enables extremely fast solutions compared to base glm() in R.

Maintained by Philip I. Pavlik Jr.. Last updated 9 months ago.

7.5 match 12 stars 5.84 score 29 scripts

sinnweja

smartDesign:Sequential Multiple Assignment Randomized Trial Design

SMART trial design, as described by He, J., McClish, D., Sabo, R. (2021) <doi:10.1080/19466315.2021.1883472>, includes multiple stages of randomization, where participants are randomized to an initial treatment in the first stage and then subsequently re-randomized between treatments in the following stage.

Maintained by Jason Sinnwell. Last updated 1 years ago.

14.5 match 3.00 score 1 scripts

taylor-arnold

ctrialsgov:Query Data from U.S. National Library of Medicine's Clinical Trials Database

Tools to create and query database from the U.S. National Library of Medicine's Clinical Trials database <https://clinicaltrials.gov/>. Functions provide access a variety of techniques for searching the data using range queries, categorical filtering, and by searching for full-text keywords. Minimal graphical tools are also provided for interactively exploring the constructed data.

Maintained by Taylor Arnold. Last updated 3 years ago.

12.4 match 3.46 score 29 scripts

lmaowisc

rmt:Restricted Mean Time in Favor of Treatment

Contains inferential and graphical routines for comparing two treatment arms in terms of the restricted mean time in favor of treatment.

Maintained by Lu Mao. Last updated 3 months ago.

9.8 match 4.34 score 22 scripts

weberse2

OncoBayes2:Bayesian Logistic Regression for Oncology Dose-Escalation Trials

Bayesian logistic regression model with optional EXchangeability-NonEXchangeability parameter modelling for flexible borrowing from historical or concurrent data-sources. The safety model can guide dose-escalation decisions for adaptive oncology Phase I dose-escalation trials which involve an arbitrary number of drugs. Please refer to Neuenschwander et al. (2008) <doi:10.1002/sim.3230> and Neuenschwander et al. (2016) <doi:10.1080/19466315.2016.1174149> for details on the methodology.

Maintained by Sebastian Weber. Last updated 14 days ago.

cpp

19.7 match 2.18 score 15 scripts

tyee001

VGAM:Vector Generalized Linear and Additive Models

An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes.

Maintained by Thomas Yee. Last updated 1 months ago.

fortran

4.0 match 10 stars 10.67 score 3.6k scripts 169 dependents

insightsengineering

rtables:Reporting Tables

Reporting tables often have structure that goes beyond simple rectangular data. The 'rtables' package provides a framework for declaring complex multi-level tabulations and then applying them to data. This framework models both tabulation and the resulting tables as hierarchical, tree-like objects which support sibling sub-tables, arbitrary splitting or grouping of data in row and column dimensions, cells containing multiple values, and the concept of contextual summary computations. A convenient pipe-able interface is provided for declaring table layouts and the corresponding computations, and then applying them to data.

Maintained by Joe Zhu. Last updated 2 months ago.

pharmaceuticals tables

3.1 match 232 stars 13.65 score 238 scripts 17 dependents

loukiaspin

rnmamod:Bayesian Network Meta-Analysis with Missing Participants

A comprehensive suite of functions to perform and visualise pairwise and network meta-analysis with aggregate binary or continuous missing participant outcome data. The package covers core Bayesian one-stage models implemented in a systematic review with multiple interventions, including fixed-effect and random-effects network meta-analysis, meta-regression, evaluation of the consistency assumption via the node-splitting approach and the unrelated mean effects model (original and revised model proposed by Spineli, (2022) <doi:10.1177/0272989X211068005>), and sensitivity analysis (see Spineli et al., (2021) <doi:10.1186/s12916-021-02195-y>). Missing participant outcome data are addressed in all models of the package (see Spineli, (2019) <doi:10.1186/s12874-019-0731-y>, Spineli et al., (2019) <doi:10.1002/sim.8207>, Spineli, (2019) <doi:10.1016/j.jclinepi.2018.09.002>, and Spineli et al., (2021) <doi:10.1002/jrsm.1478>). The robustness to primary analysis results can also be investigated using a novel intuitive index (see Spineli et al., (2021) <doi:10.1177/0962280220983544>). Methods to evaluate the transitivity assumption quantitatively are provided (see Spineli, (2024) <doi:10.1186/s12874-024-02436-7>). A novel index to facilitate interpretation of local inconsistency is also available (see Spineli, (2024) <doi:0.1186/s13643-024-02680-4>) The package also offers a rich, user-friendly visualisation toolkit that aids in appraising and interpreting the results thoroughly and preparing the manuscript for journal submission. The visualisation tools comprise the network plot, forest plots, panel of diagnostic plots, heatmaps on the extent of missing participant outcome data in the network, league heatmaps on estimation and prediction, rankograms, Bland-Altman plot, leverage plot, deviance scatterplot, heatmap of robustness, barplot of Kullback-Leibler divergence, heatmap of comparison dissimilarities and dendrogram of comparison clustering. The package also allows the user to export the results to an Excel file at the working directory.

Maintained by Loukia Spineli. Last updated 9 days ago.

jags cpp

6.3 match 5 stars 6.64 score 12 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

3.9 match 8 stars 10.32 score 98 scripts 10 dependents

genentech

psborrow2:Bayesian Dynamic Borrowing Analysis and Simulation

Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>. There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from <https://stan-dev.r-universe.dev>.

Maintained by Matt Secrest. Last updated 1 months ago.

bayesian-dynamic-borrowing psborrow2 simulation-study

5.0 match 18 stars 7.87 score 16 scripts

mxrodriguezuvigo

SpATS:Spatial Analysis of Field Trials with Splines

Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models.

Maintained by Maria Xose Rodriguez-Alvarez. Last updated 5 months ago.

7.1 match 8 stars 5.54 score 96 scripts 9 dependents

openpharma

beeca:Binary Endpoint Estimation with Covariate Adjustment

Performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) <https://osf.io/9mp58/>). Implements the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> and Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.

