R-universe search: newton

cran

NISTunits:Fundamental Physical Constants and Unit Conversions from NIST

Fundamental physical constants (Quantity, Value, Uncertainty, Unit) for SI (International System of Units) and non-SI units, plus unit conversions Based on the data from NIST (National Institute of Standards and Technology, USA)

Maintained by Jose Gama. Last updated 9 years ago.

65.2 match 2.85 score 10 dependents

hwborchers

pracma:Practical Numerical Math Functions

Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting.

Maintained by Hans W. Borchers. Last updated 1 years ago.

14.7 match 29 stars 12.34 score 6.6k scripts 931 dependents

nashjc

optimx:Expanded Replacement and Extension of the 'optim' Function

Provides a replacement and extension of the optim() function to call to several function minimization codes in R in a single statement. These methods handle smooth, possibly box constrained functions of several or many parameters. Note that function 'optimr()' was prepared to simplify the incorporation of minimization codes going forward. Also implements some utility codes and some extra solvers, including safeguarded Newton methods. Many methods previously separate are now included here. This is the version for CRAN.

Maintained by John C Nash. Last updated 2 months ago.

7.0 match 2 stars 12.87 score 1.8k scripts 89 dependents

boennecd

psqn:Partially Separable Quasi-Newton

Provides quasi-Newton methods to minimize partially separable functions. The methods are largely described by Nocedal and Wright (2006) <doi:10.1007/978-0-387-40065-5>.

Maintained by Benjamin Christoffersen. Last updated 6 months ago.

optimization optimization-algorithms quasi-newton openblas cpp openmp

15.9 match 2 stars 5.26 score 5 scripts 3 dependents

k3jph

cmna:Computational Methods for Numerical Analysis

Provides the source and examples for James P. Howard, II, "Computational Methods for Numerical Analysis with R," <https://jameshoward.us/cmna/>, a book on numerical methods in R.

Maintained by James Howard. Last updated 4 years ago.

bisection differential-equations heat-equation interpolation least-squares matrix-factorization monte-carlo newton numerical-analysis optimization partial-differential-equations quadrature root-finding secant splines testthat traveling-salesperson wave-equation

11.0 match 16 stars 5.65 score 62 scripts 3 dependents

doccstat

fastcpd:Fast Change Point Detection via Sequential Gradient Descent

Implements fast change point detection algorithm based on the paper "Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis" by Xianyang Zhang, Trisha Dawn <https://proceedings.mlr.press/v206/zhang23b.html>. The algorithm is based on dynamic programming with pruning and sequential gradient descent. It is able to detect change points a magnitude faster than the vanilla Pruned Exact Linear Time(PELT). The package includes examples of linear regression, logistic regression, Poisson regression, penalized linear regression data, and whole lot more examples with custom cost function in case the user wants to use their own cost function.

Maintained by Xingchi Li. Last updated 3 hours ago.

change-point-detection cpp custom-function gradient-descent lasso linear-regression logistic-regression offline pelt penalized-regression poisson-regression quasi-newton statistics time-series warm-start fortran openblas cpp openmp

8.0 match 22 stars 7.00 score 7 scripts

yihui

animation:A Gallery of Animations in Statistics and Utilities to Create Animations

Provides functions for animations in statistics, covering topics in probability theory, mathematical statistics, multivariate statistics, non-parametric statistics, sampling survey, linear models, time series, computational statistics, data mining and machine learning. These functions may be helpful in teaching statistics and data analysis. Also provided in this package are a series of functions to save animations to various formats, e.g. Flash, 'GIF', HTML pages, 'PDF' and videos. 'PDF' animations can be inserted into 'Sweave' / 'knitr' easily.

Maintained by Yihui Xie. Last updated 2 years ago.

animation statistical-computing statistical-graphics statistics

4.0 match 208 stars 12.08 score 2.5k scripts 29 dependents

fchamroukhi

samurais:Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')

Provides a variety of original and flexible user-friendly statistical latent variable models and unsupervised learning algorithms to segment and represent time-series data (univariate or multivariate), and more generally, longitudinal data, which include regime changes. 'samurais' is built upon the following packages, each of them is an autonomous time-series segmentation approach: Regression with Hidden Logistic Process ('RHLP'), Hidden Markov Model Regression ('HMMR'), Multivariate 'RHLP' ('MRHLP'), Multivariate 'HMMR' ('MHMMR'), Piece-Wise regression ('PWR'). For the advantages/differences of each of them, the user is referred to our mentioned paper references. These models are originally introduced and written in 'Matlab' by Faicel Chamroukhi <https://github.com/fchamroukhi?&tab=repositories&q=time-series&type=public&language=matlab>.

Maintained by Florian Lecocq. Last updated 5 years ago.

artificial-intelligence change-point-detection data-science dynamic-programming em-algorithm hidden-markov-models hidden-process-regression human-activity-recognition latent-variable-models model-selection multivariate-timeseries newton-raphson piecewise-regression statistical-inference statistical-learning time-series-analysis time-series-clustering openblas cpp

7.5 match 12 stars 6.18 score 28 scripts

asmahani

sns:Stochastic Newton Sampler (SNS)

Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided. Note: Formerly available versions of the MfUSampler can be obtained from the archive <https://cran.r-project.org/src/contrib/Archive/MfUSampler/>.

Maintained by Alireza Mahani. Last updated 2 years ago.

10.5 match 3.30 score 33 scripts 1 dependents

astamm

nloptr:R Interface to NLopt

Solve optimization problems using an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. See <https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/> for more information on the available algorithms. Building from included sources requires 'CMake'. On Linux and 'macOS', if a suitable system build of NLopt (2.7.0 or later) is found, it is used; otherwise, it is built from included sources via 'CMake'. On Windows, NLopt is obtained through 'rwinlib' for 'R <= 4.1.x' or grabbed from the appropriate toolchain for 'R >= 4.2.0'.

Maintained by Aymeric Stamm. Last updated 14 hours ago.

cpp

2.0 match 108 stars 17.13 score 1.1k scripts 1.8k dependents

cristiancastiglione

sgdGMF:Estimation of Generalized Matrix Factorization Models via Stochastic Gradient Descent

Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione, Segers, Clement, Risso (2024, <https://arxiv.org/abs/2412.20509>). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski, Hui, Warton, Hastie (2022, <http://jmlr.org/papers/v23/20-1104.html>).

Maintained by Cristian Castiglione. Last updated 11 days ago.

openblas cpp openmp

4.3 match 10 stars 7.75 score 108 scripts

otoomet

maxLik:Maximum Likelihood Estimation and Related Tools

Functions for Maximum Likelihood (ML) estimation, non-linear optimization, and related tools. It includes a unified way to call different optimizers, and classes and methods to handle the results from the Maximum Likelihood viewpoint. It also includes a number of convenience tools for testing and developing your own models.

Maintained by Ott Toomet. Last updated 12 months ago.

3.4 match 9.08 score 480 scripts 109 dependents

jrvarma

jrvFinance:Basic Finance; NPV/IRR/Annuities/Bond-Pricing; Black Scholes

Implements the basic financial analysis functions similar to (but not identical to) what is available in most spreadsheet software. This includes finding the IRR and NPV of regularly spaced cash flows and annuities. Bond pricing and YTM calculations are included. In addition, Black Scholes option pricing and Greeks are also provided.

Maintained by Jayanth Varma. Last updated 3 years ago.

