R-universe search: gradient

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.

18.7 match 2 stars 12.87 score 1.8k scripts 89 dependents

bmgaldo

graDiEnt:Stochastic Quasi-Gradient Differential Evolution Optimization

An optim-style implementation of the Stochastic Quasi-Gradient Differential Evolution (SQG-DE) optimization algorithm first published by Sala, Baldanzini, and Pierini (2018; <doi:10.1007/978-3-319-72926-8_27>). This optimization algorithm fuses the robustness of the population-based global optimization algorithm "Differential Evolution" with the efficiency of gradient-based optimization. The derivative-free algorithm uses population members to build stochastic gradient estimates, without any additional objective function evaluations. Sala, Baldanzini, and Pierini argue this algorithm is useful for 'difficult optimization problems under a tight function evaluation budget.' This package can run SQG-DE in parallel and sequentially.

Maintained by Brendan Matthew Galdo. Last updated 3 months ago.

58.8 match 4 stars 3.78 score 4 scripts

mlverse

torch:Tensors and Neural Networks with 'GPU' Acceleration

Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.

Maintained by Daniel Falbel. Last updated 6 days ago.

autograd deep-learning torch cpp

12.5 match 520 stars 16.52 score 1.4k scripts 38 dependents

eguidotti

calculus:High Dimensional Numerical and Symbolic Calculus

Efficient C++ optimized functions for numerical and symbolic calculus as described in Guidotti (2022) <doi:10.18637/jss.v104.i05>. It includes basic arithmetic, tensor calculus, Einstein summing convention, fast computation of the Levi-Civita symbol and generalized Kronecker delta, Taylor series expansion, multivariate Hermite polynomials, high-order derivatives, ordinary differential equations, differential operators (Gradient, Jacobian, Hessian, Divergence, Curl, Laplacian) and numerical integration in arbitrary orthogonal coordinate systems: cartesian, polar, spherical, cylindrical, parabolic or user defined by custom scale factors.

Maintained by Emanuele Guidotti. Last updated 2 years ago.

calculus coordinate-systems curl divergence einstein finite-difference gradient hermite hessian jacobian laplacian numerical-derivation numerical-derivatives numerical-differentiation symbolic-computation symbolic-differentiation taylor cpp

22.0 match 47 stars 8.92 score 66 scripts 7 dependents

boost-r

mboost:Model-Based Boosting

Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.

Maintained by Torsten Hothorn. Last updated 4 months ago.

boosting-algorithms gam glm machine-learning mboost modelling r-language tutorials variable-selection openblas

15.4 match 72 stars 12.70 score 540 scripts 27 dependents

albertopessia

drda:Dose-Response Data Analysis

Fit logistic functions to observed dose-response continuous data and evaluate goodness-of-fit measures. See Malyutina A., Tang J., and Pessia A. (2023) <doi:10.18637/jss.v106.i04>.

Maintained by Alberto Pessia. Last updated 2 months ago.

dose-response logistic-function

35.1 match 11 stars 5.50 score 19 scripts

mjskay

ggdist:Visualizations of Distributions and Uncertainty

Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) <https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>, density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>, quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>, complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>, and fit curves with multiple uncertainty ribbons.

Maintained by Matthew Kay. Last updated 4 months ago.

ggplot2 uncertainty uncertainty-visualization visualization cpp

11.8 match 856 stars 15.24 score 3.1k scripts 61 dependents

mlr-org

mlr3extralearners:Extra Learners For mlr3

Extra learners for use in mlr3.

Maintained by Sebastian Fischer. Last updated 4 months ago.

machine-learning mlr3

17.8 match 94 stars 9.16 score 474 scripts

bips-hb

innsight:Get the Insights of Your Neural Network

Interpretation methods for analyzing the behavior and individual predictions of modern neural networks in a three-step procedure: Converting the model, running the interpretation method, and visualizing the results. Implemented methods are, e.g., 'Connection Weights' described by Olden et al. (2004) <doi:10.1016/j.ecolmodel.2004.03.013>, layer-wise relevance propagation ('LRP') described by Bach et al. (2015) <doi:10.1371/journal.pone.0130140>, deep learning important features ('DeepLIFT') described by Shrikumar et al. (2017) <doi:10.48550/arXiv.1704.02685> and gradient-based methods like 'SmoothGrad' described by Smilkov et al. (2017) <doi:10.48550/arXiv.1706.03825>, 'Gradient x Input' or 'Vanilla Gradient'. Details can be found in the accompanying scientific paper: Koenen & Wright (2024, Journal of Statistical Software, <doi:10.18637/jss.v111.i08>).

Maintained by Niklas Koenen. Last updated 4 months ago.

21.6 match 30 stars 7.01 score 57 scripts

rstudio

keras3:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.

Maintained by Tomasz Kalinowski. Last updated 4 days ago.

10.9 match 845 stars 13.57 score 264 scripts 2 dependents

ibarraespinosa

cptcity:'cpt-city' Colour Gradients

Incorporates colour gradients from the 'cpt-city' web archive available at <http://soliton.vm.bytemark.co.uk/pub/cpt-city/>.

Maintained by Sergio Ibarra-Espinosa. Last updated 2 years ago.

color colors colour colour-palette colours cpt-city cptcity gradient

21.9 match 18 stars 6.12 score 243 scripts 2 dependents

trevorld

ggpattern:'ggplot2' Pattern Geoms

Provides 'ggplot2' geoms filled with various patterns. Includes a patterned version of every 'ggplot2' geom that has a region that can be filled with a pattern. Provides a suite of 'ggplot2' aesthetics and scales for controlling pattern appearances. Supports over a dozen builtin patterns (every pattern implemented by 'gridpattern') as well as allowing custom user-defined patterns.

Maintained by Trevor L. Davis. Last updated 2 months ago.

10.8 match 368 stars 12.33 score 1.7k scripts 3 dependents

trivialfis

xgboost:Extreme Gradient Boosting

Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>. This package is its R interface. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine which could be more than 10 times faster than existing gradient boosting packages. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.

Maintained by Jiaming Yuan. Last updated 8 months ago.

cpp openmp

11.0 match 6 stars 11.70 score 13k scripts 112 dependents

jlmelville

mize:Unconstrained Numerical Optimization Algorithms

Optimization algorithms implemented in R, including conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited memory BFGS (L-BFGS) methods. Most internal parameters can be set through the call interface. The solvers hold up quite well for higher-dimensional problems.

Maintained by James Melville. Last updated 2 months ago.

conjugate-gradient l-bfgs numerical-optimization

18.4 match 10 stars 6.95 score 25 scripts 6 dependents

airoldilab

sgd:Stochastic Gradient Descent for Scalable Estimation

A fast and flexible set of tools for large scale estimation. It features many stochastic gradient methods, built-in models, visualization tools, automated hyperparameter tuning, model checking, interval estimation, and convergence diagnostics.

Maintained by Junhyung Lyle Kim. Last updated 1 years ago.

big-data data-analysis gradient-descent statistics openblas cpp

15.8 match 62 stars 7.25 score 71 scripts

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.