Maintained by Alex Przybylski. Last updated 4 months ago.

7.2 match 6 stars 5.48 score 8 scripts

hengshiyu

cvcrand:Efficient Design and Analysis of Cluster Randomized Trials

Constrained randomization by Raab and Butcher (2001) <doi:10.1002/1097-0258(20010215)20:3%3C351::AID-SIM797%3E3.0.CO;2-C> is suitable for cluster randomized trials (CRTs) with a small number of clusters (e.g., 20 or fewer). The procedure of constrained randomization is based on the baseline values of some cluster-level covariates specified. The intervention effect on the individual outcome can then be analyzed through clustered permutation test introduced by Gail, et al. (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q>. Motivated from Li, et al. (2016) <doi:10.1002/sim.7410>, the package performs constrained randomization on the baseline values of cluster-level covariates and clustered permutation test on the individual-level outcomes for cluster randomized trials.

Maintained by Hengshi Yu. Last updated 1 years ago.

10.6 match 3.70 score 8 scripts

ropensci

workloopR:Analysis of Work Loops and Other Data from Muscle Physiology Experiments

Functions for the import, transformation, and analysis of data from muscle physiology experiments. The work loop technique is used to evaluate the mechanical work and power output of muscle. Josephson (1985) <doi:10.1242/jeb.114.1.493> modernized the technique for application in comparative biomechanics. Although our initial motivation was to provide functions to analyze work loop experiment data, as we developed the package we incorporated the ability to analyze data from experiments that are often complementary to work loops. There are currently three supported experiment types: work loops, simple twitches, and tetanus trials. Data can be imported directly from .ddf files or via an object constructor function. Through either method, data can then be cleaned or transformed via methods typically used in studies of muscle physiology. Data can then be analyzed to determine the timing and magnitude of force development and relaxation (for isometric trials) or the magnitude of work, net power, and instantaneous power among other things (for work loops). Although we do not provide plotting functions, all resultant objects are designed to be friendly to visualization via either base-R plotting or 'tidyverse' functions. This package has been peer-reviewed by rOpenSci (v. 1.1.0).

Maintained by Vikram B. Baliga. Last updated 8 months ago.

ddf muscle-force muscle-physiology-experiments tetanus work-loop workloop

6.6 match 3 stars 5.92 score 46 scripts

ubcxzhang

DesignCTPB:Design Clinical Trials with Potential Biomarker Effect

Applying 'CUDA' 'GPUs' via 'Numba' for optimal clinical design. It allows the user to utilize a 'reticulate' 'Python' environment and run intensive Monte Carlo simulation to get the optimal cutoff for the clinical design with potential biomarker effect, which can guide the realistic clinical trials.

Maintained by Yitao Lu. Last updated 4 years ago.

10.4 match 1 stars 3.70 score 5 scripts

hturner

gnm:Generalized Nonlinear Models

Functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science, and many others. Over-parameterized representations of models are used throughout; functions are provided for inference on estimable parameter combinations, as well as standard methods for diagnostics etc.

Maintained by Heather Turner. Last updated 1 years ago.

generalized-linear-models generalized-nonlinear-models statistical-models openblas

3.6 match 16 stars 10.51 score 290 scripts 21 dependents

agrdatasci

ClimMobTools:API Client for the 'ClimMob' Platform

API client for 'ClimMob', an open source software for decentralized large-N trials with the 'tricot' approach <https://climmob.net/>. Developed by van Etten et al. (2019) <doi:10.1017/S0014479716000739>, it turns the research paradigm on its head; instead of a few researchers designing complicated trials to compare several technologies in search of the best solutions for the target environment, it enables many participants to carry out reasonably simple experiments that taken together can offer even more information. 'ClimMobTools' enables project managers to deep explore and analyse their 'ClimMob' data in R.

Maintained by Kauê de Sousa. Last updated 2 months ago.

agriculture-research citizen-science crowdsourcing randomized-controlled-trials

7.4 match 4 stars 5.08 score 3 scripts

awconway

spiritR:Template for Clinical Trial Protocol

Contains an R Markdown template for a clinical trial protocol adhering to the SPIRIT statement. The SPIRIT (Standard Protocol Items for Interventional Trials) statement outlines recommendations for a minimum set of elements to be addressed in a clinical trial protocol. Also contains functions to create a xml document from the template and upload it to clinicaltrials.gov<https://www.clinicaltrials.gov/> for trial registration.

Maintained by Aaron Conway. Last updated 6 years ago.

9.4 match 2 stars 4.00 score 2 scripts

safetygraphics

safetyGraphics:Interactive Graphics for Monitoring Clinical Trial Safety

A framework for evaluation of clinical trial safety. Users can interactively explore their data using the included 'Shiny' application.

Maintained by Jeremy Wildfire. Last updated 2 years ago.

4.6 match 98 stars 8.18 score 111 scripts

byzheng

expDB:Database for Experiment Dataset

A 'SQLite' database is designed to store all information of experiment-based data including metadata, experiment design, managements, phenotypic values and climate records. The dataset can be imported from an 'Excel' file.

Maintained by Bangyou Zheng. Last updated 1 years ago.

13.8 match 2.70 score 4 scripts

thlytras

miniMeta:Web Application to Run Meta-Analyses

Shiny web application to run meta-analyses. Essentially a graphical front-end to package 'meta' for R. Can be useful as an educational tool, and for quickly analyzing and sharing meta-analyses. Provides output to quickly fill in GRADE (Grading of Recommendations, Assessment, Development and Evaluations) Summary-of-Findings tables. Importantly, it allows further processing of the results inside R, in case more specific analyses are needed.