4.9 match 11 stars 5.90 score 48 scripts 1 dependents

r-forge

DPQ:Density, Probability, Quantile ('DPQ') Computations

Computations for approximations and alternatives for the 'DPQ' (Density (pdf), Probability (cdf) and Quantile) functions for probability distributions in R. Primary focus is on (central and non-central) beta, gamma and related distributions such as the chi-squared, F, and t. -- For several distribution functions, provide functions implementing formulas from Johnson, Kotz, and Kemp (1992) <doi:10.1002/bimj.4710360207> and Johnson, Kotz, and Balakrishnan (1995) for discrete or continuous distributions respectively. This is for the use of researchers in these numerical approximation implementations, notably for my own use in order to improve standard R pbeta(), qgamma(), ..., etc: {'"dpq"'-functions}.

Maintained by Martin Maechler. Last updated 1 months ago.

fortran

5.0 match 5.75 score 43 scripts 1 dependents

kkholst

lava:Latent Variable Models

A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.

Maintained by Klaus K. Holst. Last updated 2 months ago.

latent-variable-models simulation statistics structural-equation-models

2.0 match 33 stars 12.85 score 610 scripts 476 dependents

fika-fianda

saebnocov:Small Area Estimation using Empirical Bayes without Auxiliary Variable

Estimates the parameter of small area in binary data without auxiliary variable using Empirical Bayes technique, mainly from Rao and Molina (2015,ISBN:9781118735787) with book entitled "Small Area Estimation Second Edition". This package provides another option of direct estimation using weight. This package also features alpha and beta parameter estimation on calculating process of small area. Those methods are Newton-Raphson and Moment which based on Wilcox (1979) <doi:10.1177/001316447903900302> and Kleinman (1973) <doi:10.1080/01621459.1973.10481332>.

Maintained by Siti Rafika Fiandasari. Last updated 3 years ago.

10.4 match 2.30 score 3 scripts

r-cas

Ryacas0:Legacy 'Ryacas' (Interface to 'Yacas' Computer Algebra System)

A legacy version of 'Ryacas', an interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).

Maintained by Mikkel Meyer Andersen. Last updated 2 years ago.

cpp

3.3 match 2 stars 7.11 score 36 scripts 6 dependents

zrmacc

Temporal:Parametric Time to Event Analysis

Performs maximum likelihood based estimation and inference on time to event data, possibly subject to non-informative right censoring. FitParaSurv() provides maximum likelihood estimates of model parameters and distributional characteristics, including the mean, median, variance, and restricted mean. CompParaSurv() compares the mean, median, and restricted mean survival experiences of two treatment groups. Candidate distributions include the exponential, gamma, generalized gamma, log-normal, and Weibull.

Maintained by Zachary McCaw. Last updated 1 years ago.

3.9 match 3 stars 5.96 score 34 scripts 1 dependents

fauvernierma

survPen:Multidimensional Penalized Splines for (Excess) Hazard Models, Relative Mortality Ratio Models and Marginal Intensity Models

Fits (excess) hazard, relative mortality ratio or marginal intensity models with multidimensional penalized splines allowing for time-dependent effects, non-linear effects and interactions between several continuous covariates. In survival and net survival analysis, in addition to modelling the effect of time (via the baseline hazard), one has often to deal with several continuous covariates and model their functional forms, their time-dependent effects, and their interactions. Model specification becomes therefore a complex problem and penalized regression splines represent an appealing solution to that problem as splines offer the required flexibility while penalization limits overfitting issues. Current implementations of penalized survival models can be slow or unstable and sometimes lack some key features like taking into account expected mortality to provide net survival and excess hazard estimates. In contrast, survPen provides an automated, fast, and stable implementation (thanks to explicit calculation of the derivatives of the likelihood) and offers a unified framework for multidimensional penalized hazard and excess hazard models. Later versions (>2.0.0) include penalized models for relative mortality ratio, and marginal intensity in recurrent event setting. survPen may be of interest to those who 1) analyse any kind of time-to-event data: mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to describe the associated hazard and to understand which predictors impact its dynamics, 3) wish to model the relative mortality ratio between a cohort and a reference population, 4) wish to describe the marginal intensity for recurrent event data. See Fauvernier et al. (2019a) <doi:10.21105/joss.01434> for an overview of the package and Fauvernier et al. (2019b) <doi:10.1111/rssc.12368> for the method.

Maintained by Mathieu Fauvernier. Last updated 3 months ago.

cpp

3.4 match 12 stars 6.82 score 85 scripts 1 dependents

rfastofficial

Rfast:A Collection of Efficient and Extremely Fast R Functions

A collection of fast (utility) functions for data analysis. Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. <doi:10.6339/JDS.201810_16(4).00006>. c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. <doi:10.48550/arXiv.1806.10947>. d) Tsagris M., Papadakis M., Alenazi A. and Alzeley O. (2024). Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm. Computation, 12(9): 185. <doi:10.3390/computation12090185>. e) Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. <doi:10.48550/arXiv.2501.02849>.

Maintained by Manos Papadakis. Last updated 17 days ago.

openblas cpp openmp

1.8 match 147 stars 12.54 score 1.2k scripts 166 dependents

cran

mgcv:Mixed GAM Computation Vehicle with Automatic Smoothness Estimation

Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.

Maintained by Simon Wood. Last updated 1 years ago.

openblas openmp

1.7 match 32 stars 12.71 score 17k scripts 7.8k dependents

nashjc

nlsr:Functions for Nonlinear Least Squares Solutions - Updated 2022

Provides tools for working with nonlinear least squares problems. For the estimation of models reliable and robust tools than nls(), where the the Gauss-Newton method frequently stops with 'singular gradient' messages. This is accomplished by using, where possible, analytic derivatives to compute the matrix of derivatives and a stabilization of the solution of the estimation equations. Tools for approximate or externally supplied derivative matrices are included. Bounds and masks on parameters are handled properly.

Maintained by John C Nash. Last updated 27 days ago.

3.1 match 7.02 score 94 scripts 5 dependents

isaacfab

rmetalog:The Metalog Distribution

Implementation of the metalog distribution in R. The metalog distribution is a modern, highly flexible, data-driven distribution. Metalogs are developed by Keelin (2016) <doi:10.1287/deca.2016.0338>. This package provides functions to build these distributions from raw data. Resulting metalog objects are then useful for exploratory and probabilistic analysis.

Maintained by Isaac Faber. Last updated 4 years ago.

3.3 match 34 stars 6.20 score 31 scripts

max-alletsee

pricesensitivitymeter:Van Westendorp Price Sensitivity Meter Analysis

An implementation of the van Westendorp Price Sensitivity Meter in R, which is a survey-based approach to analyze consumer price preferences and sensitivity (van Westendorp 1976, isbn:9789283100386).

Maintained by Max Alletsee. Last updated 1 years ago.

market-research price-sensitivity pricing survey survey-analysis

3.4 match 13 stars 5.72 score 27 scripts

cran

CGNM:Cluster Gauss-Newton Method

Find multiple solutions of a nonlinear least squares problem. Cluster Gauss-Newton method does not assume uniqueness of the solution of the nonlinear least squares problem and compute multiple minimizers. Please cite the following paper when this software is used in your research: Aoki et al. (2020) <doi:10.1007/s11081-020-09571-2>. Cluster Gauss–Newton method. Optimization and Engineering, 1-31. Please cite the following paper when profile likelihood plot is drawn with this software and used in your research: Aoki and Sugiyama (2024) <doi:10.1002/psp4.13055>. Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models. CPT Pharmacometrics Syst Pharmacol.13(1):54-67.

