Showing 24 of total 24 results (show query)
csafe-isu
handwriter:Handwriting Analysis in R
Perform statistical writership analysis of scanned handwritten documents. Webpage provided at: <https://github.com/CSAFE-ISU/handwriter>.
Maintained by Stephanie Reinders. Last updated 2 months ago.
24 stars 8.63 score 27 scripts 2 dependents
helske
seqHMM:Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series
Designed for fitting hidden (latent) Markov models and mixture hidden Markov models for social sequence data and other categorical time series. Also some more restricted versions of these type of models are available: Markov models, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models. The package provides functions for evaluating and comparing models, as well as functions for visualizing of multichannel sequence data and hidden Markov models. Models are estimated using maximum likelihood via the EM algorithm and/or direct numerical maximization with analytical gradients. All main algorithms are written in C++ with support for parallel computation. Documentation is available via several vignettes in this page, and the paper by Helske and Helske (2019, <doi:10.18637/jss.v088.i03>).
Maintained by Jouni Helske. Last updated 2 years ago.
categorical-dataem-algorithmhidden-markov-modelshmmmixture-markov-modelstime-seriesopenblascppopenmp
98 stars 8.52 score 92 scripts 1 dependents
bgoodri
mi:Missing Data Imputation and Model Checking
The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
Maintained by Ben Goodrich. Last updated 3 years ago.
2 stars 8.25 score 244 scripts 47 dependents
loelschlaeger
fHMM:Fitting Hidden Markov Models to Financial Data
Fitting (hierarchical) hidden Markov models to financial data via maximum likelihood estimation. See Oelschläger, L. and Adam, T. "Detecting Bearish and Bullish Markets in Financial Time Series Using Hierarchical Hidden Markov Models" (2021, Statistical Modelling) <doi:10.1177/1471082X211034048> for a reference on the method. A user guide is provided by the accompanying software paper "fHMM: Hidden Markov Models for Financial Time Series in R", Oelschläger, L., Adam, T., and Michels, R. (2024, Journal of Statistical Software) <doi:10.18637/jss.v109.i09>.
Maintained by Lennart Oelschläger. Last updated 6 days ago.
financehidden-markov-modelscppopenmp
17 stars 7.04 score 5 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 10 months ago.
3 stars 6.40 score 17 scripts
joeguinness
GpGp:Fast Gaussian Process Computation Using Vecchia's Approximation
Functions for fitting and doing predictions with Gaussian process models using Vecchia's (1988) approximation. Package also includes functions for reordering input locations, finding ordered nearest neighbors (with help from 'FNN' package), grouping operations, and conditional simulations. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. The original approximation is due to Vecchia (1988) <http://www.jstor.org/stable/2345768>, and the reordering and grouping methods are from Guinness (2018) <doi:10.1080/00401706.2018.1437476>. Model fitting employs a Fisher scoring algorithm described in Guinness (2019) <doi:10.48550/arXiv.1905.08374>.
Maintained by Joseph Guinness. Last updated 6 months ago.
10 stars 6.16 score 160 scripts 6 dependents
venpopov
bmm:Easy and Accessible Bayesian Measurement Models Using 'brms'
Fit computational and measurement models using full Bayesian inference. The package provides a simple and accessible interface by translating complex domain-specific models into 'brms' syntax, a powerful and flexible framework for fitting Bayesian regression models using 'Stan'. The package is designed so that users can easily apply state-of-the-art models in various research fields, and so that researchers can use it as a new model development framework. References: Frischkorn and Popov (2023) <doi:10.31234/osf.io/umt57>.
Maintained by Vencislav Popov. Last updated 26 days ago.
15 stars 5.92 score 35 scripts
bmait101
hatchR:Predict Fish Hatch and Emergence Timing
Predict hatch and emergence timing for a wide range of wild fishes using the effective value framework (Sparks et al., (2019) <DOI:10.1139/cjfas-2017-0468>). 'hatchR' offers users access to established phenological models and the flexibility to incorporate custom parameterizations using external datasets.
Maintained by Bryan M. Maitland. Last updated 16 days ago.
1 stars 5.89 score
nelson-gon
manymodelr:Build and Tune Several Models
Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of machine learning convenience functions. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using functions from 'caret' with easier to read syntax. Kuhn(2014) <doi:10.48550/arXiv.1405.6974>.
Maintained by Nelson Gonzabato. Last updated 9 days ago.
analysis-of-varianceanovacorrelationcorrelation-coefficientgeneralized-linear-modelsgradient-boosting-decision-treesknn-classificationlinear-modelslinear-regressionmachine-learningmissing-valuesmodelsr-programmingrandom-forest-algorithmregression-models
2 stars 5.78 score 50 scripts
silvaneojunior
kDGLM:Bayesian Analysis of Dynamic Generalized Linear Models
Provide routines for filtering and smoothing, forecasting, sampling and Bayesian analysis of Dynamic Generalized Linear Models using the methodology described in Alves et al. (2024)<doi:10.48550/arXiv.2201.05387> and dos Santos Jr. et al. (2024)<doi:10.48550/arXiv.2403.13069>.
Maintained by Silvaneo dos Santos Jr.. Last updated 10 days ago.
2 stars 5.70 score 9 scriptsloelschlaeger
RprobitB:Bayesian Probit Choice Modeling
Bayes estimation of probit choice models, both in the cross-sectional and panel setting. The package can analyze binary, multivariate, ordered, and ranked choices, as well as heterogeneity of choice behavior among deciders. The main functionality includes model fitting via Markov chain Monte Carlo m ethods, tools for convergence diagnostic, choice data simulation, in-sample and out-of-sample choice prediction, and model selection using information criteria and Bayes factors. The latent class model extension facilitates preference-based decider classification, where the number of latent classes can be inferred via the Dirichlet process or a weight-based updating heuristic. This allows for flexible modeling of choice behavior without the need to impose structural constraints. For a reference on the method see Oelschlaeger and Bauer (2021) <https://trid.trb.org/view/1759753>.