Maintained by Theodore Lytras. Last updated 9 months ago.

meta-analyses meta-analysis observational-studies randomized-controlled-trials sample-size-calculation shiny

7.9 match 5 stars 4.70 score 3 scripts

metinbulus

PowerUpR:Power Analysis Tools for Multilevel Randomized Experiments

Includes tools to calculate statistical power, minimum detectable effect size (MDES), MDES difference (MDESD), and minimum required sample size for various multilevel randomized experiments with continuous outcomes. Some of the functions can assist with planning multilevel randomized experiments sensetive to detect multilevel moderation (2-1-1, 2-1-2, 2-2-1, and 2-2-2 designs) and multilevel mediation (2-1-1, 2-2-1, 3-1-1, 3-2-1, and 3-3-1 designs). See 'PowerUp!' Excel series at <https://www.causalevaluation.org/>.

Maintained by Metin Bulus. Last updated 4 years ago.

7.8 match 2 stars 4.68 score 24 scripts

isidorogu

RCT:Assign Treatments, Power Calculations, Balances, Impact Evaluation of Experiments

Assists in the whole process of designing and evaluating Randomized Control Trials. Robust treatment assignment by strata/blocks, that handles misfits; Power calculations of the minimum detectable treatment effect or minimum populations; Balance tables of T-test of covariates; Balance Regression: (treatment ~ all x variables) with F-test of null model; Impact_evaluation: Impact evaluation regressions. This function gives you the option to include control_vars, fixed effect variables, cluster variables (for robust SE), multiple endogenous variables and multiple heterogeneous variables (to test treatment effect heterogeneity) summary_statistics: Function that creates a summary statistics table with statistics rank observations in n groups: Creates a factor variable with n groups. Each group has a min and max label attach to each category. Athey, Susan, and Guido W. Imbens (2017) <arXiv:1607.00698>.

Maintained by Isidoro Garcia-Urquieta. Last updated 1 years ago.

6.4 match 6 stars 5.68 score 23 scripts

raphaelhartmann

WienR:Derivatives of the First-Passage Time Density and Cumulative Distribution Function, and Random Sampling from the (Truncated) First-Passage Time Distribution

First, we provide functions to calculate the partial derivative of the first-passage time diffusion probability density function (PDF) and cumulative distribution function (CDF) with respect to the first-passage time t (only for PDF), the upper barrier a, the drift rate v, the relative starting point w, the non-decision time t0, the inter-trial variability of the drift rate sv, the inter-trial variability of the rel. starting point sw, and the inter-trial variability of the non-decision time st0. In addition the PDF and CDF themselves are also provided. Most calculations are done on the logarithmic scale to make it more stable. Since the PDF, CDF, and their derivatives are represented as infinite series, we give the user the option to control the approximation errors with the argument 'precision'. For the numerical integration we used the C library cubature by Johnson, S. G. (2005-2013) <https://github.com/stevengj/cubature>. Numerical integration is required whenever sv, sw, and/or st0 is not zero. Note that numerical integration reduces speed of the computation and the precision cannot be guaranteed anymore. Therefore, whenever numerical integration is used an estimate of the approximation error is provided in the output list. Note: The large number of contributors (ctb) is due to copying a lot of C/C++ code chunks from the GNU Scientific Library (GSL). Second, we provide methods to sample from the first-passage time distribution with or without user-defined truncation from above. The first method is a new adaptive rejection sampler building on the works of Gilks and Wild (1992; <doi:10.2307/2347565>) and Hartmann and Klauer (in press). The second method is a rejection sampler provided by Drugowitsch (2016; <doi:10.1038/srep20490>). The third method is an inverse transformation sampler. The fourth method is a "pseudo" adaptive rejection sampler that builds on the first method. For more details see the corresponding help files.

Maintained by Raphael Hartmann. Last updated 1 years ago.

cpp

10.4 match 2 stars 3.48 score 3 scripts 1 dependents

insightsengineering

rlistings:Clinical Trial Style Data Readout Listings

Listings are often part of the submission of clinical trial data in regulatory settings. We provide a framework for the specific formatting features often used when displaying large datasets in that context.

Maintained by Joe Zhu. Last updated 2 months ago.

3.4 match 28 stars 10.49 score 44 scripts 9 dependents

gasparrini

dlnm:Distributed Lag Non-Linear Models

Collection of functions for distributed lag linear and non-linear models.

Maintained by Antonio Gasparrini. Last updated 3 years ago.

3.5 match 77 stars 10.30 score 392 scripts 6 dependents

pharmaverse

ggsurvfit:Flexible Time-to-Event Figures

Ease the creation of time-to-event (i.e. survival) endpoint figures. The modular functions create figures ready for publication. Each of the functions that add to or modify the figure are written as proper 'ggplot2' geoms or stat methods, allowing the functions from this package to be combined with any function or customization from 'ggplot2' and other 'ggplot2' extension packages.

Maintained by Daniel D. Sjoberg. Last updated 2 months ago.

3.4 match 76 stars 10.50 score 640 scripts 2 dependents

cran

randomizeR:Randomization for Clinical Trials

This tool enables the user to choose a randomization procedure based on sound scientific criteria. It comprises the generation of randomization sequences as well the assessment of randomization procedures based on carefully selected criteria. Furthermore, 'randomizeR' provides a function for the comparison of randomization procedures.

Maintained by Ralf-Dieter Hilgers. Last updated 1 years ago.

10.6 match 2 stars 3.38 score 1 dependents

merck

r2rtf:Easily Create Production-Ready Rich Text Format (RTF) Tables and Figures

Create production-ready Rich Text Format (RTF) tables and figures with flexible format.

Maintained by Benjamin Wang. Last updated 5 days ago.

3.3 match 78 stars 10.82 score 171 scripts 10 dependents

drj001

wgaim:Whole Genome Average Interval Mapping for QTL Detection and Estimation using ASReml-R

A computationally efficient whole genome approach to detecting and estimating significant QTL in linkage maps using the flexible linear mixed modelling functionality of ASReml-R.

Maintained by Julian Taylor. Last updated 7 months ago.