Maintained by Yasunori Aoki. Last updated 2 months ago.

7.0 match 2.48 score

climateanalytics

RGN:Robust-Gauss Newton (RGN) Optimization of Sum-of-Squares Objective Function

Implementation of the Robust Gauss-Newton (RGN) algorithm, designed for solving optimization problems with a sum of least squares objective function. For algorithm details please refer to Qin et. al. (2018) <doi:10.1029/2017WR022488>.

Maintained by David McInerney. Last updated 1 years ago.

fortran

5.2 match 3.18 score 2 scripts 1 dependents

robinhankin

elliptic:Weierstrass and Jacobi Elliptic Functions

A suite of elliptic and related functions including Weierstrass and Jacobi forms. Also includes various tools for manipulating and visualizing complex functions.

Maintained by Robin K. S. Hankin. Last updated 12 days ago.

1.8 match 3 stars 9.31 score 54 scripts 79 dependents

kaskr

RTMB:'R' Bindings for 'TMB'

Native 'R' interface to 'TMB' (Template Model Builder) so models can be written entirely in 'R' rather than 'C++'. Automatic differentiation, to any order, is available for a rich subset of 'R' features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. 'RTMB' provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.

Maintained by Kasper Kristensen. Last updated 1 months ago.

openblas cpp

1.5 match 54 stars 10.49 score 394 scripts 9 dependents

gabrielshimizu

AgroReg:Regression Analysis Linear and Nonlinear for Agriculture

Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). <doi:10.2134/agronj2012.0506>). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically.

Maintained by Gabriel Danilo Shimizu. Last updated 1 years ago.

5.6 match 2.71 score 102 scripts

dcnorris

DTAT:Dose Titration Algorithm Tuning

Dose Titration Algorithm Tuning (DTAT) is a methodologic framework allowing dose individualization to be conceived as a continuous learning process that begins in early-phase clinical trials and continues throughout drug development, on into clinical practice. This package includes code that researchers may use to reproduce or extend key results of the DTAT research programme, plus tools for trialists to design and simulate a '3+3/PC' dose-finding study. Please see Norris (2017a) <doi:10.12688/f1000research.10624.3> and Norris (2017c) <doi:10.1101/240846>.

Maintained by David C. Norris. Last updated 10 months ago.

5.1 match 2.90 score 20 scripts

cran

nleqslv:Solve Systems of Nonlinear Equations

Solve a system of nonlinear equations using a Broyden or a Newton method with a choice of global strategies such as line search and trust region. There are options for using a numerical or user supplied Jacobian, for specifying a banded numerical Jacobian and for allowing a singular or ill-conditioned Jacobian.

Maintained by Berend Hasselman. Last updated 1 years ago.

fortran openblas

2.3 match 3 stars 6.45 score 168 dependents

centerforassessment

SGP:Student Growth Percentiles & Percentile Growth Trajectories

An analytic framework for the calculation of norm- and criterion-referenced academic growth estimates using large scale, longitudinal education assessment data as developed in Betebenner (2009) <doi:10.1111/j.1745-3992.2009.00161.x>.

Maintained by Damian W. Betebenner. Last updated 2 months ago.

percentile-growth-projections quantile-regression sgp sgp-analyses student-growth-percentiles student-growth-projections

1.5 match 20 stars 9.69 score 88 scripts

cran

bayesm:Bayesian Inference for Marketing/Micro-Econometrics

Covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley first edition 2005 and second forthcoming) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014).

Maintained by Peter Rossi. Last updated 1 years ago.

openblas cpp

1.7 match 20 stars 8.20 score 322 scripts 43 dependents

cran

scam:Shape Constrained Additive Models

Generalized additive models under shape constraints on the component functions of the linear predictor. Models can include multiple shape-constrained (univariate and bivariate) and unconstrained terms. Routines of the package 'mgcv' are used to set up the model matrix, print, and plot the results. Multiple smoothing parameter estimation by the Generalized Cross Validation or similar. See Pya and Wood (2015) <doi:10.1007/s11222-013-9448-7> for an overview. A broad selection of shape-constrained smoothers, linear functionals of smooths with shape constraints, and Gaussian models with AR1 residuals.

Maintained by Natalya Pya. Last updated 2 months ago.

1.8 match 5 stars 7.17 score 388 scripts 23 dependents

bioc

EBSeq:An R package for gene and isoform differential expression analysis of RNA-seq data

Differential Expression analysis at both gene and isoform level using RNA-seq data

Maintained by Xiuyu Ma. Last updated 2 months ago.

immunooncology statisticalmethod differentialexpression multiplecomparison rnaseq sequencing cpp

1.6 match 7.77 score 162 scripts 6 dependents

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.

1.8 match 13 stars 6.96 score 63 scripts 13 dependents

mlysy

SuperGauss:Superfast Likelihood Inference for Stationary Gaussian Time Series

Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations. This package provides a "superfast" O(n log^2 n) algorithm written in C++, crossing over with Durbin-Levinson around n = 300. Efficient implementations of the score and Hessian functions are also provided, leading to superfast versions of inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo. The C++ code provides a Toeplitz matrix class packaged as a header-only library, to simplify low-level usage in other packages and outside of R.

Maintained by Martin Lysy. Last updated 1 months ago.

fftw3 cpp

1.8 match 2 stars 5.60 score 33 scripts 2 dependents

ahudde

greeks:Sensitivities of Prices of Financial Options and Implied Volatilities

Methods to calculate sensitivities of financial option prices for European, geometric and arithmetic Asian, and American options, with various payoff functions in the Black Scholes model, and in more general jump diffusion models. A shiny app to interactively plot the results is included. Furthermore, methods to compute implied volatilities are provided for a wide range of option types and custom payoff functions. Classical formulas are implemented for European options in the Black Scholes Model, as is presented in Hull, J. C. (2017), Options, Futures, and Other Derivatives. In the case of Asian options, Malliavin Monte Carlo Greeks are implemented, see Hudde, A. & Rüschendorf, L. (2023). European and Asian Greeks for exponential Lévy processes. <doi:10.1007/s11009-023-10014-5>. For American options, the Binomial Tree Method is implemented, as is presented in Hull, J. C. (2017).

Maintained by Anselm Hudde. Last updated 3 days ago.

asian-option greeks implied-volatility option cpp

1.7 match 7 stars 5.75 score 6 scripts

kaskr

RTMBp:'R' Bindings for 'TMB'

Native 'R' interface to 'TMB' (Template Model Builder) so models can be written entirely in 'R' rather than 'C++'. Automatic differentiation, to any order, is available for a rich subset of 'R' features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. 'RTMBp' provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.

Maintained by Kasper Kristensen. Last updated 1 months ago.

openblas cpp openmp

1.5 match 51 stars 6.44 score 1 scripts

rmojab63

ldt:Automated Uncertainty Analysis

Methods and tools for model selection and multi-model inference (Burnham and Anderson (2002) <doi:10.1007/b97636>, among others). 'SUR' (for parameter estimation), 'logit'/'probit' (for binary classification), and 'VARMA' (for time-series forecasting) are implemented. Evaluations are both in-sample and out-of-sample. It is designed to be efficient in terms of CPU usage and memory consumption.

Maintained by Ramin Mojab. Last updated 8 months ago.

openblas cpp openmp

3.9 match 2.48 score 7 scripts

npmldabook

npmlda:Non-Parametric Models for Longitudinal Data Analysis

Support the book: Wu CO and Tian X (2018). Nonparametric Models for Longitudinal Data: With Implementation in R. (Chapman & Hall/CRC Monographs on Statistics & Applied Probability); Present global and local smoothing methods for the conditional-mean and conditional-distribution based nonparametric models with longitudinal Data.