9.2 match 29 stars 12.34 score 6.6k scripts 931 dependents

distancedevelopment

mrds:Mark-Recapture Distance Sampling

Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.

Maintained by Laura Marshall. Last updated 2 months ago.

13.9 match 4 stars 8.05 score 78 scripts 7 dependents

r-lib

scales:Scale Functions for Visualization

Graphical scales map data to aesthetics, and provide methods for automatically determining breaks and labels for axes and legends.

Maintained by Thomas Lin Pedersen. Last updated 5 months ago.

ggplot2

5.5 match 419 stars 19.88 score 88k scripts 7.9k dependents

funecology

fundiversity:Easy Computation of Functional Diversity Indices

Computes six functional diversity indices. These are namely, Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness (FRic), Functional Richness intersections (FRic_intersect), Functional Dispersion (FDis), and Rao's entropy (Q) (reviewed in Villéger et al. 2008 <doi:10.1890/07-1206.1>). Provides efficient, modular, and parallel functions to compute functional diversity indices (Grenié & Gruson 2023 <doi:10.1111/ecog.06585>).

Maintained by Matthias Grenié. Last updated 8 months ago.

biodiversity biodiversity-indicators biodiversity-informatics functional-diversity functional-ecology functional-trait functional-traits trait trait-based traits

13.7 match 38 stars 7.34 score 38 scripts

dieghernan

tidyterra:'tidyverse' Methods and 'ggplot2' Helpers for 'terra' Objects

Extension of the 'tidyverse' for 'SpatRaster' and 'SpatVector' objects of the 'terra' package. It includes also new 'geom_' functions that provide a convenient way of visualizing 'terra' objects with 'ggplot2'.

Maintained by Diego Hernangómez. Last updated 1 days ago.

terra ggplot-extension r-spatial rspatial

7.2 match 191 stars 13.62 score 1.9k scripts 25 dependents

tidyverse

ggplot2:Create Elegant Data Visualisations Using the Grammar of Graphics

A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

Maintained by Thomas Lin Pedersen. Last updated 9 days ago.

data-visualisation visualisation

3.9 match 6.6k stars 25.10 score 645k scripts 7.5k dependents

haghish

mlim:Single and Multiple Imputation with Automated Machine Learning

Machine learning algorithms have been used for performing single missing data imputation and most recently, multiple imputations. However, this is the first attempt for using automated machine learning algorithms for performing both single and multiple imputation. Automated machine learning is a procedure for fine-tuning the model automatic, performing a random search for a model that results in less error, without overfitting the data. The main idea is to allow the model to set its own parameters for imputing each variable separately instead of setting fixed predefined parameters to impute all variables of the dataset. Using automated machine learning, the package fine-tunes an Elastic Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting, or Stacked Ensemble machine learning model (from one or a combination of other supported algorithms) for imputing the missing observations. This procedure has been implemented for the first time by this package and is expected to outperform other packages for imputing missing data that do not fine-tune their models. The multiple imputation is implemented via bootstrapping without letting the duplicated observations to harm the cross-validation procedure, which is the way imputed variables are evaluated. Most notably, the package implements automated procedure for handling imputing imbalanced data (class rarity problem), which happens when a factor variable has a level that is far more prevalent than the other(s). This is known to result in biased predictions, hence, biased imputation of missing data. However, the autobalancing procedure ensures that instead of focusing on maximizing accuracy (classification error) in imputing factor variables, a fairer procedure and imputation method is practiced.

Maintained by E. F. Haghish. Last updated 8 months ago.

automatic-machine-learning automl classimbalance data-science elastic-net extreme-gradient-boosting gbm glm gradient-boosting gradient-boosting-machine imputation imputation-algorithm imputation-methods machine-learning missing-data multipleimputation stack-ensemble

21.6 match 31 stars 4.49 score 7 scripts

mpiktas

midasr:Mixed Data Sampling Regression

Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions.

Maintained by Vaidotas Zemlys-Balevičius. Last updated 3 years ago.

16.8 match 77 stars 5.76 score 150 scripts

gavinsimpson

analogue:Analogue and Weighted Averaging Methods for Palaeoecology

Fits Modern Analogue Technique and Weighted Averaging transfer function models for prediction of environmental data from species data, and related methods used in palaeoecology.

Maintained by Gavin L. Simpson. Last updated 6 months ago.

10.7 match 14 stars 8.96 score 185 scripts 4 dependents

collinerickson

GauPro:Gaussian Process Fitting

Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel, and 'Rcpp' has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) <doi:10.1016/j.ejor.2017.10.002>.

Maintained by Collin Erickson. Last updated 11 hours ago.

openblas cpp openmp

11.1 match 16 stars 8.44 score 104 scripts 1 dependents

ropensci

stplanr:Sustainable Transport Planning

Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. The package was originally developed to support the 'Propensity to Cycle Tool', a publicly available strategic cycle network planning tool (Lovelace et al. 2017) <doi:10.5198/jtlu.2016.862>, but has since been extended to support public transport routing and accessibility analysis (Moreno-Monroy et al. 2017) <doi:10.1016/j.jtrangeo.2017.08.012> and routing with locally hosted routing engines such as 'OSRM' (Lowans et al. 2023) <doi:10.1016/j.enconman.2023.117337>. The main functions are for creating and manipulating geographic "desire lines" from origin-destination (OD) data (building on the 'od' package); calculating routes on the transport network locally and via interfaces to routing services such as <https://cyclestreets.net/> (Desjardins et al. 2021) <doi:10.1007/s11116-021-10197-1>; and calculating route segment attributes such as bearing. The package implements the 'travel flow aggregration' method described in Morgan and Lovelace (2020) <doi:10.1177/2399808320942779> and the 'OD jittering' method described in Lovelace et al. (2022) <doi:10.32866/001c.33873>. Further information on the package's aim and scope can be found in the vignettes and in a paper in the R Journal (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053>, and in a paper outlining the landscape of open source software for geographic methods in transport planning (Lovelace, 2021) <doi:10.1007/s10109-020-00342-2>.

Maintained by Robin Lovelace. Last updated 7 months ago.

cycle cycling desire-lines origin-destination peer-reviewed pubic-transport route-network routes routing spatial transport transport-planning transportation walking

7.0 match 427 stars 12.31 score 684 scripts 3 dependents

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

8.6 match 1 stars 9.61 score 1.2k scripts 216 dependents

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.

8.9 match 9.08 score 480 scripts 109 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 1 months ago.

data-science data-visualization machine-learning machine-learning-library visualization

11.3 match 145 stars 7.09 score 50 scripts 2 dependents

hneth

unicol:The Colors of your University

Most universities use specific color combinations to express their unique brand identity. The 'unicol' package provides the colors and color palettes of various universities for easy plotting and printing in R. We collect and provide a diverse range of color palettes for creating scientific visualizations.