6.9 match 2 stars 5.20 score 16 scripts

raybaser

PROscorer:Functions to Score Commonly-Used Patient-Reported Outcome (PRO) Measures and Other Psychometric Instruments

An extensible repository of accurate, up-to-date functions to score commonly used patient-reported outcome (PRO), quality of life (QOL), and other psychometric and psychological measures. 'PROscorer', together with the 'PROscorerTools' package, is a system to facilitate the incorporation of PRO measures into research studies and clinical settings in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote best practices for scoring PRO and PRO-like measures in research. The 'PROscorer' Instrument Descriptions vignette contains descriptions of each instrument scored by 'PROscorer', complete with references. These instrument descriptions are suitable for inclusion in formal study protocol documents, grant proposals, and manuscript Method sections. Each 'PROscorer' function is composed of helper functions from the 'PROscorerTools' package, and users are encouraged to contribute new functions to 'PROscorer'. More scoring functions are currently in development and will be added in future updates.

Maintained by Ray Baser. Last updated 5 months ago.

clinical-trials pros psychometrics qol quality-of-life quality-of-life-questionnaire r-pkg scoring

7.5 match 4 stars 4.75 score 14 scripts

dardisco

survMisc:Miscellaneous Functions for Survival Data

A collection of functions to help in the analysis of right-censored survival data. These extend the methods available in package:survival.

Maintained by Chris Dardis. Last updated 5 years ago.

3.8 match 1 stars 9.49 score 218 scripts 55 dependents

mskcc-epi-bio

PROscorerTools:Tools to Score Patient-Reported Outcome (PRO) and Other Psychometric Measures

Provides a reliable and flexible toolbox to score patient-reported outcome (PRO), Quality of Life (QOL), and other psychometric measures. The guiding philosophy is that scoring errors can be eliminated by using a limited number of well-tested, well-behaved functions to score PRO-like measures. The workhorse of the package is the 'scoreScale' function, which can be used to score most single-scale measures. It can reverse code items that need to be reversed before scoring and pro-rate scores for missing item data. Currently, three different types of scores can be output: summed item scores, mean item scores, and scores scaled to range from 0 to 100. The 'PROscorerTools' functions can be used to write new functions that score more complex measures. In fact, 'PROscorerTools' functions are the building blocks of the scoring functions in the 'PROscorer' package (which is a repository of functions that score specific commonly-used instruments). Users are encouraged to use 'PROscorerTools' to write scoring functions for their favorite PRO-like instruments, and to submit these functions for inclusion in 'PROscorer' (a tutorial vignette will be added soon). The long-term vision for the 'PROscorerTools' and 'PROscorer' packages is to provide an easy-to-use system to facilitate the incorporation of PRO measures into research studies in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote "best practices" for scoring and describing PRO-like measures in research.

Maintained by Ray Baser. Last updated 1 years ago.

clinical-trials pros psychometrics qol quality-of-life questionnaire survey

7.5 match 2 stars 4.73 score 18 scripts 1 dependents

ctu-bern

accrualPlot:Accrual Plots and Predictions for Clinical Trials

Tracking accrual in clinical trials is important for trial success. If accrual is too slow, the trial will take too long and be too expensive. If accrual is much faster than expected, time sensitive tasks such as the writing of statistical analysis plans might need to be rushed. 'accrualPlot' provides functions to aid the tracking of accrual and predict when a trial will reach it's intended sample size.

Maintained by Lukas Bütikofer. Last updated 7 months ago.

recruitment

7.0 match 3 stars 5.01 score 17 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.

4.0 match 18 stars 8.78 score 33 scripts 1 dependents

cran

YEAB:Analyze Data from Analysis of Behavior Experiments

Analyze data from behavioral experiments conducted using 'MED-PC' software developed by Med Associates Inc. Includes functions to fit exponential and hyperbolic models for delay discounting tasks, exponential mixtures for inter-response times, and Gaussian plus ramp models for peak procedure data, among others. For more details, refer to Alcala et al. (2023) <doi:10.31234/osf.io/8aq2j>.

Maintained by Emmanuel Alcala. Last updated 1 months ago.

8.6 match 4.00 score

alenxav

NAM:Nested Association Mapping

Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.

Maintained by Alencar Xavier. Last updated 5 years ago.

cpp

6.0 match 2 stars 5.72 score 44 scripts 1 dependents

wviechtb

metadat:Meta-Analysis Datasets

A collection of meta-analysis datasets for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses.

Maintained by Wolfgang Viechtbauer. Last updated 2 days ago.

dataset datasets meta-analysis

3.3 match 30 stars 10.54 score 65 scripts 93 dependents

bbecker-bayer

interim:Scheduling Interim Analyses in Clinical Trials

Allows the simulation of the recruitment and both the event and treatment phase of a clinical trial. Based on these simulations, the timing of interim analyses can be assessed.

Maintained by Bastian Becker. Last updated 6 years ago.

23.0 match 1.48 score 7 scripts 1 dependents

bioc

randPack:Randomization routines for Clinical Trials

A suite of classes and functions for randomizing patients in clinical trials.

Maintained by Robert Gentleman. Last updated 5 months ago.

statisticalmethod

10.2 match 3.30 score 5 scripts

arminstroebel

atable:Create Tables for Reporting Clinical Trials

Create Tables for Reporting Clinical Trials. Calculates descriptive statistics and hypothesis tests, arranges the results in a table ready for reporting with LaTeX, HTML or Word.

Maintained by Armin Ströbel. Last updated 4 years ago.

7.1 match 9 stars 4.74 score 41 scripts

weiliang

powerSurvEpi:Power and Sample Size Calculation for Survival Analysis of Epidemiological Studies

Functions to calculate power and sample size for testing main effect or interaction effect in the survival analysis of epidemiological studies (non-randomized studies), taking into account the correlation between the covariate of the interest and other covariates. Some calculations also take into account the competing risks and stratified analysis. This package also includes a set of functions to calculate power and sample size for testing main effect in the survival analysis of randomized clinical trials and conditional logistic regression for nested case-control study.

Maintained by Weiliang Qiu. Last updated 4 years ago.