Maintained by Xin Tian. Last updated 6 years ago.

3.5 match 2.70 score 8 scripts

bioc

EBarrays:Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification

EBarrays provides tools for the analysis of replicated/unreplicated microarray data.

Maintained by Ming Yuan. Last updated 5 months ago.

clustering differentialexpression

1.7 match 5.56 score 5 scripts 6 dependents

namezys

polyMatrix:Infrastructure for Manipulation Polynomial Matrices

Implementation of class "polyMatrix" for storing a matrix of polynomials and implements basic matrix operations; including a determinant and characteristic polynomial. It is based on the package 'polynom' and uses a lot of its methods to implement matrix operations. This package includes 3 methods of triangularization of polynomial matrices: Extended Euclidean algorithm which is most classical but numerically unstable; Sylvester algorithm based on LQ decomposition; Interpolation algorithm is based on LQ decomposition and Newton interpolation. Both methods are described in D. Henrion & M. Sebek, Reliable numerical methods for polynomial matrix triangularization, IEEE Transactions on Automatic Control (Volume 44, Issue 3, Mar 1999, Pages 497-508) <doi:10.1109/9.751344> and in Salah Labhalla, Henri Lombardi & Roger Marlin, Algorithmes de calcule de la reduction de Hermite d'une matrice a coefficients polynomeaux, Theoretical Computer Science (Volume 161, Issue 1-2, July 1996, Pages 69-92) <doi:10.1016/0304-3975(95)00090-9>.

Maintained by Nikolai Ryzhkov. Last updated 4 years ago.

3.1 match 1 stars 3.00 score 2 scripts

forestscientist

StemAnalysis:Reconstructing Tree Growth and Carbon Accumulation with Stem Analysis Data

Use stem analysis data to reconstructing tree growth and carbon accumulation. Users can independently or in combination perform a number of standard tasks for any tree species. (i) Age class determination. (ii) The cumulative growth, mean annual increment, and current annual increment of diameter at breast height (DBH) with bark, tree height, and stem volume with bark are estimated. (iii) Tree biomass and carbon storage estimation from volume and allometric models are calculated. (iv) Height-diameter relationship is fitted with nonlinear models, if diameter at breast height (DBH) or tree height are available, which can be used to retrieve tree height and diameter at breast height (DBH). <https://github.com/forestscientist/StemAnalysis>.

Maintained by Huili Wu. Last updated 2 years ago.

2.1 match 4 stars 4.38 score 12 scripts

laurimeh

lmfor:Functions for Forest Biometrics

Functions for different purposes related to forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, modelling of diameter distributions and estimation off stand density using ITD. Several empirical datasets are also included.

Maintained by Lauri Mehtatalo. Last updated 3 years ago.

3.4 match 3 stars 2.42 score 29 scripts 1 dependents

bertvanderveen

minic:Minimization Methods for Ill-Conditioned Problems

Implementation of methods for minimizing ill-conditioned problems. Currently only includes regularized (quasi-)newton optimization (Kanzow and Steck et al. (2023), <doi:10.1007/s12532-023-00238-4>).

Maintained by Bert van der Veen. Last updated 6 months ago.

cpp

2.4 match 1 stars 3.40 score

bioc

scDDboost:A compositional model to assess expression changes from single-cell rna-seq data

scDDboost is an R package to analyze changes in the distribution of single-cell expression data between two experimental conditions. Compared to other methods that assess differential expression, scDDboost benefits uniquely from information conveyed by the clustering of cells into cellular subtypes. Through a novel empirical Bayesian formulation it calculates gene-specific posterior probabilities that the marginal expression distribution is the same (or different) between the two conditions. The implementation in scDDboost treats gene-level expression data within each condition as a mixture of negative binomial distributions.

Maintained by Xiuyu Ma. Last updated 2 months ago.

singlecell software clustering sequencing geneexpression differentialexpression bayesian cpp

1.7 match 4.58 score 19 scripts

inlabru-org

dirinla:Hierarchical Bayesian Dirichlet regression models using Integrated Nested Laplace Approximation

The R-package dirinla allows the user to fit models in the compositional data context. In particular, it allows fit Dirichlet regression models using the Integrated Nested Laplace Approximation (INLA) methodology.

Maintained by Joaquín Martínez-Minaya. Last updated 4 months ago.

2.0 match 4 stars 3.89 score 13 scripts

karlines

rootSolve:Nonlinear Root Finding, Equilibrium and Steady-State Analysis of Ordinary Differential Equations

Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). Includes routines that: (1) generate gradient and jacobian matrices (full and banded), (2) find roots of non-linear equations by the 'Newton-Raphson' method, (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running, (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations by numerical differencing (using the method-of-lines approach). Includes fortran code.

Maintained by Karline Soetaert. Last updated 1 years ago.

fortran

0.8 match 1 stars 9.61 score 1.2k scripts 216 dependents

ninohardt

echoice2:Choice Models with Economic Foundation

Implements choice models based on economic theory, including estimation using Markov chain Monte Carlo (MCMC), prediction, and more. Its usability is inspired by ideas from 'tidyverse'. Models include versions of the Hierarchical Multinomial Logit and Multiple Discrete-Continous (Volumetric) models with and without screening. The foundations of these models are described in Allenby, Hardt and Rossi (2019) <doi:10.1016/bs.hem.2019.04.002>. Models with conjunctive screening are described in Kim, Hardt, Kim and Allenby (2022) <doi:10.1016/j.ijresmar.2022.04.001>. Models with set-size variation are described in Hardt and Kurz (2020) <doi:10.2139/ssrn.3418383>.

Maintained by Nino Hardt. Last updated 1 years ago.

choice-models openblas cpp openmp

1.8 match 1 stars 4.00 score 7 scripts

mezarafael

Bhat:General Likelihood Exploration

Provides functions for Maximum Likelihood Estimation, Markov Chain Monte Carlo, finding confidence intervals. The implementation is heavily based on the original Fortran source code translated to R.

Maintained by Rafael Meza. Last updated 3 years ago.

3.3 match 2.16 score 36 scripts

nashjc

Rtnmin:Truncated Newton Function Minimization with Bounds Constraints

Truncated Newton function minimization with bounds constraints based on the 'Matlab'/'Octave' codes of Stephen Nash.

Maintained by John C Nash. Last updated 9 years ago.

7.0 match 1.00 score 5 scripts

cran

Opt5PL:Optimal Designs for the 5-Parameter Logistic Model

Obtain and evaluate various optimal designs for the 3, 4, and 5-parameter logistic models. The optimal designs are obtained based on the numerical algorithm in Hyun, Wong, Yang (2018) <doi:10.18637/jss.v083.i05>.

Maintained by Seung Won Hyun. Last updated 6 years ago.

cpp

6.8 match 1.00 score

tabea17

noisySBM:Noisy Stochastic Block Mode: Graph Inference by Multiple Testing

Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying binary graph. This procedure comes with a control of the false discovery rate. The method is described in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, E. Roquain, F. Villers (2020) <arXiv:1907.10176>.

Maintained by Tabea Rebafka. Last updated 4 years ago.

3.3 match 2.00 score 2 scripts

covaruber

sommer:Solving Mixed Model Equations in R

Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.