Maintained by Hansjoerg Neth. Last updated 7 months ago.

branding color color-palettes color-schemes corporate-design university-colors visual-identity

11.9 match 9 stars 6.58 score 10 scripts

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 4 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

11.1 match 22 stars 7.00 score 7 scripts

stan-dev

cmdstanr:R Interface to 'CmdStan'

A lightweight interface to 'Stan' <https://mc-stan.org>. The 'CmdStanR' interface is an alternative to 'RStan' that calls the command line interface for compilation and running algorithms instead of interfacing with C++ via 'Rcpp'. This has many benefits including always being compatible with the latest version of Stan, fewer installation errors, fewer unexpected crashes in RStudio, and a more permissive license.

Maintained by Andrew Johnson. Last updated 9 months ago.

bayes bayesian markov-chain-monte-carlo maximum-likelihood mcmc stan variational-inference

6.3 match 145 stars 12.27 score 5.2k scripts 9 dependents

t-kalinowski

keras:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.

Maintained by Tomasz Kalinowski. Last updated 11 months ago.

6.9 match 10.82 score 10k scripts 54 dependents

ypan1988

roptim:General Purpose Optimization in R using C++

Perform general purpose optimization in R using C++. A unified wrapper interface is provided to call C functions of the five optimization algorithms ('Nelder-Mead', 'BFGS', 'CG', 'L-BFGS-B' and 'SANN') underlying optim().

Maintained by Yi Pan. Last updated 3 years ago.

armadillo bfgs conjugate-gradient l-bfgs-b nelder-mead optim rcpp simulated-annealing openblas cpp

10.8 match 20 stars 6.93 score 15 scripts 12 dependents

renkun-ken

formattable:Create 'Formattable' Data Structures

Provides functions to create formattable vectors and data frames. 'Formattable' vectors are printed with text formatting, and formattable data frames are printed with multiple types of formatting in HTML to improve the readability of data presented in tabular form rendered in web pages.

Maintained by Kun Ren. Last updated 3 months ago.

5.1 match 700 stars 14.69 score 3.6k scripts 26 dependents

dmurdoch

plotrix:Various Plotting Functions

Lots of plots, various labeling, axis and color scaling functions. The author/maintainer died in September 2023.

Maintained by Duncan Murdoch. Last updated 1 years ago.

6.3 match 5 stars 11.31 score 9.2k scripts 361 dependents

facebookexperimental

Robyn:Semi-Automated Marketing Mix Modeling (MMM) from Meta Marketing Science

Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.

Maintained by Gufeng Zhou. Last updated 19 days ago.

adstocking budget-allocation cost-response-curve econometrics evolutionary-algorithm gradient-based-optimisation hyperparameter-optimization marketing-mix-modeling marketing-mix-modelling marketing-science mmm ridge-regression

6.7 match 1.2k stars 10.32 score 95 scripts

ropensci

slopes:Calculate Slopes of Roads, Rivers and Trajectories

Functions and example data to support research into the slope (also known as longitudinal gradient or steepness) of linear geographic entities such as roads <doi:10.1038/s41597-019-0147-x> and rivers <doi:10.1016/j.jhydrol.2018.06.066>. The package was initially developed to calculate the steepness of street segments but can be used to calculate steepness of any linear feature that can be represented as LINESTRING geometries in the 'sf' class system. The package takes two main types of input data for slope calculation: vector geographic objects representing linear features, and raster geographic objects with elevation values (which can be downloaded using functionality in the package) representing a continuous terrain surface. Where no raster object is provided the package attempts to download elevation data using the 'ceramic' package.

Maintained by Robin Lovelace. Last updated 5 months ago.

9.9 match 70 stars 6.91 score 39 scripts

jeffreyracine

np:Nonparametric Kernel Smoothing Methods for Mixed Data Types

Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.

Maintained by Jeffrey S. Racine. Last updated 1 months ago.

5.3 match 49 stars 12.64 score 672 scripts 44 dependents

r-spatialecology

belg:Boltzmann Entropy of a Landscape Gradient

Calculates the Boltzmann entropy of a landscape gradient. This package uses the analytical method created by Gao, P., Zhang, H. and Li, Z., 2018 (<doi:10.1111/tgis.12315>) and by Gao, P. and Li, Z., 2019 (<doi:10.1007/s10980-019-00854-3>). It also extend the original ideas by allowing calculations on data with missing values.

Maintained by Jakub Nowosad. Last updated 2 years ago.

entropy landscape raster spatial cpp

10.9 match 19 stars 5.80 score 11 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

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

nanxstats

stackgbm:Stacked Gradient Boosting Machines

A minimalist implementation of model stacking by Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> for boosted tree models. A classic, two-layer stacking model is implemented, where the first layer generates features using gradient boosting trees, and the second layer employs a logistic regression model that uses these features as inputs. Utilities for training the base models and parameters tuning are provided, allowing users to experiment with different ensemble configurations easily. It aims to provide a simple and efficient way to combine multiple gradient boosting models to improve predictive model performance and robustness.

Maintained by Nan Xiao. Last updated 11 months ago.

automl catboost decision-trees ensemble-learning gbdt gbm gradient-boosting lightgbm machine-learning model-stacking xgboost

11.4 match 25 stars 5.40 score 3 scripts

brian-j-smith

MachineShop:Machine Learning Models and Tools

Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.

Maintained by Brian J Smith. Last updated 7 months ago.

classification-models machine-learning predictive-modeling regression-models survival-models

7.5 match 61 stars 7.95 score 121 scripts

fifis

pnd:Parallel Numerical Derivatives, Gradients, Jacobians, and Hessians of Arbitrary Accuracy Order

Numerical derivatives through finite-difference approximations can be calculated using the 'pnd' package with parallel capabilities and optimal step-size selection to improve accuracy. These functions facilitate efficient computation of derivatives, gradients, Jacobians, and Hessians, allowing for more evaluations to reduce the mathematical and machine errors. Designed for compatibility with the 'numDeriv' package, which has not received updates in several years, it introduces advanced features such as computing derivatives of arbitrary order, improving the accuracy of Hessian approximations by avoiding repeated differencing, and parallelising slow functions on Windows, Mac, and Linux.

Maintained by Andreï Victorovitch Kostyrka. Last updated 6 days ago.

11.3 match 1 stars 5.22 score 5 scripts

ghbolstad

evolvability:Calculation of Evolvability Parameters

Provides tools for calculating evolvability parameters from estimated G-matrices as defined in Hansen and Houle (2008) <doi:10.1111/j.1420-9101.2008.01573.x> and fits phylogenetic comparative models that link the rate of evolution of a trait to the state of another evolving trait (see Hansen et al. 2021 Systematic Biology <doi:10.1093/sysbio/syab079>). The package was released with Bolstad et al. (2014) <doi:10.1098/rstb.2013.0255>, which contains some examples of use.

Maintained by Geir H. Bolstad. Last updated 10 months ago.

13.7 match 4.20 score 16 scripts

bioc

zinbwave:Zero-Inflated Negative Binomial Model for RNA-Seq Data

Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data.

