R-universe search: exports:inference

Showing 15 of total 15 results (show query)

statswithr

statsr:Companion Software for the Coursera Statistics with R Specialization

Data and functions to support Bayesian and frequentist inference and decision making for the Coursera Specialization "Statistics with R". See <https://github.com/StatsWithR/statsr> for more information.

Maintained by Merlise Clyde. Last updated 4 years ago.

bayesian-inference coursera statistics

71 stars 7.82 score 880 scripts

klarsen1

MarketMatching:Market Matching and Causal Impact Inference

For a given test market find the best control markets using time series matching and analyze the impact of an intervention. The intervention could be a marketing event or some other local business tactic that is being tested. The workflow implemented in the Market Matching package utilizes dynamic time warping (the 'dtw' package) to do the matching and the 'CausalImpact' package to analyze the causal impact. In fact, this package can be considered a "workflow wrapper" for those two packages. In addition, if you don't have a chosen set of test markets to match, the Market Matching package can provide suggested test/control market pairs and pseudo prospective power analysis (measuring causal impact at fake interventions).

Maintained by Larsen Kim. Last updated 1 years ago.

132 stars 6.91 score 38 scripts

fbertran

Cascade:Selection, Reverse-Engineering and Prediction in Cascade Networks

A modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.

Maintained by Frederic Bertrand. Last updated 2 years ago.

1 stars 6.56 score 40 scripts 2 dependents

donaldrwilliams

GGMncv:Gaussian Graphical Models with Nonconvex Regularization

Estimate Gaussian graphical models with nonconvex penalties <doi:10.31234/osf.io/ad57p>, including the atan Wang and Zhu (2016) <doi:10.1155/2016/6495417>, seamless L0 Dicker, Huang, and Lin (2013) <doi:10.5705/ss.2011.074>, exponential Wang, Fan, and Zhu <doi:10.1007/s10463-016-0588-3>, smooth integration of counting and absolute deviation Lv and Fan (2009) <doi:10.1214/09-AOS683>, logarithm Mazumder, Friedman, and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>, Lq, smoothly clipped absolute deviation Fan and Li (2001) <doi:10.1198/016214501753382273>, and minimax concave penalty Zhang (2010) <doi:10.1214/09-AOS729>. There are also extensions for computing variable inclusion probabilities, multiple regression coefficients, and statistical inference <doi:10.1214/15-EJS1031>.

Maintained by Donald Williams. Last updated 3 years ago.

openblas cpp

5 stars 6.22 score 22 scripts 2 dependents

shabbychef

SharpeR:Statistical Significance of the Sharpe Ratio

A collection of tools for analyzing significance of assets, funds, and trading strategies, based on the Sharpe ratio and overfit of the same. Provides density, distribution, quantile and random generation of the Sharpe ratio distribution based on normal returns, as well as the optimal Sharpe ratio over multiple assets. Computes confidence intervals on the Sharpe and provides a test of equality of Sharpe ratios based on the Delta method. The statistical foundations of the Sharpe can be found in the author's Short Sharpe Course <doi:10.2139/ssrn.3036276>.

Maintained by Steven E. Pav. Last updated 3 months ago.

19 stars 6.18 score 53 scripts

fbertran

Patterns:Deciphering Biological Networks with Patterned Heterogeneous Measurements

A modeling tool dedicated to biological network modeling (Bertrand and others 2020, <doi:10.1093/bioinformatics/btaa855>). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, <doi:10.1093/bioinformatics/btt705>). Example of use with microarray or RNA-Seq data are provided.

Maintained by Frederic Bertrand. Last updated 11 months ago.

4 stars 6.16 score 18 scripts

bioc

IsoBayes:IsoBayes: Single Isoform protein inference Method via Bayesian Analyses

IsoBayes is a Bayesian method to perform inference on single protein isoforms. Our approach infers the presence/absence of protein isoforms, and also estimates their abundance; additionally, it provides a measure of the uncertainty of these estimates, via: i) the posterior probability that a protein isoform is present in the sample; ii) a posterior credible interval of its abundance. IsoBayes inputs liquid cromatography mass spectrometry (MS) data, and can work with both PSM counts, and intensities. When available, trascript isoform abundances (i.e., TPMs) are also incorporated: TPMs are used to formulate an informative prior for the respective protein isoform relative abundance. We further identify isoforms where the relative abundance of proteins and transcripts significantly differ. We use a two-layer latent variable approach to model two sources of uncertainty typical of MS data: i) peptides may be erroneously detected (even when absent); ii) many peptides are compatible with multiple protein isoforms. In the first layer, we sample the presence/absence of each peptide based on its estimated probability of being mistakenly detected, also known as PEP (i.e., posterior error probability). In the second layer, for peptides that were estimated as being present, we allocate their abundance across the protein isoforms they map to. These two steps allow us to recover the presence and abundance of each protein isoform.