Maintained by Giovanny Covarrubias-Pazaran. Last updated 22 days ago.

average-information mixed-models rcpparmadillo openblas cpp openmp

0.5 match 43 stars 12.70 score 300 scripts 9 dependents

cran

Compositional:Compositional Data Analysis

Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. We further include functions for percentages (or proportions). The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include: a) Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. <doi:10.48550/arXiv.1106.1451> b) Tsagris M. (2014). "The k-NN algorithm for compositional data: a revised approach with and without zero values present". Journal of Data Science, 12(3): 519--534. <doi:10.6339/JDS.201407_12(3).0008>. c) Tsagris M. (2015). "A novel, divergence based, regression for compositional data". Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. <doi:10.48550/arXiv.1511.07600>. d) Tsagris M. (2015). "Regression analysis with compositional data containing zero values". Chilean Journal of Statistics, 6(2): 47--57. <https://soche.cl/chjs/volumes/06/02/Tsagris(2015).pdf>. e) Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved supervised classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. <doi:10.1007/s00357-016-9207-5>. f) Tsagris M., Preston S. and Wood A.T.A. (2017). "Nonparametric hypothesis testing for equality of means on the simplex". Journal of Statistical Computation and Simulation, 87(2): 406--422. <doi:10.1080/00949655.2016.1216554>. g) Tsagris M. and Stewart C. (2018). "A Dirichlet regression model for compositional data with zeros". Lobachevskii Journal of Mathematics, 39(3): 398--412. <doi:10.1134/S1995080218030198>. h) Alenazi A. (2019). "Regression for compositional data with compositional data as predictor variables with or without zero values". Journal of Data Science, 17(1): 219--238. <doi:10.6339/JDS.201901_17(1).0010>. i) Tsagris M. and Stewart C. (2020). "A folded model for compositional data analysis". Australian and New Zealand Journal of Statistics, 62(2): 249--277. <doi:10.1111/anzs.12289>. j) Alenazi A.A. (2022). "f-divergence regression models for compositional data". Pakistan Journal of Statistics and Operation Research, 18(4): 867--882. <doi:10.18187/pjsor.v18i4.3969>. k) Tsagris M. and Stewart C. (2022). "A Review of Flexible Transformations for Modeling Compositional Data". In Advances and Innovations in Statistics and Data Science, pp. 225--234. <doi:10.1007/978-3-031-08329-7_10>. l) Alenazi A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. <doi:10.1080/03610926.2021.2014890>. m) Tsagris M., Alenazi A. and Stewart C. (2023). "Flexible non-parametric regression models for compositional response data with zeros". Statistics and Computing, 33(106). <doi:10.1007/s11222-023-10277-5>. n) Tsagris. M. (2025). "Constrained least squares simplicial-simplicial regression". Statistics and Computing, 35(27). <doi:10.1007/s11222-024-10560-z>. o) Sevinc V. and Tsagris. M. (2024). "Energy Based Equality of Distributions Testing for Compositional Data". <doi:10.48550/arXiv.2412.05199>.

Maintained by Michail Tsagris. Last updated 2 months ago.

1.8 match 3 stars 3.64 score 4 dependents

piusdahinden

disprofas:Non-Parametric Dissolution Profile Analysis

Similarity of dissolution profiles is assessed using the similarity factor f2 according to the EMA guideline (European Medicines Agency 2010) "On the investigation of bioequivalence". Dissolution profiles are regarded as similar if the f2 value is between 50 and 100. For the applicability of the similarity factor f2, the variability between profiles needs to be within certain limits. Often, this constraint is violated. One possibility in this situation is to resample the measured profiles in order to obtain a bootstrap estimate of f2 (Shah et al. (1998) <doi:10.1023/A:1011976615750>). Other alternatives are the model-independent non-parametric multivariate confidence region (MCR) procedure (Tsong et al. (1996) <doi:10.1177/009286159603000427>) or the T2-test for equivalence procedure (Hoffelder (2016) <https://www.ecv.de/suse_item.php?suseId=Z|pi|8430>). Functions for estimation of f1, f2, bootstrap f2, MCR / T2-test for equivalence procedure are implemented.

Maintained by Pius Dahinden. Last updated 9 months ago.

1.7 match 3.70 score 2 scripts

cran

spuRs:Functions and Datasets for "Introduction to Scientific Programming and Simulation Using R"

Provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson. 2014. An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.

Maintained by Andrew Robinson. Last updated 7 years ago.

3.6 match 1 stars 1.66 score 46 scripts

mfasiolo

qgam:Smooth Additive Quantile Regression Models

Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>. See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. Differently from 'quantreg', the smoothing parameters are estimated automatically by marginal loss minimization, while the regression coefficients are estimated using either PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian credible intervals of the estimated effects have approximately the correct coverage. The main function is qgam() which is similar to gam() in 'mgcv', but fits non-parametric quantile regression models.

Maintained by Matteo Fasiolo. Last updated 5 days ago.

0.5 match 33 stars 10.13 score 133 scripts 15 dependents

hayoungshin1

GeodRegr:Geodesic Regression

Provides a gradient descent algorithm to find a geodesic relationship between real-valued independent variables and a manifold-valued dependent variable (i.e. geodesic regression). Available manifolds are Euclidean space, the sphere, hyperbolic space, and Kendall's 2-dimensional shape space. Besides the standard least-squares loss, the least absolute deviations, Huber, and Tukey biweight loss functions can also be used to perform robust geodesic regression. Functions to help choose appropriate cutoff parameters to maintain high efficiency for the Huber and Tukey biweight estimators are included, as are functions for generating random tangent vectors from the Riemannian normal distributions on the sphere and hyperbolic space. The n-sphere is a n-dimensional manifold: we represent it as a sphere of radius 1 and center 0 embedded in (n+1)-dimensional space. Using the hyperboloid model of hyperbolic space, n-dimensional hyperbolic space is embedded in (n+1)-dimensional Minkowski space as the upper sheet of a hyperboloid of two sheets. Kendall's 2D shape space with K landmarks is of real dimension 2K-4; preshapes are represented as complex K-vectors with mean 0 and magnitude 1. Details are described in Shin, H.-Y. and Oh, H.-S. (2020) <arXiv:2007.04518>. Also see Fletcher, P. T. (2013) <doi:10.1007/s11263-012-0591-y>.

Maintained by Ha-Young Shin. Last updated 4 years ago.

1.8 match 2.70 score 6 scripts

wahani

saeRobust:Robust Small Area Estimation

Methods to fit robust alternatives to commonly used models used in Small Area Estimation. The methods here used are based on best linear unbiased predictions and linear mixed models. At this time available models include area level models incorporating spatial and temporal correlation in the random effects.

Maintained by Sebastian Warnholz. Last updated 1 years ago.

openblas cpp

1.2 match 1 stars 4.03 score 12 scripts 3 dependents

hdakpo

ucminf:General-Purpose Unconstrained Non-Linear Optimization

An algorithm for general-purpose unconstrained non-linear optimization. The algorithm is of quasi-Newton type with BFGS updating of the inverse Hessian and soft line search with a trust region type monitoring of the input to the line search algorithm. The interface of 'ucminf' is designed for easy interchange with 'optim'.

Maintained by K Hervé Dakpo. Last updated 9 months ago.

fortran openblas

0.5 match 3 stars 9.24 score 155 scripts 204 dependents

jocelynchi

L2E:Robust Structured Regression via the L2 Criterion

An implementation of a computational framework for performing robust structured regression with the L2 criterion from Chi and Chi (2021+). Improvements using the majorization-minimization (MM) principle from Liu, Chi, and Lange (2022+) added in Version 2.0.