Maintained by Davide Risso. Last updated 5 months ago.

immunooncology dimensionreduction geneexpression rnaseq software transcriptomics sequencing singlecell

5.5 match 43 stars 10.53 score 190 scripts 6 dependents

stochastictree

stochtree:Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference

Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285> for supervised learning and Bayesian Causal Forests (BCF) Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195> for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers.

Maintained by Drew Herren. Last updated 18 days ago.

bart bayesian-machine-learning bayesian-methods decision-trees gradient-boosted-trees machine-learning probabilistic-models tree-ensembles cpp

6.7 match 20 stars 8.52 score 40 scripts

trevorhastie

glmnet:Lasso and Elastic-Net Regularized Generalized Linear Models

Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression; see <doi:10.18637/jss.v033.i01> and <doi:10.18637/jss.v039.i05>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<doi:10.18637/jss.v106.i01>). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited.

Maintained by Trevor Hastie. Last updated 2 years ago.

fortran cpp

3.7 match 82 stars 15.15 score 22k scripts 736 dependents

glmmtmb

glmmTMB:Generalized Linear Mixed Models using Template Model Builder

Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.

Maintained by Mollie Brooks. Last updated 12 days ago.

cpp openmp

3.3 match 312 stars 16.77 score 3.7k scripts 24 dependents

r-forge

numDeriv:Accurate Numerical Derivatives

Methods for calculating (usually) accurate numerical first and second order derivatives. Accurate calculations are done using 'Richardson''s' extrapolation or, when applicable, a complex step derivative is available. A simple difference method is also provided. Simple difference is (usually) less accurate but is much quicker than 'Richardson''s' extrapolation and provides a useful cross-check. Methods are provided for real scalar and vector valued functions.

Maintained by Paul Gilbert. Last updated 2 months ago.

3.9 match 1 stars 14.10 score 1.2k scripts 3.1k dependents

asgr

imager:Image Processing Library Based on 'CImg'

Fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one colour dimension). Provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analysing image data using R. The package wraps 'CImg', <http://cimg.eu>, a simple, modern C++ library for image processing.

Maintained by Aaron Robotham. Last updated 27 days ago.

libx11 fftw3 tiff cpp openmp

4.0 match 17 stars 13.62 score 2.4k scripts 45 dependents

lbbe-software

fitdistrplus:Help to Fit of a Parametric Distribution to Non-Censored or Censored Data

Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit estimation (MGE) and maximum spacing estimation (MSE) methods (available only for non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction to parametric estimation.

Maintained by Aurélie Siberchicot. Last updated 13 days ago.

3.3 match 54 stars 16.15 score 4.5k scripts 153 dependents

jonclayden

shades:Simple Colour Manipulation

Functions for easily manipulating colours, creating colour scales and calculating colour distances.

Maintained by Jon Clayden. Last updated 5 months ago.

color color-manipulation colour colour-manipulation colour-spaces

5.6 match 83 stars 9.58 score 178 scripts 37 dependents

hdvinod

generalCorr:Generalized Correlations, Causal Paths and Portfolio Selection

Function gmcmtx0() computes a more reliable (general) correlation matrix. Since causal paths from data are important for all sciences, the package provides many sophisticated functions. causeSummBlk() and causeSum2Blk() give easy-to-interpret causal paths. Let Z denote control variables and compare two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent" than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These inequalities between many absolute values are quantified by four orders of stochastic dominance. Our third criterion Cr3, for the causal path X to Y, requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|. The function parcorVec() reports generalized partials between the first variable and all others. The package provides several R functions including get0outliers() for outlier detection, bigfp() for numerical integration by the trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts, canonRho() for generalized canonical correlations, depMeas() measures nonlinear dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx() can rank several stocks. Functions whose names begin with 'boot' provide bootstrap statistical inference, including a new bootGcRsq() test for "Granger-causality" allowing nonlinear relations. A new tool for evaluation of out-of-sample portfolio performance is outOFsamp(). Panel data implementation is now included. See eight vignettes of the package for theory, examples, and usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}.

Maintained by H. D. Vinod. Last updated 1 years ago.

11.5 match 2 stars 4.48 score 63 scripts 1 dependents

davidgohel

ggiraph:Make 'ggplot2' Graphics Interactive

Create interactive 'ggplot2' graphics using 'htmlwidgets'.

Maintained by David Gohel. Last updated 3 months ago.

libpng cpp

3.5 match 819 stars 14.39 score 4.1k scripts 34 dependents

mlampros

OpenImageR:An Image Processing Toolkit

Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.

Maintained by Lampros Mouselimis. Last updated 2 years ago.

filtering gabor-feature-extraction gabor-filters hog-features image image-hashing processing rcpparmadillo recognition slic slico superpixels openblas cpp openmp

5.2 match 60 stars 9.86 score 358 scripts 8 dependents

bioc

pcaMethods:A collection of PCA methods

Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany.

Maintained by Henning Redestig. Last updated 5 months ago.

bayesian cpp

3.9 match 49 stars 13.10 score 538 scripts 73 dependents

bioc

RCy3:Functions to Access and Control Cytoscape

Vizualize, analyze and explore networks using Cytoscape via R. Anything you can do using the graphical user interface of Cytoscape, you can now do with a single RCy3 function.

Maintained by Alex Pico. Last updated 5 months ago.

visualization graphandnetwork thirdpartyclient network

3.6 match 52 stars 13.39 score 628 scripts 15 dependents

mlr-org

mlr3learners:Recommended Learners for 'mlr3'

Recommended Learners for 'mlr3'. Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting.

Maintained by Marc Becker. Last updated 4 months ago.

classification learners machine-learning mlr3 regression

4.1 match 91 stars 11.51 score 1.5k scripts 10 dependents

gavinsimpson

coenocliner:Coenocline Simulation

Simulate species occurrence and abundances (counts) along gradients.

Maintained by Gavin L. Simpson. Last updated 4 years ago.

7.8 match 12 stars 6.03 score 15 scripts 1 dependents

feddelegrand7

corazon:Apply 'colorffy' Color Gradients Within 'shiny' Elements

Allows the user to apply nice color gradients to 'shiny' elements. The gradients are extracted from the 'colorffy' website. See <https://www.colorffy.com/gradients/catalog>.

Maintained by Mohamed El Fodil Ihaddaden. Last updated 4 years ago.

css shiny

11.1 match 3 stars 4.22 score 11 scripts

mlampros

KernelKnn:Kernel k Nearest Neighbors

Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.

Maintained by Lampros Mouselimis. Last updated 2 years ago.

cpp11 distance-metric kernel-methods knn rcpparmadillo openblas cpp openmp

5.1 match 17 stars 9.16 score 54 scripts 13 dependents

tomasfryda

h2o:R Interface for the 'H2O' Scalable Machine Learning Platform

R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).

Maintained by Tomas Fryda. Last updated 1 years ago.

5.7 match 3 stars 8.20 score 7.8k scripts 11 dependents

roliveros-ramos

calibrar:Automated Parameter Estimation for Complex Models

General optimisation and specific tools for the parameter estimation (i.e. calibration) of complex models, including stochastic ones. It implements generic functions that can be used for fitting any type of models, especially those with non-differentiable objective functions, with the same syntax as 'stats::optim()'. It supports multiple phases estimation (sequential parameter masking), constrained optimization (bounding box restrictions) and automatic parallel computation of numerical gradients. Some common maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs is provided. See <https://roliveros-ramos.github.io/calibrar/> for more details.