9.0 match 3.72 score 77 scripts 2 dependents

pharmaverse

pharmaversesdtm:SDTM Test Data for the 'Pharmaverse' Family of Packages

A set of Study Data Tabulation Model (SDTM) datasets from the Clinical Data Interchange Standards Consortium (CDISC) pilot project used for testing and developing Analysis Data Model (ADaM) datasets inside the pharmaverse family of packages. SDTM dataset specifications are described in the CDISC SDTM implementation guide, accessible by creating a free account on <https://www.cdisc.org/>.

Maintained by Edoardo Mancini. Last updated 2 days ago.

4.5 match 15 stars 7.40 score 143 scripts

emeyers

NeuroDecodeR:Decode Information from Neural Activity

Neural decoding is method of analyzing neural data that uses a pattern classifiers to predict experimental conditions based on neural activity. 'NeuroDecodeR' is a system of objects that makes it easy to run neural decoding analyses. For more information on neural decoding see Meyers & Kreiman (2011) <doi:10.7551/mitpress/8404.003.0024>.

Maintained by Ethan Meyers. Last updated 1 years ago.

5.1 match 12 stars 6.49 score 17 scripts

begglest

BayesCTDesign:Two Arm Bayesian Clinical Trial Design with and Without Historical Control Data

A set of functions to help clinical trial researchers calculate power and sample size for two-arm Bayesian randomized clinical trials that do or do not incorporate historical control data. At some point during the design process, a clinical trial researcher who is designing a basic two-arm Bayesian randomized clinical trial needs to make decisions about power and sample size within the context of hypothesized treatment effects. Through simulation, the simple_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about treatment effect,control group characteristics, and outcome. If the clinical trial researcher has access to historical control data, then the researcher can design a two-arm Bayesian randomized clinical trial that incorporates the historical data. In such a case, the researcher needs to work through the potential consequences of historical and randomized control differences on trial characteristics, in addition to working through issues regarding power in the context of sample size, treatment effect size, and outcome. If a researcher designs a clinical trial that will incorporate historical control data, the researcher needs the randomized controls to be from the same population as the historical controls. What if this is not the case when the designed trial is implemented? During the design phase, the researcher needs to investigate the negative effects of possible historic/randomized control differences on power, type one error, and other trial characteristics. Using this information, the researcher should design the trial to mitigate these negative effects. Through simulation, the historic_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about historical and randomized control differences as well as treatment effects and outcomes. The results from historic_sim() and simple_sim() can be printed with print_table() and graphed with plot_table() methods. Outcomes considered are Gaussian, Poisson, Bernoulli, Lognormal, Weibull, and Piecewise Exponential. The methods are described in Eggleston et al. (2021) <doi:10.18637/jss.v100.i21>.

Maintained by Barry Eggleston. Last updated 3 years ago.

10.5 match 2 stars 3.15 score 14 scripts

pletschm

aldvmm:Adjusted Limited Dependent Variable Mixture Models

The goal of the package 'aldvmm' is to fit adjusted limited dependent variable mixture models of health state utilities. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package 'aldvmm' uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) <doi:10.1177/1536867X1501500307> using normal component distributions and a multinomial logit model of probabilities of component membership.

Maintained by Mark Pletscher. Last updated 1 years ago.

clinical-trials cost-effectiveness eq5d finite-mixture health-economics hta hui limited-dependent-variable mapping mixture-model patient-reported-outcomes quality-of-life utilities

7.5 match 5 stars 4.40 score 2 scripts

mjuraska

CoRpower:Power Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials

Calculates power for assessment of intermediate biomarker responses as correlates of risk in the active treatment group in clinical efficacy trials, as described in Gilbert, Janes, and Huang, Power/Sample Size Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials (2016, Statistics in Medicine). The methods differ from past approaches by accounting for the level of clinical treatment efficacy overall and in biomarker response subgroups, which enables the correlates of risk results to be interpreted in terms of potential correlates of efficacy/protection. The methods also account for inter-individual variability of the observed biomarker response that is not biologically relevant (e.g., due to technical measurement error of the laboratory assay used to measure the biomarker response), which is important because power to detect a specified correlate of risk effect size is heavily affected by the biomarker's measurement error. The methods can be used for a general binary clinical endpoint model with a univariate dichotomous, trichotomous, or continuous biomarker response measured in active treatment recipients at a fixed timepoint after randomization, with either case-cohort Bernoulli sampling or case-control without-replacement sampling of the biomarker (a baseline biomarker is handled as a trivial special case). In a specified two-group trial design, the computeN() function can initially be used for calculating additional requisite design parameters pertaining to the target population of active treatment recipients observed to be at risk at the biomarker sampling timepoint. Subsequently, the power calculation employs an inverse probability weighted logistic regression model fitted by the tps() function in the 'osDesign' package. Power results as well as the relationship between the correlate of risk effect size and treatment efficacy can be visualized using various plotting functions. To link power calculations for detecting a correlate of risk and a correlate of treatment efficacy, a baseline immunogenicity predictor (BIP) can be simulated according to a specified classification rule (for dichotomous or trichotomous BIPs) or correlation with the biomarker response (for continuous BIPs), then outputted along with biomarker response data under assignment to treatment, and clinical endpoint data for both treatment and placebo groups.

Maintained by Michal Juraska. Last updated 4 years ago.

7.9 match 4.15 score 14 scripts

dmphillippo

multinma:Bayesian Network Meta-Analysis of Individual and Aggregate Data

Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using 'Stan'.

Maintained by David M. Phillippo. Last updated 2 days ago.

statistics cpp

3.6 match 35 stars 9.11 score 163 scripts

hojsgaard

geepack:Generalized Estimating Equation Package

Generalized estimating equations solver for parameters in mean, scale, and correlation structures, through mean link, scale link, and correlation link. Can also handle clustered categorical responses. See e.g. Halekoh and Højsgaard, (2005, <doi:10.18637/jss.v015.i02>), for details.