Maintained by Jocelyn Chi. Last updated 3 years ago.

1.8 match 2.70 score 2 scripts

cran

BayesFBHborrow:Bayesian Dynamic Borrowing with Flexible Baseline Hazard Function

Allows Bayesian borrowing from a historical dataset for time-to- event data. A flexible baseline hazard function is achieved via a piecewise exponential likelihood with time varying split points and smoothing prior on the historic baseline hazards. The method is described in Scott and Lewin (2024) <doi:10.48550/arXiv.2401.06082>, and the software paper is in Axillus et al. (2024) <doi:10.48550/arXiv.2408.04327>.

Maintained by Darren Scott. Last updated 6 months ago.

3.6 match 1.30 score

s-baumann

FixedPoint:Algorithms for Finding Fixed Point Vectors of Functions

For functions that take and return vectors (or scalars), this package provides 8 algorithms for finding fixed point vectors (vectors for which the inputs and outputs to the function are the same vector). These algorithms include Anderson (1965) acceleration <doi:10.1145/321296.321305>, epsilon extrapolation methods (Wynn 1962 <doi:10.2307/2004051>) and minimal polynomial methods (Cabay and Jackson 1976 <doi:10.1137/0713060>).

Maintained by Stuart Baumann. Last updated 2 years ago.

1.3 match 1 stars 3.69 score 33 scripts 1 dependents

zhengsengui

NLRoot:searching for the root of equation

This is a package which can help you search for the root of a equation.

Maintained by Zheng Sengui. Last updated 13 years ago.

4.0 match 1.00 score

plasmogenepi

malaria.em:EM Estimation of Malaria Haplotype Probabilities from Multiply Infected Human Blood Samples

Using EM algorithm to estimate malaria haplotype probabilities and the number of infections from multiply infected human blood samples. Estimated haplotype probabilities and their standard error are reported.

Maintained by Xiaohong Li. Last updated 2 months ago.

2.3 match 1.70 score

cran

GlarmaVarSel:Variable Selection in Sparse GLARMA Models

Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2020), <arXiv:2007.08623v1>.

Maintained by Marina Gomtsyan. Last updated 4 years ago.

1.8 match 2.00 score 2 scripts

cran

MultiGlarmaVarSel:Variable Selection in Sparse Multivariate GLARMA Models

Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2022), <arXiv:2208.14721>.

Maintained by Marina Gomtsyan. Last updated 3 years ago.

1.8 match 2.00 score

mgomtsyan

NBtsVarSel:Variable Selection in a Specific Regression Time Series of Counts

Performs variable selection in sparse negative binomial GLARMA (Generalised Linear Autoregressive Moving Average) models. For further details we refer the reader to the paper Gomtsyan (2023), <arXiv:2307.00929>.

Maintained by Marina Gomtsyan. Last updated 2 years ago.

1.8 match 2.00 score 2 scripts

vivianephilipps

marqLevAlg:A Parallelized General-Purpose Optimization Based on Marquardt-Levenberg Algorithm

This algorithm provides a numerical solution to the problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than the Gauss-Newton-like algorithm when starting from points very far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2021 <doi:10.32614/RJ-2021-089>.

Maintained by Viviane Philipps. Last updated 1 years ago.

fortran

0.5 match 7 stars 6.52 score 12 scripts 10 dependents

weiserc

mvQuad:Methods for Multivariate Quadrature

Provides methods to construct multivariate grids, which can be used for multivariate quadrature. This grids can be based on different quadrature rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss quadrature (Gauss-Hermite, Gauss-Legendre, ...). For the construction of the multidimensional grid the product-rule or the combination- technique can be applied.

Maintained by Constantin Weiser. Last updated 9 years ago.

0.5 match 3 stars 6.15 score 45 scripts 7 dependents

cran

ivaBSS:Tools for Independent Vector Analysis

Independent vector analysis (IVA) is a blind source separation (BSS) model where several datasets are jointly unmixed. This package provides several methods for the unmixing together with some performance measures. For details, see Anderson et al. (2011) <doi:10.1109/TSP.2011.2181836> and Lee et al. (2007) <doi:10.1016/j.sigpro.2007.01.010>.

Maintained by Mika Sipilä. Last updated 3 years ago.

1.8 match 1.70 score

hmjianggatech

SAM:Sparse Additive Modelling

Computationally efficient tools for high dimensional predictive modeling (regression and classification). SAM is short for sparse additive modeling, and adopts the computationally efficient basis spline technique. We solve the optimization problems by various computational algorithms including the block coordinate descent algorithm, fast iterative soft-thresholding algorithm, and newton method. The computation is further accelerated by warm-start and active-set tricks.

Maintained by Haoming Jiang. Last updated 3 years ago.

cpp openmp

0.5 match 6 stars 5.86 score 20 scripts 4 dependents

garybaylor

mixR:Finite Mixture Modeling for Raw and Binned Data

Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'.

Maintained by Youjiao Yu. Last updated 5 months ago.

cpp

0.5 match 8 stars 5.66 score 95 scripts 1 dependents

bioc

fmrs:Variable Selection in Finite Mixture of AFT Regression and FMR Models

The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net.

Maintained by Farhad Shokoohi. Last updated 5 months ago.

survival regression dimensionreduction

0.5 match 3 stars 5.00 score 55 scripts 1 dependents

cran

VGAMextra:Additions and Extensions of the 'VGAM' Package

Extending the functionalities of the 'VGAM' package with additional functions and datasets. At present, 'VGAMextra' comprises new family functions (ffs) to estimate several time series models by maximum likelihood using Fisher scoring, unlike popular packages in CRAN relying on optim(), including ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non- seasonal), the Order-(p) VAR model, error correction models for cointegrated time series, and ARMA-structures with Student-t errors. For independent data, new ffs to estimate the inverse- Weibull, the inverse-gamma, the generalized beta of the second kind and the general multivariate normal distributions are available. In addition, 'VGAMextra' incorporates new VGLM-links for the mean-function, and the quantile-function (as an alternative to ordinary quantile modelling) of several 1-parameter distributions, that are compatible with the class of VGLM/VGAM family functions. Currently, only fixed-effects models are implemented. All functions are subject to change; see the NEWS for further details on the latest changes.

Maintained by Victor Miranda. Last updated 1 months ago.

2.0 match 1 stars 1.26 score

mlysy

LocalCop:Local Likelihood Inference for Conditional Copula Models

Implements a local likelihood estimator for the dependence parameter in bivariate conditional copula models. Copula family and local likelihood bandwidth parameters are selected by leave-one-out cross-validation. The models are implemented in 'TMB', meaning that the local score function is efficiently calculated via automated differentiation (AD), such that quasi-Newton algorithms may be used for parameter estimation.

Maintained by Martin Lysy. Last updated 6 months ago.

0.5 match 1 stars 4.85 score 9 scripts

cran

Copula.Markov:Copula-Based Estimation and Statistical Process Control for Serially Correlated Time Series

Estimation and statistical process control are performed under copula-based time-series models. Available are statistical methods in Long and Emura (2014 JCSA), Emura et al. (2017 Commun Stat-Simul) <DOI:10.1080/03610918.2015.1073303>, Huang and Emura (2021 Commun Stat-Simul) <DOI:10.1080/03610918.2019.1602647>, Lin et al. (2021 Comm Stat-Simul) <DOI:10.1080/03610918.2019.1652318>, Sun et al. (2020 JSS Series in Statistics)<DOI:10.1007/978-981-15-4998-4>, and Huang and Emura (2021, in revision).