Maintained by Ricardo Oliveros-Ramos. Last updated 19 days ago.

modeling optimization optimization-methods

7.3 match 7 stars 6.05 score 27 scripts

dwbapst

paleoAM:Simulating Assemblage Models of Abundance for the Fossil Record

Provides functions for fitting abundance distributions over environmental gradients to the species in ecological communities, and tools for simulating the fossil assemblages from those abundance models for such communities, as well as simulating assemblages across various patterns of sedimentary history and sampling. These tools are for particular use with fossil records with detailed age models and abundance distributions used for calculating environmental gradients from ordinations or other indices based on fossil assemblages.

Maintained by David W Bapst. Last updated 5 months ago.

10.0 match 4 stars 4.38 score 1 scripts

cran

DiceOptim:Kriging-Based Optimization for Computer Experiments

Efficient Global Optimization (EGO) algorithm as described in "Roustant et al. (2012)" <doi:10.18637/jss.v051.i01> and adaptations for problems with noise ("Picheny and Ginsbourger, 2012") <doi:10.1016/j.csda.2013.03.018>, parallel infill, and problems with constraints.

Maintained by Victor Picheny. Last updated 4 years ago.

13.9 match 4 stars 3.11 score 107 scripts 1 dependents

jhorzek

lessSEM:Non-Smooth Regularization for Structural Equation Models

Provides regularized structural equation modeling (regularized SEM) with non-smooth penalty functions (e.g., lasso) building on 'lavaan'. The package is heavily inspired by the ['regsem'](<https://github.com/Rjacobucci/regsem>) and ['lslx'](<https://github.com/psyphh/lslx>) packages.

Maintained by Jannik H. Orzek. Last updated 1 years ago.

lasso psychometrics regularization regularized-structural-equation-model sem structural-equation-modeling openblas cpp openmp

6.0 match 7 stars 7.19 score 223 scripts

sthomas522

hmclearn:Fit Statistical Models Using Hamiltonian Monte Carlo

Provide users with a framework to learn the intricacies of the Hamiltonian Monte Carlo algorithm with hands-on experience by tuning and fitting their own models. All of the code is written in R. Theoretical references are listed below:. Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418, Betancourt, Michael (2017) "A Conceptual Introduction to Hamiltonian Monte Carlo" <arXiv:1701.02434>, Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arXiv:2006.16194>, Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174.

Maintained by Samuel Thomas. Last updated 4 years ago.

7.6 match 11 stars 5.64 score 16 scripts

jefferis

colorRamps:Builds Color Tables

Builds gradient color maps.

Maintained by Gregory Jefferis. Last updated 1 years ago.

6.3 match 1 stars 6.82 score 1.7k scripts 46 dependents

spatstat

spatstat.data:Datasets for 'spatstat' Family

Contains all the datasets for the 'spatstat' family of packages.

Maintained by Adrian Baddeley. Last updated 20 hours ago.

kernel-density point-process spatial-analysis spatial-data spatial-data-analysis spatstat statistical-analysis statistical-methods statistical-tests statistics

3.8 match 6 stars 11.00 score 186 scripts 228 dependents

jdtuck

fdasrvf:Elastic Functional Data Analysis

Performs alignment, PCA, and modeling of multidimensional and unidimensional functions using the square-root velocity framework (Srivastava et al., 2011 <doi:10.48550/arXiv.1103.3817> and Tucker et al., 2014 <DOI:10.1016/j.csda.2012.12.001>). This framework allows for elastic analysis of functional data through phase and amplitude separation.

Maintained by J. Derek Tucker. Last updated 27 days ago.

openblas cpp openmp

5.2 match 11 stars 7.74 score 83 scripts 3 dependents

bioc

BEclear:Correction of batch effects in DNA methylation data

Provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers.

Maintained by Livia Rasp. Last updated 5 months ago.

batcheffect dnamethylation software preprocessing statisticalmethod batch-effects bioconductor-package dna-methylation latent-factor-model methylation missing-data missing-values stochastic-gradient-descent cpp

6.7 match 4 stars 5.90 score 11 scripts

sgdinference-lab

SGDinference:Inference with Stochastic Gradient Descent

Estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms. The inference procedure handles cross-sectional data sequentially: (i) updating the parameter estimate with each incoming "new observation", (ii) aggregating it as a Polyak-Ruppert average, and (iii) computing an asymptotically pivotal statistic for inference through random scaling. The methodology used in the SGDinference package is described in detail in the following papers: (i) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2022) <doi:10.1609/aaai.v36i7.20701> "Fast and robust online inference with stochastic gradient descent via random scaling". (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023) <arXiv:2209.14502> "Fast Inference for Quantile Regression with Tens of Millions of Observations".

Maintained by Youngki Shin. Last updated 1 years ago.

inference sgd stochastic-gradient-descent subgradient openblas cpp

10.6 match 1 stars 3.70 score 4 scripts

bioc

SPIAT:Spatial Image Analysis of Tissues

SPIAT (**Sp**atial **I**mage **A**nalysis of **T**issues) is an R package with a suite of data processing, quality control, visualization and data analysis tools. SPIAT is compatible with data generated from single-cell spatial proteomics platforms (e.g. OPAL, CODEX, MIBI, cellprofiler). SPIAT reads spatial data in the form of X and Y coordinates of cells, marker intensities and cell phenotypes. SPIAT includes six analysis modules that allow visualization, calculation of cell colocalization, categorization of the immune microenvironment relative to tumor areas, analysis of cellular neighborhoods, and the quantification of spatial heterogeneity, providing a comprehensive toolkit for spatial data analysis.

Maintained by Yuzhou Feng. Last updated 18 hours ago.

biomedicalinformatics cellbiology spatial clustering dataimport immunooncology qualitycontrol singlecell software visualization

4.4 match 22 stars 8.59 score 69 scripts

glasgowcompbio

shinyKGode:An Interactive Application for ODE Parameter Inference Using Gradient Matching

An interactive Shiny application to perform fast parameter inference on dynamical systems (described by ordinary differential equations) using gradient matching. Please see the project page for more details.

Maintained by Joe Wandy. Last updated 7 years ago.

differential-equations gradient-matching inference ode sbml

12.5 match 2 stars 3.00 score 2 scripts

tlverse

sl3:Pipelines for Machine Learning and Super Learning

A modern implementation of the Super Learner prediction algorithm, coupled with a general purpose framework for composing arbitrary pipelines for machine learning tasks.

Maintained by Jeremy Coyle. Last updated 4 months ago.

data-science ensemble-learning ensemble-model machine-learning model-selection regression stacking statistics

3.7 match 100 stars 9.94 score 748 scripts 7 dependents

ipd-tools

ipd:Inference on Predicted Data

Performs valid statistical inference on predicted data (IPD) using recent methods, where for a subset of the data, the outcomes have been predicted by an algorithm. Provides a wrapper function with specified defaults for the type of model and method to be used for estimation and inference. Further provides methods for tidying and summarizing results. Salerno et al., (2024) <doi:10.48550/arXiv.2410.09665>.