Maintained by Søren Højsgaard. Last updated 7 months ago.

cpp

3.4 match 1 stars 9.59 score 1.7k scripts 43 dependents

kaifenglu

lrstat:Power and Sample Size Calculation for Non-Proportional Hazards and Beyond

Performs power and sample size calculation for non-proportional hazards model using the Fleming-Harrington family of weighted log-rank tests. The sequentially calculated log-rank test score statistics are assumed to have independent increments as characterized in Anastasios A. Tsiatis (1982) <doi:10.1080/01621459.1982.10477898>. The mean and variance of log-rank test score statistics are calculated based on Kaifeng Lu (2021) <doi:10.1002/pst.2069>. The boundary crossing probabilities are calculated using the recursive integration algorithm described in Christopher Jennison and Bruce W. Turnbull (2000, ISBN:0849303168). The package can also be used for continuous, binary, and count data. For continuous data, it can handle missing data through mixed-model for repeated measures (MMRM). In crossover designs, it can estimate direct treatment effects while accounting for carryover effects. For binary data, it can design Simon's 2-stage, modified toxicity probability-2 (mTPI-2), and Bayesian optimal interval (BOIN) trials. For count data, it can design group sequential trials for negative binomial endpoints with censoring. Additionally, it facilitates group sequential equivalence trials for all supported data types. Moreover, it can design adaptive group sequential trials for changes in sample size, error spending function, number and spacing or future looks. Finally, it offers various options for adjusted p-values, including graphical and gatekeeping procedures.

Maintained by Kaifeng Lu. Last updated 3 months ago.

cpp

5.8 match 2 stars 5.58 score 30 scripts

optad

adoptr:Adaptive Optimal Two-Stage Designs

Optimize one or two-arm, two-stage designs for clinical trials with respect to several implemented objective criteria or custom objectives. Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported. See Pilz et al. (2019) <doi:10.1002/sim.8291> and Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09> for details.

Maintained by Maximilian Pilz. Last updated 5 months ago.

4.5 match 1 stars 7.09 score 39 scripts 1 dependents

pharmaverse

datacutr:SDTM Datacut

Supports the process of applying a cut to Standard Data Tabulation Model (SDTM), as part of the analysis of specific points in time of the data, normally as part of investigation into clinical trials. The functions support different approaches of cutting to the different domains of SDTM normally observed.

Maintained by Tim Barnett. Last updated 1 months ago.

4.3 match 14 stars 7.48 score 11 scripts

fanglu-chen

CARM:Covariate-Adjusted Adaptive Randomization via Mahalanobis-Distance

In randomized controlled trial (RCT), balancing covariate is often one of the most important concern. CARM package provides functions to balance the covariates and generate allocation sequence by covariate-adjusted Adaptive Randomization via Mahalanobis-distance (ARM) for RCT. About what ARM is and how it works please see Y. Qin, Y. Li, W. Ma, H. Yang, and F. Hu (2022). "Adaptive randomization via Mahalanobis distance" Statistica Sinica. <doi:10.5705/ss.202020.0440>. In addition, the package is also suitable for the randomization process of multi-arm trials. For details, please see Yang H, Qin Y, Wang F, et al. (2023). "Balancing covariates in multi-arm trials via adaptive randomization" Computational Statistics & Data Analysis.<doi:10.1016/j.csda.2022.107642>.

Maintained by Fanglu Chen. Last updated 8 months ago.

adaptive-design clinical-trials

8.4 match 5 stars 3.70 score

flavjack

inti:Tools and Statistical Procedures in Plant Science

The 'inti' package is part of the 'inkaverse' project for developing different procedures and tools used in plant science and experimental designs. The mean aim of the package is to support researchers during the planning of experiments and data collection (tarpuy()), data analysis and graphics (yupana()) , and technical writing. Learn more about the 'inkaverse' project at <https://inkaverse.com/>.

Maintained by Flavio Lozano-Isla. Last updated 19 hours ago.

agriculture apps inkaverse lmm plant-breeding plant-science shiny

3.8 match 5 stars 8.27 score 193 scripts

cran

Keyboard:Bayesian Designs for Early Phase Clinical Trials

We developed a package 'Keyboard' for designing single-agent, drug-combination, or phase I/II dose-finding clinical trials. The 'Keyboard' designs are novel early phase trial designs that can be implemented simply and transparently, similar to the 3+3 design, but yield excellent performance, comparable to those of more-complicated, model-based designs (Yan F, Mandrekar SJ, Yuan Y (2017) <doi:10.1158/1078-0432.CCR-17-0220>, Li DH, Whitmore JB, Guo W, Ji Y. (2017) <doi:10.1158/1078-0432.CCR-16-1125>, Liu S, Johnson VE (2016) <doi:10.1093/biostatistics/kxv040>, Zhou Y, Lee JJ, Yuan Y (2019) <doi:10.1002/sim.8475>, Pan H, Lin R, Yuan Y (2020) <doi:10.1016/j.cct.2020.105972>). The 'Keyboard' package provides tools for designing, conducting, and analyzing single-agent, drug-combination, and phase I/II dose-finding clinical trials. For more details about how to use this packge, please refer to Li C, Sun H, Cheng C, Tang L, and Pan H. (2022) "A software tool for both the maximum tolerated dose and the optimal biological dose finding trials in early phase designs". Manuscript submitted for publication.

Maintained by Xiaomeng Yuan. Last updated 3 years ago.

19.3 match 1 stars 1.60 score

cran

coxme:Mixed Effects Cox Models

Fit Cox proportional hazards models containing both fixed and random effects. The random effects can have a general form, of which familial interactions (a "kinship" matrix) is a particular special case. Note that the simplest case of a mixed effects Cox model, i.e. a single random per-group intercept, is also called a "frailty" model. The approach is based on Ripatti and Palmgren, Biometrics 2002.

Maintained by Terry M. Therneau. Last updated 7 months ago.