Maintained by Lennart Oelschläger. Last updated 6 months ago.
bayesdiscrete-choiceprobitopenblascppopenmp
4 stars 5.45 score 1 scripts
reconverse
i2extras:Functions to Work with 'incidence2' Objects
Provides functions to work with 'incidence2' objects, including a simplified interface for trend fitting and peak estimation. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis (<https://www.reconverse.org/).
Maintained by Tim Taylor. Last updated 8 months ago.
2 stars 5.25 score 22 scripts
gabrielgesteira
qtlpoly:Random-Effect Multiple QTL Mapping in Autopolyploids
Performs random-effect multiple interval mapping (REMIM) in full-sib families of autopolyploid species based on restricted maximum likelihood (REML) estimation and score statistics, as described in Pereira et al. (2020) <doi:10.1534/genetics.120.303080>.
Maintained by Gabriel de Siqueira Gesteira. Last updated 5 months ago.
polyploidqtl-mappingopenblascppopenmp
6 stars 5.17 score 61 scripts
bioc
ttgsea:Tokenizing Text of Gene Set Enrichment Analysis
Functional enrichment analysis methods such as gene set enrichment analysis (GSEA) have been widely used for analyzing gene expression data. GSEA is a powerful method to infer results of gene expression data at a level of gene sets by calculating enrichment scores for predefined sets of genes. GSEA depends on the availability and accuracy of gene sets. There are overlaps between terms of gene sets or categories because multiple terms may exist for a single biological process, and it can thus lead to redundancy within enriched terms. In other words, the sets of related terms are overlapping. Using deep learning, this pakage is aimed to predict enrichment scores for unique tokens or words from text in names of gene sets to resolve this overlapping set issue. Furthermore, we can coin a new term by combining tokens and find its enrichment score by predicting such a combined tokens.
Maintained by Dongmin Jung. Last updated 5 months ago.
softwaregeneexpressiongenesetenrichment
4.95 score 3 scripts 3 dependents
vasileioskarapoulios
LDNN:Longitudinal Data Neural Network
This is a Neural Network regression model implementation using 'Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs.
Maintained by Vasileios Karapoulios. Last updated 4 years ago.
3.70 score 6 scripts
pakillo
BayesianWebs:Bayesian Modelling of Bipartite Networks
Bayesian modelling of bipartite network structure, following the approach of Young et al. <doi:10.1038/s41467-021-24149-x>.
Maintained by Francisco Rodriguez-Sanchez. Last updated 3 months ago.
12 stars 3.68 score 3 scripts
sciviews
modelit:Statistical Models for 'SciViews::R'
Create and use statistical models (linear, general, nonlinear...) with extensions to support rich-formatted tables, equations and plots for the 'SciViews::R' dialect.
Maintained by Philippe Grosjean. Last updated 4 months ago.
1 stars 3.30 score 8 scripts
inbo
n2kanalysis:Generic Functions to Analyse Data from the 'Natura 2000' Monitoring
All generic functions and classes for the analysis for the 'Natura 2000' monitoring. The classes contain all required data and definitions to fit the model without the need to access other sources. Potentially they might need access to one or more parent objects. An aggregation object might for example need the result of an imputation object. The actual definition of the analysis, using these generic function and classes, is defined in dedictated analysis R packages for every monitoring scheme. For example 'abvanalysis' and 'watervogelanalysis'.
Maintained by Thierry Onkelinx. Last updated 2 months ago.
1 stars 3.18 score 7 scripts
mrc-ide
moz.utils:Utility functions
Utility functions, for useful utilitarian uses.
Maintained by Oli Stevens. Last updated 2 months ago.
2.26 score 18 scripts
bips-hb
expard:Drug EXPosures and ADRs
An R package for fitting complex drug exposure and adverse drug reaction (ADR) relationships
Maintained by Louis Dijkstra. Last updated 2 months ago.
1 stars 1.81 score 13 scriptsinbo
ladybird:Analysis of Ladybird Occurrence Data
Analysis of ladybird occurrence data from Belgium, the Netherlands and the UK since 1990.
Maintained by Thierry Onkelinx. Last updated 4 years ago.
1.70 score 3 scripts
cran
RcppDPR:'Rcpp' Implementation of Dirichlet Process Regression
'Rcpp' reimplementation of the the Bayesian non-parametric Dirichlet Process Regression model for penalized regression first published in Zeng and Zhou (2017) <doi:10.1038/s41467-017-00470-2>. A full Bayesian version is implemented with Gibbs sampling, as well as a faster but less accurate variational Bayes approximation.
Maintained by Mohammad Abu Gazala. Last updated 10 days ago.
1.30 score
mayamathur
NRejections:Metrics for Multiple Testing with Correlated Outcomes
Implements methods in Mathur and VanderWeele (in preparation) to characterize global evidence strength across W correlated ordinary least squares (OLS) hypothesis tests. Specifically, uses resampling to estimate a null interval for the total number of rejections in, for example, 95% of samples generated with no associations (the global null), the excess hits (the difference between the observed number of rejections and the upper limit of the null interval), and a test of the global null based on the number of rejections.
Maintained by Maya B. Mathur. Last updated 5 years ago.
1.00 score 10 scripts