Maintained by Stephen Salerno. Last updated 2 months ago.

5.9 match 8 stars 6.13 score 5 scripts

stephanehess

apollo:Tools for Choice Model Estimation and Application

Choice models are a widely used technique across numerous scientific disciplines. The Apollo package is a very flexible tool for the estimation and application of choice models in R. Users are able to write their own model functions or use a mix of already available ones. Random heterogeneity, both continuous and discrete and at the level of individuals and choices, can be incorporated for all models. There is support for both standalone models and hybrid model structures. Both classical and Bayesian estimation is available, and multiple discrete continuous models are covered in addition to discrete choice. Multi-threading processing is supported for estimation and a large number of pre and post-estimation routines, including for computing posterior (individual-level) distributions are available. For examples, a manual, and a support forum, visit <http://www.ApolloChoiceModelling.com>. For more information on choice models see Train, K. (2009) <isbn:978-0-521-74738-7> and Hess, S. & Daly, A.J. (2014) <isbn:978-1-781-00314-5> for an overview of the field.

Maintained by Stephane Hess. Last updated 4 days ago.

openblas cpp openmp

7.5 match 1 stars 4.74 score 173 scripts

uclahs-cds

BoutrosLab.plotting.general:Functions to Create Publication-Quality Plots

Contains several plotting functions such as barplots, scatterplots, heatmaps, as well as functions to combine plots and assist in the creation of these plots. These functions will give users great ease of use and customization options in broad use for biomedical applications, as well as general purpose plotting. Each of the functions also provides valid default settings to make plotting data more efficient and producing high quality plots with standard colour schemes simpler. All functions within this package are capable of producing plots that are of the quality to be presented in scientific publications and journals. P'ng et al.; BPG: Seamless, automated and interactive visualization of scientific data; BMC Bioinformatics 2019 <doi:10.1186/s12859-019-2610-2>.

Maintained by Paul Boutros. Last updated 5 months ago.

4.3 match 12 stars 8.36 score 414 scripts 6 dependents

ohdsi

PatientLevelPrediction:Develop Clinical Prediction Models Using the Common Data Model

A user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) <doi:10.1093/jamia/ocy032>.

Maintained by Egill Fridgeirsson. Last updated 9 days ago.

hades openjdk

3.3 match 190 stars 10.85 score 297 scripts

nelson-gon

manymodelr:Build and Tune Several Models

Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of machine learning convenience functions. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using functions from 'caret' with easier to read syntax. Kuhn(2014) <arXiv:1405.6974>.

Maintained by Nelson Gonzabato. Last updated 2 days ago.

analysis-of-variance anova correlation correlation-coefficient generalized-linear-models gradient-boosting-decision-trees knn-classification linear-models linear-regression machine-learning missing-values models r-programming random-forest-algorithm regression-models

6.3 match 2 stars 5.60 score 50 scripts

vegandevs

vegan:Community Ecology Package

Ordination methods, diversity analysis and other functions for community and vegetation ecologists.

Maintained by Jari Oksanen. Last updated 16 days ago.

ecological-modelling ecology ordination fortran openblas

1.8 match 472 stars 19.41 score 15k scripts 440 dependents

alaninglis

vivid:Variable Importance and Variable Interaction Displays

A suite of plots for displaying variable importance and two-way variable interaction jointly. Can also display partial dependence plots laid out in a pairs plot or 'zenplots' style.

Maintained by Alan Inglis. Last updated 8 months ago.

4.6 match 21 stars 7.39 score 39 scripts

r-forge

sandwich:Robust Covariance Matrix Estimators

Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC) covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC) covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators); clustered covariances (one-way and multi-way); panel and panel-corrected covariances; outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>, Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.

Maintained by Achim Zeileis. Last updated 2 months ago.

2.3 match 14.92 score 11k scripts 887 dependents

kassambara

ggpubr:'ggplot2' Based Publication Ready Plots

The 'ggplot2' package is excellent and flexible for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a 'ggplot', the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. 'ggpubr' provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots.

Maintained by Alboukadel Kassambara. Last updated 2 years ago.

2.0 match 1.2k stars 16.68 score 65k scripts 409 dependents

nk027

sanic:Solving Ax = b Nimbly in C++

Routines for solving large systems of linear equations and eigenproblems in R. Direct and iterative solvers from the Eigen C++ library are made available. Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate Gradient, BiCGSTAB). Dense and sparse problems are supported.

Maintained by Nikolas Kuschnig. Last updated 2 years ago.

bicgstab cholesky conjugate-gradient eigen linear-equations solvers cpp

8.0 match 9 stars 4.13 score 1 scripts 1 dependents

cran

OptimModel:Perform Nonlinear Regression Using 'optim' as the Optimization Engine

A wrapper for 'optim' for nonlinear regression problems; see Nocedal J and Write S (2006, ISBN: 978-0387-30303-1). Performs ordinary least squares (OLS), iterative re-weighted least squares (IRWLS), and maximum likelihood (MLE). Also includes the robust outlier detection (ROUT) algorithm; see Motulsky, H and Brown, R (2006)<doi:10.1186/1471-2105-7-123>.

Maintained by Steven Novick. Last updated 1 years ago.

16.5 match 1 stars 2.00 score

david-cortes

nonneg.cg:Non-Negative Conjugate-Gradient Minimizer

Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0), using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in (Li, (2013) <https://www.hindawi.com/journals/jam/2013/986317/abs/>).

Maintained by David Cortes. Last updated 5 years ago.

conjugate-gradient minimize optimization openblas cpp openmp

11.0 match 2 stars 3.00 score 1 scripts

joeguinness

GpGp:Fast Gaussian Process Computation Using Vecchia's Approximation

Functions for fitting and doing predictions with Gaussian process models using Vecchia's (1988) approximation. Package also includes functions for reordering input locations, finding ordered nearest neighbors (with help from 'FNN' package), grouping operations, and conditional simulations. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. The original approximation is due to Vecchia (1988) <http://www.jstor.org/stable/2345768>, and the reordering and grouping methods are from Guinness (2018) <doi:10.1080/00401706.2018.1437476>. Model fitting employs a Fisher scoring algorithm described in Guinness (2019) <doi:10.48550/arXiv.1905.08374>.

Maintained by Joseph Guinness. Last updated 5 months ago.

openblas cpp openmp

5.3 match 10 stars 6.16 score 160 scripts 6 dependents

stan-dev

rstan:R Interface to Stan

User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.

Maintained by Ben Goodrich. Last updated 2 days ago.

bayesian-data-analysis bayesian-inference bayesian-statistics mcmc stan cpp

1.8 match 1.1k stars 18.67 score 14k scripts 279 dependents

dkahle

mpoly:Symbolic Computation and More with Multivariate Polynomials

Symbolic computing with multivariate polynomials in R.