Maintained by Simone Tiberi. Last updated 5 months ago.

statisticalmethod bayesian proteomics massspectrometry alternativesplicing sequencing rnaseq geneexpression genetics visualization software cpp

7 stars 5.39 score 10 scripts

cran

frbs:Fuzzy Rule-Based Systems for Classification and Regression Tasks

An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at representing the reasoning of human experts in a set of IF-THEN rules, to handle real-life problems in, e.g., control, prediction and inference, data mining, bioinformatics data processing, and robotics. FRBSs are also known as fuzzy inference systems and fuzzy models. During the modeling of an FRBS, there are two important steps that need to be conducted: structure identification and parameter estimation. Nowadays, there exists a wide variety of algorithms to generate fuzzy IF-THEN rules automatically from numerical data, covering both steps. Approaches that have been used in the past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering methods, genetic algorithms, squares methods, etc. Furthermore, in this version we provide a universal framework named 'frbsPMML', which is adopted from the Predictive Model Markup Language (PMML), for representing FRBS models. PMML is an XML-based language to provide a standard for describing models produced by data mining and machine learning algorithms. Therefore, we are allowed to export and import an FRBS model to/from 'frbsPMML'. Finally, this package aims to implement the most widely used standard procedures, thus offering a standard package for FRBS modeling to the R community.

Maintained by Christoph Bergmeir. Last updated 5 years ago.

12 stars 4.18 score 1 dependents

haowang47

PCGII:Partial Correlation Graph with Information Incorporation

Large-scale gene expression studies allow gene network construction to uncover associations among genes. This package is developed for estimating and testing partial correlation graphs with prior information incorporated.

Maintained by Hao Wang. Last updated 1 years ago.

1 stars 3.70 score 10 scripts

jeremyroos

gmgm:Gaussian Mixture Graphical Model Learning and Inference

Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian networks (their temporal extension) whose local probability distributions are described by Gaussian mixture models. They are powerful tools for graphically and quantitatively representing nonlinear dependencies between continuous variables. This package provides a complete framework to create, manipulate, learn the structure and the parameters, and perform inference in these models. Most of the algorithms are described in the PhD thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.

Maintained by Jérémy Roos. Last updated 3 years ago.

bayesian-networks gaussian-mixture-models inference machine-learning probabilistic-graphical-models

5 stars 3.40 score 7 scripts

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 8 months ago.

cpp

1 stars 3.32 score 21 scripts

wernerstahel

relevance:Calculate Relevance and Significance Measures

Calculates relevance and significance values for simple models and for many types of regression models. These are introduced in 'Stahel, Werner A.' (2021) "Measuring Significance and Relevance instead of p-values." <https://stat.ethz.ch/~stahel/relevance/stahel-relevance2103.pdf>. These notions are also applied to replication studies, as described in the manuscript 'Stahel, Werner A.' (2022) "'Replicability': Terminology, Measuring Success, and Strategy" available in the documentation.

Maintained by Werner A. Stahel. Last updated 1 years ago.

2.00 score 3 scripts

chrislloyd58

exact.n:Exact Samples Sizes and Inference for Clinical Trials with Binary Endpoint

Allows the user to determine minimum sample sizes that achieve target size and power at a specified alternative. For more information, see “Exact samples sizes for clinical trials subject to size and power constraints” by Lloyd, C.J. (2022) Preprint <doi:10.13140/RG.2.2.11828.94085>.

Maintained by Chris J. Lloyd. Last updated 1 years ago.

1.70 score

chrislloyd58

CLAST:Exact Confidence Limits after a Sequential Trial

The user first provides design vectors n, a and b as well as null (p0) and alternative (p1) benchmark values for the probability of success. The key function "mv.plots.SM()" calculates mean values of exact upper and lower limits based on four different rank ordering methods. These plots form the basis of selecting a rank ordering. The function "inference()" calculates exact limits from a provided realisation and ordering choice. For more information, see "Exact confidence limits after a group sequential single arm binary trial" by Lloyd, C.J. (2020), Statistics in Medicine, Volume 38, 2389-2399, <doi:10.1002/sim.8909>.

Maintained by Chris J. Lloyd. Last updated 3 years ago.

1.00 score

kolassa-dev

PHInfiniteEstimates:Tools for Inference in the Presence of a Monotone Likelihood

Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist. These difficulties are related to those arising from logistic and multinomial regression. References for methods are given in the separate function documents. Supported by grant NSF DMS 1712839.

Maintained by John E. Kolassa. Last updated 1 years ago.

1.00 score