Showing 16 of total 16 results (show query)
luca-scr
mclust:Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
Maintained by Luca Scrucca. Last updated 11 months ago.
21 stars 12.23 score 6.6k scripts 587 dependents
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 3 days ago.
35 stars 9.34 score 163 scripts
trevorhastie
mda:Mixture and Flexible Discriminant Analysis
Mixture and flexible discriminant analysis, multivariate adaptive regression splines (MARS), BRUTO, and vector-response smoothing splines. Hastie, Tibshirani and Friedman (2009) "Elements of Statistical Learning (second edition, chap 12)" Springer, New York.
Maintained by Trevor Hastie. Last updated 5 months ago.
3 stars 7.60 score 428 scripts 17 dependents
kkholst
targeted:Targeted Inference
Various methods for targeted and semiparametric inference including augmented inverse probability weighted (AIPW) estimators for missing data and causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>), variable importance and conditional average treatment effects (CATE) (van der Laan (2006) <doi:10.2202/1557-4679.1008>), estimators for risk differences and relative risks (Richardson et al. (2017) <doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).
Maintained by Klaus K. Holst. Last updated 2 months ago.
causal-inferencedouble-robustestimationsemiparametric-estimationstatisticsopenblascppopenmp
11 stars 7.20 score 30 scripts 1 dependents
egenn
rtemis:Machine Learning and Visualization
Advanced Machine Learning and Visualization. Unsupervised Learning (Clustering, Decomposition), Supervised Learning (Classification, Regression), Cross-Decomposition, Bagging, Boosting, Meta-models. Static and interactive graphics.
Maintained by E.D. Gennatas. Last updated 2 months ago.
data-sciencedata-visualizationmachine-learningmachine-learning-libraryvisualization
145 stars 7.09 score 50 scripts 2 dependents
weecology
LDATS:Latent Dirichlet Allocation Coupled with Time Series Analyses
Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial time series methods in a two-stage analysis to quantify dynamics in high-dimensional temporal data. LDA decomposes multivariate data into lower-dimension latent groupings, whose relative proportions are modeled using generalized Bayesian time series models that include abrupt changepoints and smooth dynamics. The methods are described in Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>, Western and Kleykamp (2004) <doi:10.1093/pan/mph023>, Venables and Ripley (2002, ISBN-13:978-0387954578), and Christensen et al. (2018) <doi:10.1002/ecy.2373>.
Maintained by Juniper L. Simonis. Last updated 5 years ago.
changepointldaparallel-temperingportalsoftmax
25 stars 6.93 score 45 scripts
zhenkewu
baker:"Nested Partially Latent Class Models"
Provides functions to specify, fit and visualize nested partially-latent class models ( Wu, Deloria-Knoll, Hammitt, and Zeger (2016) <doi:10.1111/rssc.12101>; Wu, Deloria-Knoll, and Zeger (2017) <doi:10.1093/biostatistics/kxw037>; Wu and Chen (2021) <doi:10.1002/sim.8804>) for inference of population disease etiology and individual diagnosis. In the motivating Pneumonia Etiology Research for Child Health (PERCH) study, because both quantities of interest sum to one hundred percent, the PERCH scientists frequently refer to them as population etiology pie and individual etiology pie, hence the name of the package.
Maintained by Zhenke Wu. Last updated 11 months ago.
bayesiancase-controllatent-class-analysisjagscpp
8 stars 6.00 score 21 scripts
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
staffanbetner
rethinking:Statistical Rethinking book package
Utilities for fitting and comparing models
Maintained by Richard McElreath. Last updated 4 months ago.
5.42 score 4.4k scripts
pingyangchen
milr:Multiple-Instance Logistic Regression with LASSO Penalty
The multiple instance data set consists of many independent subjects (called bags) and each subject is composed of several components (called instances). The outcomes of such data set are binary or categorical responses, and, we can only observe the subject-level outcomes. For example, in manufacturing processes, a subject is labeled as "defective" if at least one of its own components is defective, and otherwise, is labeled as "non-defective". The 'milr' package focuses on the predictive model for the multiple instance data set with binary outcomes and performs the maximum likelihood estimation with the Expectation-Maximization algorithm under the framework of logistic regression. Moreover, the LASSO penalty is attached to the likelihood function for simultaneous parameter estimation and variable selection.
Maintained by Ping-Yang Chen. Last updated 4 years ago.
lasso-penaltymachine-learningopenblascpp
10 stars 4.88 score 15 scripts
ashesitr
reservr:Fit Distributions and Neural Networks to Censored and Truncated Data
Define distribution families and fit them to interval-censored and interval-truncated data, where the truncation bounds may depend on the individual observation. The defined distributions feature density, probability, sampling and fitting methods as well as efficient implementations of the log-density log f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow' neural networks via the 'tensorflow' package. Allows training parametric neural networks on interval-censored and interval-truncated data with flexible parameterization. Applications include Claims Development in Non-Life Insurance, e.g. modelling reporting delay distributions from incomplete data, see Bücher, Rosenstock (2022) <doi:10.1007/s13385-022-00314-4>.
Maintained by Alexander Rosenstock. Last updated 9 months ago.
4 stars 3.78 score 9 scripts
cran
qrnn:Quantile Regression Neural Network
Fit quantile regression neural network models with optional left censoring, partial monotonicity constraints, generalized additive model constraints, and the ability to fit multiple non-crossing quantile functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005> and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.
Maintained by Alex J. Cannon. Last updated 1 years ago.
8 stars 3.08 score 1 dependents
namanlab
covalchemy:Constructing Joint Distributions with Control Over Statistical Properties
Synthesizing joint distributions from marginal densities, focusing on controlling key statistical properties such as correlation for continuous data, mutual information for categorical data, and inducing Simpson's Paradox. Generate datasets with specified correlation structures for continuous variables, adjust mutual information between categorical variables, and manipulate subgroup correlations to intentionally create Simpson's Paradox.
Maintained by Naman Agrawal. Last updated 4 months ago.
3.00 score 1 scripts
doufu1402
ProxReg:Linear Models for Prediction and Classification using Proximal Operators
Implements optimization techniques for Lasso regression, R.Tibshirani(1996)<doi:10.1111/j.2517-6161.1996.tb02080.x> using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and Iterative Shrinkage-Thresholding Algorithm (ISTA) based on proximal operators, A.Beck(2009)<doi:10.1137/080716542>. The package is useful for high-dimensional regression problems and includes cross-validation procedures to select optimal penalty parameters.
Maintained by YingHong Chen. Last updated 14 days ago.
2.00 score
bentaylor1
deepNN:Deep Learning
Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning.
Maintained by Benjamin Taylor. Last updated 2 years ago.
1.34 score 22 scripts