Maintained by David Kahle. Last updated 4 months ago.

5.2 match 12 stars 6.25 score 70 scripts 7 dependents

boost-r

FDboost:Boosting Functional Regression Models

Regression models for functional data, i.e., scalar-on-function, function-on-scalar and function-on-function regression models, are fitted by a component-wise gradient boosting algorithm. For a manual on how to use 'FDboost', see Brockhaus, Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.

Maintained by David Ruegamer. Last updated 3 months ago.

boosting boosting-algorithms function-on-function-regression function-on-scalar-regression machine-learning scalar-on-function-regression variable-selection

4.0 match 17 stars 8.00 score 98 scripts

yrosseel

lavaan:Latent Variable Analysis

Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models.

Maintained by Yves Rosseel. Last updated 8 hours ago.

factor-analysis growth-curve-models latent-variables missing-data multilevel-models multivariate-analysis path-analysis psychometrics statistical-modeling structural-equation-modeling

1.9 match 453 stars 16.83 score 8.4k scripts 216 dependents

jarioksa

gravy:Gradient Analysis of Vegetation

Experimental tools for gradient analysis of community data. Most of the functionality is now included in package 'eHOF', but there are still some divergent features.

Maintained by Jari Oksanen. Last updated 10 months ago.

15.8 match 2 stars 2.00 score

cran

bst:Gradient Boosting

Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.

Maintained by Zhu Wang. Last updated 2 years ago.

7.6 match 4.17 score 5 dependents

bioc

smoppix:Analyze Single Molecule Spatial Omics Data Using the Probabilistic Index

Test for univariate and bivariate spatial patterns in spatial omics data with single-molecule resolution. The tests implemented allow for analysis of nested designs and are automatically calibrated to different biological specimens. Tests for aggregation, colocalization, gradients and vicinity to cell edge or centroid are provided.

Maintained by Stijn Hawinkel. Last updated 28 days ago.

transcriptomics spatial singlecell cpp

6.0 match 1 stars 5.18 score 4 scripts

dankelley

oce:Analysis of Oceanographic Data

Supports the analysis of Oceanographic data, including 'ADCP' measurements, measurements made with 'argo' floats, 'CTD' measurements, sectional data, sea-level time series, coastline and topographic data, etc. Provides specialized functions for calculating seawater properties such as potential temperature in either the 'UNESCO' or 'TEOS-10' equation of state. Produces graphical displays that conform to the conventions of the Oceanographic literature. This package is discussed extensively by Kelley (2018) "Oceanographic Analysis with R" <doi:10.1007/978-1-4939-8844-0>.

Maintained by Dan Kelley. Last updated 2 days ago.

oceanography fortran cpp

2.0 match 146 stars 15.42 score 4.2k scripts 18 dependents

bioc

tigre:Transcription factor Inference through Gaussian process Reconstruction of Expression

The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF.

Maintained by Antti Honkela. Last updated 5 months ago.

microarray timecourse geneexpression transcription generegulation networkinference bayesian

7.0 match 4.38 score 6 scripts

jameslamb

lightgbm:Light Gradient Boosting Machine

Tree based algorithms can be improved by introducing boosting frameworks. 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>. This package offers an R interface to work with it. It is designed to be distributed and efficient with the following advantages: 1. Faster training speed and higher efficiency. 2. Lower memory usage. 3. Better accuracy. 4. Parallel learning supported. 5. Capable of handling large-scale data. In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions. Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines.

Maintained by James Lamb. Last updated 1 months ago.

cpp openmp

3.6 match 1 stars 8.47 score 1.6k scripts 6 dependents

konrad1991

dfdr:Automatic Differentiation of Simple Functions

Implementation of automatically computing derivatives of functions (see Mailund Thomas (2017) <doi:10.1007/978-1-4842-2881-4>). Moreover, calculating gradients, Hessian and Jacobian matrices is possible.

Maintained by Konrad Krämer. Last updated 26 days ago.

5.7 match 7 stars 5.27 score 11 scripts 2 dependents

talgalili

dendextend:Extending 'dendrogram' Functionality in R

Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another.

Maintained by Tal Galili. Last updated 2 months ago.

1.8 match 154 stars 17.02 score 6.0k scripts 164 dependents

bmcclintock

momentuHMM:Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models

Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, discrete- or continuous-time HMMs, continuous- or discrete-space movement models, approximate Langevin diffusion models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.

Maintained by Brett McClintock. Last updated 1 months ago.

openblas cpp

3.5 match 43 stars 8.47 score 162 scripts

dashaub

DidacticBoost:A Simple Implementation and Demonstration of Gradient Boosting

A basic, clear implementation of tree-based gradient boosting designed to illustrate the core operation of boosting models. Tuning parameters (such as stochastic subsampling, modified learning rate, or regularization) are not implemented. The only adjustable parameter is the number of training rounds. If you are looking for a high performance boosting implementation with tuning parameters, consider the 'xgboost' package.

Maintained by David Shaub. Last updated 9 years ago.

gradient-boosting

11.0 match 2.70 score 1 scripts

civisanalytics

civis:R Client for the 'Civis Platform API'

A convenient interface for making requests directly to the 'Civis Platform API' <https://www.civisanalytics.com/platform/>. Full documentation available 'here' <https://civisanalytics.github.io/civis-r/>.

Maintained by Peter Cooman. Last updated 2 months ago.

3.8 match 16 stars 7.84 score 144 scripts

jrnold

ggthemes:Extra Themes, Scales and Geoms for 'ggplot2'

Some extra themes, geoms, and scales for 'ggplot2'. Provides 'ggplot2' themes and scales that replicate the look of plots by Edward Tufte, Stephen Few, 'Fivethirtyeight', 'The Economist', 'Stata', 'Excel', and 'The Wall Street Journal', among others. Provides 'geoms' for Tufte's box plot and range frame.

Maintained by Jeffrey B. Arnold. Last updated 1 years ago.

data-visualisation ggplot2 ggplot2-themes plot plotting theme visualization

1.8 match 1.3k stars 16.17 score 40k scripts 102 dependents

cardiomoon

ztable:Zebra-Striped Tables in LaTeX and HTML Formats

Makes zebra-striped tables (tables with alternating row colors) in LaTeX and HTML formats easily from a data.frame, matrix, lm, aov, anova, glm, coxph, nls, fitdistr, mytable and cbind.mytable objects.

Maintained by Keon-Woong Moon. Last updated 2 years ago.

3.6 match 21 stars 7.90 score 212 scripts 2 dependents

anthonychristidis

CPGLIB:Competing Proximal Gradients Library

Functions to generate ensembles of generalized linear models using competing proximal gradients. The optimal sparsity and diversity tuning parameters are selected via an alternating grid search.

Maintained by Anthony Christidis. Last updated 7 months ago.

openblas cpp openmp

10.4 match 2.70 score 2 scripts

tickingclock1992

RIdeogram:Drawing SVG Graphics to Visualize and Map Genome-Wide Data on Idiograms

For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. RIdeogram was developed to visualize and map whole-genome data on idiograms with no restriction of species.