3.5 match 2 stars 8.78 score 562 scripts 15 dependents

alanarnholt

BSDA:Basic Statistics and Data Analysis

Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens.

Maintained by Alan T. Arnholt. Last updated 2 years ago.

3.3 match 7 stars 9.11 score 1.3k scripts 6 dependents

yqzhong7

AIPW:Augmented Inverse Probability Weighting

The 'AIPW' package implements the augmented inverse probability weighting, a doubly robust estimator, for average causal effect estimation with user-defined stacked machine learning algorithms. To cite the 'AIPW' package, please use: "Yongqi Zhong, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi (2021). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology. doi: 10.1093/aje/kwab207". Visit: <https://yqzhong7.github.io/AIPW/> for more information.

Maintained by Yongqi Zhong. Last updated 6 months ago.

causal-inference machine-learning robust-estimators

4.0 match 24 stars 7.35 score 31 scripts 1 dependents

kogalur

randomForestSRC:Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)

Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes. Extreme random forests and randomized splitting. Suite of imputation methods for missing data. Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance. Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.

Maintained by Udaya B. Kogalur. Last updated 2 months ago.

openmp

3.6 match 10 stars 7.90 score 1.2k scripts 12 dependents

jamescbell

gestate:Generalised Survival Trial Assessment Tool Environment

Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).

Maintained by James Bell. Last updated 2 years ago.

10.9 match 2 stars 2.60 score 8 scripts

divdyn

divDyn:Diversity Dynamics using Fossil Sampling Data

Functions to describe sampling and diversity dynamics of fossil occurrence datasets (e.g. from the Paleobiology Database). The package includes methods to calculate range- and occurrence-based metrics of taxonomic richness, extinction and origination rates, along with traditional sampling measures. A powerful subsampling tool is also included that implements frequently used sampling standardization methods in a multiple bin-framework. The plotting of time series and the occurrence data can be simplified by the functions incorporated in the package, as well as other calculations, such as environmental affinities and extinction selectivity testing. Details can be found in: Kocsis, A.T.; Reddin, C.J.; Alroy, J. and Kiessling, W. (2019) <doi:10.1101/423780>.

Maintained by Adam T. Kocsis. Last updated 4 months ago.

diversity extinction fossil-data occurrences origination paleobiology cpp

4.3 match 11 stars 6.48 score 137 scripts

mlr-org

mlr3proba:Probabilistic Supervised Learning for 'mlr3'

Provides extensions for probabilistic supervised learning for 'mlr3'. This includes extending the regression task to probabilistic and interval regression, adding a survival task, and other specialized models, predictions, and measures.

Maintained by John Zobolas. Last updated 2 months ago.

density-estimation machine-learning mlr3 probabilistic-regression probabilistic-supervised-learning supervised-learning survival-analysis cpp

3.6 match 135 stars 7.78 score 246 scripts

bgcarlisle

cthist:Clinical Trial Registry History

Retrieves historical versions of clinical trial registry entries from <https://ClinicalTrials.gov>. Package functionality and implementation for v 1.0.0 is documented in Carlisle (2022) <DOI:10.1371/journal.pone.0270909>.

Maintained by Benjamin Gregory Carlisle. Last updated 8 months ago.

6.8 match 8 stars 4.08 score 6 scripts

rsparapa

BART:Bayesian Additive Regression Trees

Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.

Maintained by Rodney Sparapani. Last updated 9 months ago.

cpp openmp

3.5 match 14 stars 7.96 score 474 scripts 10 dependents

giabaio

survHE:Survival Analysis in Health Economic Evaluation

Contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). To run the Bayesian models, the user needs to install additional modules (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed using 'remotes::install_github' from their GitHub repositories: (<https://github.com/giabaio/survHEhmc> and <https://github.com/giabaio/survHEinla/> respectively). 'survHEinla' is based on the package INLA, which is available for download at <https://inla.r-inla-download.org/R/stable/>. The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners). <doi:10.18637/jss.v095.i14>.

Maintained by Gianluca Baio. Last updated 9 days ago.

frequentist hamiltonian-monte-carlo health-economic-evaluation inla plotting-survival-curves rstan survival-analysis survival-models uncertainty openjdk

4.0 match 42 stars 6.88 score 2 dependents

adayim

consort:Create Consort Diagram

To make it easy to create CONSORT diagrams for the transparent reporting of participant allocation in randomized, controlled clinical trials. This is done by creating a standardized disposition data, and using this data as the source for the creation a standard CONSORT diagram. Human effort by supplying text labels on the node can also be achieved.

Maintained by Alim Dayim. Last updated 1 months ago.

4.1 match 32 stars 6.62 score 59 scripts

victor-navarro

calmr:Canonical Associative Learning Models and their Representations

Implementations of canonical associative learning models, with tools to run experiment simulations, estimate model parameters, and compare model representations. Experiments and results are represented using S4 classes and methods.

Maintained by Victor Navarro. Last updated 9 months ago.

4.3 match 3 stars 6.40 score 17 scripts

nlmixr2

rxode2:Facilities for Simulating from ODE-Based Models

Facilities for running simulations from ordinary differential equation ('ODE') models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The 'VODE' and 'LSODA' are in the public domain. The information is available in the inst/COPYRIGHTS.

Maintained by Matthew L. Fidler. Last updated 30 days ago.

fortran openblas cpp openmp

2.4 match 39 stars 11.16 score 220 scripts 13 dependents

cran

GCalcium:A Data Manipulation and Analysis Package for Calcium Indicator Data

Provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users.

Maintained by Andrew Tamalunas. Last updated 6 years ago.

9.3 match 2.90 score 16 scripts

jwbowers

RItools:Randomization Inference Tools

Tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008) <doi:10.1214/08-STS254> . This test is useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.

Maintained by Jake Bowers. Last updated 10 months ago.

7.0 match 3.84 score 196 scripts

thothorn

exactRankTests:Exact Distributions for Rank and Permutation Tests

Computes exact conditional p-values and quantiles using an implementation of the Shift-Algorithm by Streitberg & Roehmel.