Maintained by Takeshi Emura. Last updated 3 years ago.

1.6 match 3 stars 1.48 score

juhkim111

MGLM:Multivariate Response Generalized Linear Models

Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data. Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.

Maintained by Juhyun Kim. Last updated 3 years ago.

0.5 match 4 stars 4.65 score 53 scripts 1 dependents

um-kevinhe

surtvep:Cox Non-Proportional Hazards Model with Time-Varying Coefficients

Fit Cox non-proportional hazards models with time-varying coefficients. Both unpenalized procedures (Newton and proximal Newton) and penalized procedures (P-splines and smoothing splines) are included using B-spline basis functions for estimating time-varying coefficients. For penalized procedures, cross validations, mAIC, TIC or GIC are implemented to select tuning parameters. Utilities for carrying out post-estimation visualization, summarization, point-wise confidence interval and hypothesis testing are also provided. For more information, see Wu et al. (2022) <doi: 10.1007/s10985-021-09544-2> and Luo et al. (2023) <doi:10.1177/09622802231181471>.

Maintained by Lingfeng Luo. Last updated 1 years ago.

openblas cpp openmp

0.8 match 1 stars 3.00 score 2 scripts

nchenderson

rvalues:R-Values for Ranking in High-Dimensional Settings

A collection of functions for computing "r-values" from various kinds of user input such as MCMC output or a list of effect size estimates and associated standard errors. Given a large collection of measurement units, the r-value, r, of a particular unit is a reported percentile that may be interpreted as the smallest percentile at which the unit should be placed in the top r-fraction of units.

Maintained by Nicholas Henderson. Last updated 4 years ago.

1.8 match 1.30 score 20 scripts

dvdscripter

FI:Provide functions for forest inventory calculations

Provide functions for forest inventory calculations. Common volumetric equations (Smalian, Newton and Huber) as well stacking factor and form

Maintained by David V. Dias. Last updated 12 years ago.

0.5 match 1 stars 3.70 score 4 scripts

ejikeugba

serp:Smooth Effects on Response Penalty for CLM

Implements a regularization method for cumulative link models using the Smooth-Effect-on-Response Penalty (SERP). This method allows flexible modeling of ordinal data by enabling a smooth transition from a general cumulative link model to a simplified version of the same model. As the tuning parameter increases from zero to infinity, the subject-specific effects for each variable converge to a single global effect. The approach addresses common issues in cumulative link models, such as parameter unidentifiability and numerical instability, by maximizing a penalized log-likelihood instead of the standard non-penalized version. Fitting is performed using a modified Newton's method. Additionally, the package includes various model performance metrics and descriptive tools. For details on the implemented penalty method, see Ugba (2021) <doi:10.21105/joss.03705> and Ugba et al. (2021) <doi:10.3390/stats4030037>.

Maintained by Ejike R. Ugba. Last updated 4 months ago.

categorical-data ordinal-regression penalized-regression proportional-odds-regression regularization-techniques

0.5 match 1 stars 3.86 score 44 scripts

jinhuasu

SemiEstimate:Solve Semi-Parametric Estimation by Implicit Profiling

Semi-parametric estimation problem can be solved by two-step Newton-Raphson iteration. The implicit profiling method<arXiv:2108.07928> is an improved method of two-step NR iteration especially for the implicit-bundled type of the parametric part and non-parametric part. This package provides a function semislv() supporting the above two methods and numeric derivative approximation for unprovided Jacobian matrix.

Maintained by Jinhua Su. Last updated 3 years ago.

0.5 match 3.70 score 3 scripts

beanb2

intkrige:A Numerical Implementation of Interval-Valued Kriging

An interval-valued extension of ordinary and simple kriging. Optimization of the function is based on a generalized interval distance. This creates a non-differentiable cost function that requires a differentiable approximation to the absolute value function. This differentiable approximation is optimized using a Newton-Raphson algorithm with a penalty function to impose the constraints. Analyses in the package are driven by the 'intsp' and 'intgrd' classes, which are interval-valued extensions of 'SpatialPointsDataFrame' and 'SpatialPixelsDataFrame' respectively. The package includes several wrappers to functions in the 'gstat' and 'sp' packages.

Maintained by Brennan Bean. Last updated 4 years ago.

openblas cpp

0.5 match 3.70 score 6 scripts

zzheng68

MixTwice:Large-Scale Hypothesis Testing by Variance Mixing

Implements large-scale hypothesis testing by variance mixing. It takes two statistics per testing unit -- an estimated effect and its associated squared standard error -- and fits a nonparametric, shape-constrained mixture separately on two latent parameters. It reports local false discovery rates (lfdr) and local false sign rates (lfsr). Manuscript describing algorithm of MixTwice: Zheng et al(2021) <doi: 10.1093/bioinformatics/btab162>.

Maintained by Zihao Zheng. Last updated 3 years ago.

1.9 match 1.00 score 1 scripts

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.

1.8 match 1.00 score

asmahani

RegressionFactory:Expander Functions for Generating Full Gradient and Hessian from Single-Slot and Multi-Slot Base Distributions

The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors' paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.

Maintained by Alireza S. Mahani. Last updated 4 years ago.

0.8 match 2.30 score 20 scripts

cran

optR:Optimization Toolbox for Solving Linear Systems

Solves linear systems of form Ax=b via Gauss elimination, LU decomposition, Gauss-Seidel, Conjugate Gradient Method (CGM) and Cholesky methods.

Maintained by Prakash. Last updated 8 years ago.

1.8 match 1 stars 1.00 score

cran

glmglrt:GLRT P-Values in Generalized Linear Models

Provides functions to compute Generalized Likelihood Ratio Tests (GLRT) also known as Likelihood Ratio Tests (LRT) and Rao's score tests of simple and complex contrasts of Generalized Linear Models (GLMs). It provides the same interface as summary.glm(), adding GLRT P-values, less biased than Wald's P-values and consistent with profile-likelihood confidence interval generated by confint(). See Wilks (1938) <doi:10.1214/aoms/1177732360> for the LRT chi-square approximation. See Rao (1948) <doi:10.1017/S0305004100023987> for Rao's score test. See Wald (1943) <doi:10.2307/1990256> for Wald's test.

Maintained by André GILLIBERT. Last updated 5 years ago.

1.7 match 1.00 score

cran

glarma:Generalized Linear Autoregressive Moving Average Models

Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables. These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.

Maintained by "William T.M. Dunsmuir". Last updated 7 years ago.

0.5 match 2 stars 3.26 score 1 dependents

cran

JSparO:Joint Sparse Optimization via Proximal Gradient Method for Cell Fate Conversion

Implementation of joint sparse optimization (JSparO) to infer the gene regulatory network for cell fate conversion. The proximal gradient method is implemented to solve different low-order regularization models for JSparO.

Maintained by Xinlin Hu. Last updated 3 years ago.

1.6 match 1 stars 1.00 score

cran

RegCombin:Partially Linear Regression under Data Combination

We implement linear regression when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked, based on D'Haultfoeuille, Gaillac, Maurel (2022) <doi:10.3386/w29953>. The package allows for common regressors observed in both datasets, and for various shape constraints on the effect of covariates on the outcome of interest. It also provides the tools to perform a test of point identification. See the associated vignette <https://github.com/cgaillac/RegCombin/blob/master/RegCombin_vignette.pdf> for theory and code examples.

Maintained by Christophe Gaillac. Last updated 1 years ago.

