Showing 12 of total 12 results (show query)
rspatial
dismo:Species Distribution Modeling
Methods for species distribution modeling, that is, predicting the environmental similarity of any site to that of the locations of known occurrences of a species.
Maintained by Robert J. Hijmans. Last updated 4 months ago.
25 stars 11.88 score 2.8k scripts 21 dependents
bioc
diffcyt:Differential discovery in high-dimensional cytometry via high-resolution clustering
Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.
Maintained by Lukas M. Weber. Last updated 2 months ago.
immunooncologyflowcytometryproteomicssinglecellcellbasedassayscellbiologyclusteringfeatureextractionsoftware
20 stars 9.98 score 225 scripts 5 dependentsbioc
ClassifyR:A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing
The software formalises a framework for classification and survival model evaluation in R. There are four stages; Data transformation, feature selection, model training, and prediction. The requirements of variable types and variable order are fixed, but specialised variables for functions can also be provided. The framework is wrapped in a driver loop that reproducibly carries out a number of cross-validation schemes. Functions for differential mean, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework.
Maintained by Dario Strbenac. Last updated 5 days ago.
6 stars 8.46 score 45 scripts 3 dependents
philips-software
latrend:A Framework for Clustering Longitudinal Data
A framework for clustering longitudinal datasets in a standardized way. The package provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease. The 'akmedoids' package is available from <https://github.com/MAnalytics/akmedoids>.
Maintained by Niek Den Teuling. Last updated 3 months ago.
cluster-analysisclustering-evaluationclustering-methodsdata-sciencelongitudinal-clusteringlongitudinal-datamixture-modelstime-series-analysis
30 stars 6.77 score 26 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-modelssimulation-engine
13 stars 6.26 score 9 scripts
wlenhard
cNORM:Continuous Norming
A comprehensive toolkit for generating continuous test norms in psychometrics and biometrics, and analyzing model fit. The package offers both distribution-free modeling using Taylor polynomials and parametric modeling using the beta-binomial distribution. Originally developed for achievement tests, it is applicable to a wide range of mental, physical, or other test scores dependent on continuous or discrete explanatory variables. The package provides several advantages: It minimizes deviations from representativeness in subsamples, interpolates between discrete levels of explanatory variables, and significantly reduces the required sample size compared to conventional norming per age group. cNORM enables graphical and analytical evaluation of model fit, accommodates a wide range of scales including those with negative and descending values, and even supports conventional norming. It generates norm tables including confidence intervals. It also includes methods for addressing representativeness issues through Iterative Proportional Fitting.
Maintained by Wolfgang Lenhard. Last updated 4 months ago.
beta-binomialbiometricscontinuous-norminggrowth-curvenorm-scoresnorm-tablesnormalization-techniquespercentilepsychometricsregression-based-normingtaylor-series
2 stars 5.38 score 75 scripts
vsousa
poolABC:Approximate Bayesian Computation with Pooled Sequencing Data
Provides functions to simulate Pool-seq data under models of demographic formation and to import Pool-seq data from real populations. Implements two ABC algorithms for performing parameter estimation and model selection using Pool-seq data. Cross-validation can also be performed to assess the accuracy of ABC estimates and model choice. Carvalho et al., (2022) <doi:10.1111/1755-0998.13834>.
Maintained by João Carvalho. Last updated 2 years ago.
1 stars 3.70 score 3 scriptsbioc
OrderedList:Similarities of Ordered Gene Lists
Detection of similarities between ordered lists of genes. Thereby, either simple lists can be compared or gene expression data can be used to deduce the lists. Significance of similarities is evaluated by shuffling lists or by resampling in microarray data, respectively.
Maintained by Claudio Lottaz. Last updated 5 months ago.
microarraydifferentialexpressionmultiplecomparison
3.30 score 9 scripts
korydjohnson
rai:Revisiting-Alpha-Investing for Polynomial Regression
A modified implementation of stepwise regression that greedily searches the space of interactions among features in order to build polynomial regression models. Furthermore, the hypothesis tests conducted are valid-post model selection due to the use of a revisiting procedure that implements an alpha-investing rule. As a result, the set of rejected sequential hypotheses is proven to control the marginal false discover rate. When not searching for polynomials, the package provides a statistically valid algorithm to run and terminate stepwise regression. For more information, see Johnson, Stine, and Foster (2019) <arXiv:1510.06322>.
Maintained by Kory D. Johnson. Last updated 3 years ago.
3 stars 3.18 score 7 scripts
f-rousset
blackbox:Black Box Optimization and Exploration of Parameter Space
Performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. Includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.
Maintained by François Rousset. Last updated 1 years ago.
1.79 score 8 scripts 1 dependents
laurabarbieri
panelSUR:Two-Way Error Component SUR Systems Estimation on Unbalanced Panel Data
Generalized Least Squares (GLS) estimation of Seemingly Unrelated Regression (SUR) systems on unbalanced panel in the one/two-way cases also taking into account the possibility of cross equation restrictions. Methodological details can be found in Biørn (2004) <doi:10.1016/j.jeconom.2003.10.023> and Platoni, Sckokai, Moro (2012) <doi:10.1080/07474938.2011.607098>.
Maintained by Laura Barbieri. Last updated 1 years ago.
1 stars 1.00 score
numbersman77
OrdFacReg:Least Squares, Logistic, and Cox-Regression with Ordered Predictors
In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.
Maintained by Kaspar Rufibach. Last updated 10 years ago.
1.00 score 2 scripts