Maintained by Torsten Hothorn. Last updated 3 years ago.

3.8 match 1 stars 7.13 score 276 scripts 65 dependents

dominicmagirr

nphRCT:Non-Proportional Hazards in Randomized Controlled Trials

Perform a stratified weighted log-rank test in a randomized controlled trial. Tests can be visualized as a difference in average score on the two treatment arms. These methods are described in Magirr and Burman (2018) <doi:10.48550/arXiv.1807.11097>, Magirr (2020) <doi:10.48550/arXiv.2007.04767>, and Magirr and Jimenez (2022) <doi:10.48550/arXiv.2201.10445>.

Maintained by Dominic Magirr. Last updated 9 months ago.

6.4 match 4.16 score 24 scripts 1 dependents

paulojus

geoR:Analysis of Geostatistical Data

Geostatistical analysis including variogram-based, likelihood-based and Bayesian methods. Software companion for Diggle and Ribeiro (2007) <doi:10.1007/978-0-387-48536-2>.

Maintained by Paulo Justiniano Ribeiro Jr. Last updated 1 years ago.

3.5 match 10 stars 7.57 score 1.8k scripts 12 dependents

jackmwolf

tehtuner:Fit and Tune Models to Detect Treatment Effect Heterogeneity

Implements methods to fit Virtual Twins models (Foster et al. (2011) <doi:10.1002/sim.4322>) for identifying subgroups with differential effects in the context of clinical trials while controlling the probability of falsely detecting a differential effect when the conditional average treatment effect is uniform across the study population using parameter selection methods proposed in Wolf et al. (2022) <doi:10.1177/17407745221095855>.

Maintained by Jack Wolf. Last updated 2 years ago.

clinical-trials heterogeneity-of-treatment-effect subgroup-identification

8.0 match 4 stars 3.30 score 6 scripts

olssol

idem:Inference in Randomized Controlled Trials with Death and Missingness

In randomized studies involving severely ill patients, functional outcomes are often unobserved due to missed clinic visits, premature withdrawal or death. It is well known that if these unobserved functional outcomes are not handled properly, biased treatment comparisons can be produced. In this package, we implement a procedure for comparing treatments that is based on the composite endpoint of both the functional outcome and survival. The procedure was proposed in Wang et al. (2016) <DOI:10.1111/biom.12594> and Wang et al. (2020) <DOI:10.18637/jss.v093.i12>. It considers missing data imputation with different sensitivity analysis strategies to handle the unobserved functional outcomes not due to death.

Maintained by Chenguang Wang. Last updated 2 years ago.

cpp

7.5 match 3.51 score 16 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.

3.6 match 6 stars 7.29 score 151 scripts 10 dependents

pharmaverse

logrx:A Logging Utility Focus on Clinical Trial Programming Workflows

A utility to facilitate the logging and review of R programs in clinical trial programming workflows.

Maintained by Nathan Kosiba. Last updated 10 days ago.

3.4 match 41 stars 7.60 score 15 scripts

hermitz9

BayesAT:Bayesian Adaptive Trial

Bayesian adaptive trial algorithm implements multiple-stage interim analysis. Package includes data generating function, and Bayesian hypothesis testing function.

Maintained by Yuan Zhong. Last updated 1 months ago.

8.6 match 3.00 score

glsnow

blockrand:Randomization for Block Random Clinical Trials

Create randomizations for block random clinical trials. Can also produce a pdf file of randomization cards.

Maintained by Greg Snow. Last updated 5 years ago.

7.1 match 2 stars 3.60 score 67 scripts 1 dependents

r-forge

Sleuth3:Data Sets from Ramsey and Schafer's "Statistical Sleuth (3rd Ed)"

Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed)", Cengage Learning.

Maintained by Berwin A Turlach. Last updated 1 years ago.

4.0 match 6.38 score 522 scripts

resplab

predtools:Prediction Model Tools

Provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.

Maintained by Amin Adibi. Last updated 2 years ago.

cpp

3.8 match 9 stars 6.74 score 77 scripts

cran

agricolae:Statistical Procedures for Agricultural Research

Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster.

Maintained by Felipe de Mendiburu. Last updated 1 years ago.

3.6 match 7 stars 7.01 score 15 dependents

nbarrowman

vtree:Display Information About Nested Subsets of a Data Frame

A tool for calculating and drawing "variable trees". Variable trees display information about nested subsets of a data frame.

Maintained by Nick Barrowman. Last updated 13 hours ago.

data-science data-visualization exploratory-data-analysis statistics

3.5 match 76 stars 7.09 score 65 scripts

apariciojohan

flexFitR:Flexible Non-Linear Least Square Model Fitting

Provides tools for flexible non-linear least squares model fitting using general-purpose optimization techniques. The package supports a variety of optimization algorithms, including those provided by the 'optimx' package, making it suitable for handling complex non-linear models. Features include parallel processing support via the 'future' and 'foreach' packages, comprehensive model diagnostics, and visualization capabilities. Implements methods described in Nash and Varadhan (2011, <doi:10.18637/jss.v043.i09>).

Maintained by Johan Aparicio. Last updated 8 days ago.

nls optimization

3.5 match 2 stars 7.09 score 77 scripts

marvels2031

PWEALL:Design and Monitoring of Survival Trials Accounting for Complex Situations

Calculates various functions needed for design and monitoring survival trials accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual, and different censoring distributions between groups. The event time distribution is assumed to be piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution. As compared with Version 1.2.1, two more types of hybrid crossover are added. A bug is corrected in the function "pwecx" that calculates the crossover-adjusted survival, distribution, density, hazard and cumulative hazard functions. Also, to generate the crossover-adjusted event time random variable, a more efficient algorithm is used and the output includes crossover indicators.

Maintained by Xiaodong Luo. Last updated 2 years ago.

fortran

10.2 match 2.42 score 44 scripts 2 dependents