1.6 match 1 stars 1.00 score

loremerdrignac

EpiStats:Tools for Epidemiologists

Provides set of functions aimed at epidemiologists. The package includes commands for measures of association and impact for case control studies and cohort studies. It may be particularly useful for outbreak investigations including univariable analysis and stratified analysis. The functions for cohort studies include the CS(), CSTable() and CSInter() commands. The functions for case control studies include the CC(), CCTable() and CCInter() commands. References - Cornfield, J. 1956. A statistical problem arising from retrospective studies. In Vol. 4 of Proceedings of the Third Berkeley Symposium, ed. J. Neyman, 135-148. Berkeley, CA - University of California Press. Woolf, B. 1955. On estimating the relation between blood group disease. Annals of Human Genetics 19 251-253. Reprinted in Evolution of Epidemiologic Ideas Annotated Readings on Concepts and Methods, ed. S. Greenland, pp. 108-110. Newton Lower Falls, MA Epidemiology Resources. Gilles Desve & Peter Makary, 2007. 'CSTABLE Stata module to calculate summary table for cohort study' Statistical Software Components S456879, Boston College Department of Economics. Gilles Desve & Peter Makary, 2007. 'CCTABLE Stata module to calculate summary table for case-control study' Statistical Software Components S456878, Boston College Department of Economics.

Maintained by Lore Merdrignac. Last updated 1 years ago.

0.5 match 2.91 score 82 scripts

cran

mixdist:Finite Mixture Distribution Models

Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.

Maintained by Peter Macdonald. Last updated 7 years ago.

0.5 match 2.78 score 2 dependents

uds-mf-imbei

BSW:Fitting a Log-Binomial Model using the Bekhit-Schöpe-Wagenpfeil (BSW) Algorithm

Implements a modified Newton-type algorithm (BSW algorithm) for solving the maximum likelihood estimation problem in fitting a log-binomial model under linear inequality constraints.

Maintained by Adam Bekhit. Last updated 4 years ago.

0.5 match 2.70 score

nashjc

nlmrt:Functions for Nonlinear Least Squares Solutions

Replacement for nls() tools for working with nonlinear least squares problems. The calling structure is similar to, but much simpler than, that of the nls() function. Moreover, where nls() specifically does NOT deal with small or zero residual problems, nlmrt is quite happy to solve them. It also attempts to be more robust in finding solutions, thereby avoiding 'singular gradient' messages that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach in nlmrt generally works more reliably to get a solution, though this may be one of a set of possibilities, and may also be statistically unsatisfactory. Added print and summary as of August 28, 2012.

Maintained by John C. Nash. Last updated 9 years ago.

0.5 match 2.72 score 52 scripts

vallejosgroup

bayefdr:Bayesian Estimation and Optimisation of Expected False Discovery Rate

Implements the Bayesian FDR control described by Newton et al. (2004), <doi:10.1093/biostatistics/5.2.155>. Allows optimisation and visualisation of expected error rates based on tail posterior probability tests. Based on code written by Catalina Vallejos for BASiCS, see Beyond comparisons of means: understanding changes in gene expression at the single-cell level Vallejos et al. (2016) <doi:10.1186/s13059-016-0930-3>.

Maintained by Alan OCallaghan. Last updated 3 years ago.

0.5 match 2.70 score 1 scripts

sangkyustat

EMSS:Some EM-Type Estimation Methods for the Heckman Selection Model

Some EM-type algorithms to estimate parameters for the well-known Heckman selection model are provided in the package. Such algorithms are as follow: ECM(Expectation/Conditional Maximization), ECM(NR)(the Newton-Raphson method is adapted to the ECM) and ECME(Expectation/Conditional Maximization Either). Since the algorithms are based on the EM algorithm, they also have EM’s main advantages, namely, stability and ease of implementation. Further details and explanations of the algorithms can be found in Zhao et al. (2020) <doi: 10.1016/j.csda.2020.106930>.

Maintained by Sang Kyu Lee. Last updated 3 years ago.

0.5 match 2.48 score 1 scripts

novacta

nprotreg:Nonparametric Rotations for Sphere-Sphere Regression

Fits sphere-sphere regression models by estimating locally weighted rotations. Simulation of sphere-sphere data according to non-rigid rotation models. Provides methods for bias reduction applying iterative procedures within a Newton-Raphson learning scheme. Cross-validation is exploited to select smoothing parameters. See Marco Di Marzio, Agnese Panzera & Charles C. Taylor (2018) <doi:10.1080/01621459.2017.1421542>.

Maintained by Giovanni Lafratta. Last updated 1 years ago.

0.5 match 1 stars 2.34 score 44 scripts

renschler

pqantimalarials:web tool for estimating under-five deaths caused by poor-quality antimalarials in sub-Saharan Africa

This package allows users to calculate the number of under-five child deaths caused by consumption of poor quality antimalarials across 39 sub-Saharan nations. The package supports one function, that starts an interactive web tool created using the shiny R package. The web tool runs locally on the user's machine. The web tool allows users to set input parameters (prevalence of poor quality antimalarials, case fatality rate of children who take poor quality antimalarials, and sample size) which are then used to perform an uncertainty analysis following the Latin hypercube sampling scheme. Users can download the output figures as PDFs, and the output data as CSVs. Users can also download their input parameters for reference. This package was designed to accompany the analysis presented in: J. Patrick Renschler, Kelsey Walters, Paul Newton, Ramanan Laxminarayan "Estimated under-five deaths associated with poor-quality antimalarials in sub-Saharan Africa", 2014. Paper submitted.

Maintained by J. Patrick Renschler. Last updated 11 years ago.

0.5 match 1.70 score 3 scripts

nineisprime

asm:Optimal Convex M-Estimation for Linear Regression via Antitonic Score Matching

Performs linear regression with respect to a data-driven convex loss function that is chosen to minimize the asymptotic covariance of the resulting M-estimator. The convex loss function is estimated in 5 steps: (1) form an initial OLS (ordinary least squares) or LAD (least absolute deviation) estimate of the regression coefficients; (2) use the resulting residuals to obtain a kernel estimator of the error density; (3) estimate the score function of the errors by differentiating the logarithm of the kernel density estimate; (4) compute the L2 projection of the estimated score function onto the set of decreasing functions; (5) take a negative antiderivative of the projected score function estimate. Newton's method (with Hessian modification) is then used to minimize the convex empirical risk function. Further details of the method are given in Feng et al. (2024) <doi:10.48550/arXiv.2403.16688>.

Maintained by Min Xu. Last updated 2 months ago.

0.5 match 1.68 score 16 scripts

kkunji

BigQuic:Big Quadratic Inverse Covariance Estimation

Use Newton's method, coordinate descent, and METIS clustering to solve the L1 regularized Gaussian MLE inverse covariance matrix estimation problem.

Maintained by Khalid B. Kunji. Last updated 2 years ago.

cpp openmp

0.5 match 1 stars 1.00 score 10 scripts

klauschn

fastM:Fast Computation of Multivariate M-Estimators

Implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. The algorithm is described in Duembgen, Nordhausen and Schuhmacher (2016) <doi:10.1016/j.jmva.2015.11.009>.

Maintained by Klaus Nordhausen. Last updated 7 years ago.

openblas cpp

0.5 match 1.00 score 5 scripts

eulogepagui

PLordprob:Multivariate Ordered Probit Model via Pairwise Likelihood

Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likelihood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb.

Maintained by Euloge Clovis Kenne Pagui. Last updated 7 years ago.

fortran

0.5 match 1.00 score 2 scripts