Maintained by Zhaodong Hao. Last updated 4 years ago.

3.5 match 169 stars 7.97 score 62 scripts

wilkelab

ggridges:Ridgeline Plots in 'ggplot2'

Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.

Maintained by Claus O. Wilke. Last updated 3 months ago.

1.7 match 418 stars 16.71 score 14k scripts 285 dependents

kisungyou

Rlinsolve:Iterative Solvers for (Sparse) Linear System of Equations

Solving a system of linear equations is one of the most fundamental computational problems for many fields of mathematical studies, such as regression problems from statistics or numerical partial differential equations. We provide basic stationary iterative solvers such as Jacobi, Gauss-Seidel, Successive Over-Relaxation and SSOR methods. Nonstationary, also known as Krylov subspace methods are also provided. Sparse matrix computation is also supported in that solving large and sparse linear systems can be manageable using 'Matrix' package along with 'RcppArmadillo'. For a more detailed description, see a book by Saad (2003) <doi:10.1137/1.9780898718003>.

Maintained by Kisung You. Last updated 2 years ago.

openblas cpp openmp

7.8 match 4 stars 3.56 score 30 scripts 1 dependents

martin3141

spant:MR Spectroscopy Analysis Tools

Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.

Maintained by Martin Wilson. Last updated 30 days ago.

brain mri mrs mrshub spectroscopy fortran

3.2 match 25 stars 8.52 score 81 scripts

kasselhingee

scorematchingad:Score Matching Estimation by Automatic Differentiation

Hyvärinen's score matching (Hyvärinen, 2005) <https://jmlr.org/papers/v6/hyvarinen05a.html> is a useful estimation technique when the normalising constant for a probability distribution is difficult to compute. This package implements score matching estimators using automatic differentiation in the 'CppAD' library <https://github.com/coin-or/CppAD> and is designed for quickly implementing score matching estimators for new models. Also available is general robustification (Windham, 1995) <https://www.jstor.org/stable/2346159>. Already in the package are estimators for directional distributions (Mardia, Kent and Laha, 2016) <doi:10.48550/arXiv.1604.08470> and the flexible Polynomially-Tilted Pairwise Interaction model for compositional data. The latter estimators perform well when there are zeros in the compositions (Scealy and Wood, 2023) <doi:10.1080/01621459.2021.2016422>, even many zeros (Scealy, Hingee, Kent, and Wood, 2024) <doi:10.1007/s11222-024-10412-w>. A partial interface to CppAD's ADFun objects is also available.

Maintained by Kassel Liam Hingee. Last updated 2 months ago.

automatic-differentiation score-matching statistical-inference cpp

6.9 match 3.98 score 1 scripts

sparklyr

sparklyr:R Interface to Apache Spark

R interface to Apache Spark, a fast and general engine for big data processing, see <https://spark.apache.org/>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.

Maintained by Edgar Ruiz. Last updated 10 days ago.

apache-spark distributed dplyr ide livy machine-learning remote-clusters spark sparklyr

1.8 match 959 stars 15.16 score 4.0k scripts 21 dependents

dkahle

TITAN2:Threshold Indicator Taxa Analysis

Uses indicator species scores across binary partitions of a sample set to detect congruence in taxon-specific changes of abundance and occurrence frequency along an environmental gradient as evidence of an ecological community threshold. Relevant references include Baker and King (2010) <doi:10.1111/j.2041-210X.2009.00007.x>, King and Baker (2010) <doi:10.1899/09-144.1>, and Baker and King (2013) <doi:10.1899/12-142.1>.

Maintained by David Kahle. Last updated 1 years ago.

4.1 match 13 stars 6.59 score 30 scripts

zeehio

condformat:Conditional Formatting in Data Frames

Apply and visualize conditional formatting to data frames in R. It renders a data frame with cells formatted according to criteria defined by rules, using a tidy evaluation syntax. The table is printed either opening a web browser or within the 'RStudio' viewer if available. The conditional formatting rules allow to highlight cells matching a condition or add a gradient background to a given column. This package supports both 'HTML' and 'LaTeX' outputs in 'knitr' reports, and exporting to an 'xlsx' file.

Maintained by Sergio Oller Moreno. Last updated 1 years ago.

formatting html latex table visualisation

4.1 match 25 stars 6.53 score 91 scripts 1 dependents

tidymodels

dials:Tools for Creating Tuning Parameter Values

Many models contain tuning parameters (i.e. parameters that cannot be directly estimated from the data). These tools can be used to define objects for creating, simulating, or validating values for such parameters.

Maintained by Hannah Frick. Last updated 30 days ago.

1.9 match 114 stars 14.31 score 426 scripts 52 dependents

pabrod

waydown:Computation of Approximate Potentials for Weakly Non-Gradient Fields

Computation of approximate potentials for both gradient and non gradient fields. It is known from physics that only gradient fields, also known as conservative, have a well defined potential function. Here we present an algorithm, based on the classical Helmholtz decomposition, to obtain an approximate potential function for non gradient fields. More information in Rodríguez-Sánchez (2020) <doi:10.1371/journal.pcbi.1007788>.

Maintained by Pablo Rodríguez-Sánchez. Last updated 1 years ago.

6.4 match 3 stars 4.18 score 6 scripts

cdmuir

tealeaves:Solve for Leaf Temperature Using Energy Balance

Implements models of leaf temperature using energy balance. It uses units to ensure that parameters are properly specified and transformed before calculations. It allows separate lower and upper surface conductances to heat and water vapour, so sensible and latent heat loss are calculated for each surface separately as in Foster and Smith (1986) <doi:10.1111/j.1365-3040.1986.tb02108.x>. It's straightforward to model leaf temperature over environmental gradients such as light, air temperature, humidity, and wind. It can also model leaf temperature over trait gradients such as leaf size or stomatal conductance. Other references are Monteith and Unsworth (2013, ISBN:9780123869104), Nobel (2009, ISBN:9780123741431), and Okajima et al. (2012) <doi:10.1007/s11284-011-0905-5>.

Maintained by Chris Muir. Last updated 1 years ago.

4.0 match 11 stars 6.69 score 49 scripts 1 dependents

teunbrand

ggh4x:Hacks for 'ggplot2'

A 'ggplot2' extension that does a variety of little helpful things. The package extends 'ggplot2' facets through customisation, by setting individual scales per panel, resizing panels and providing nested facets. Also allows multiple colour and fill scales per plot. Also hosts a smaller collection of stats, geoms and axis guides.

Maintained by Teun van den Brand. Last updated 3 months ago.

ggplot-extension ggplot2

1.9 match 616 stars 13.98 score 4.4k scripts 20 dependents

flxzimmer

irtpwr:Power Analysis for IRT Models Using the Wald, LR, Score, and Gradient Statistics

Implementation of analytical and sampling-based power analyses for the Wald, likelihood ratio (LR), score, and gradient tests. Can be applied to item response theory (IRT) models that are fitted using marginal maximum likelihood estimation. The methods are described in our paper (Zimmer et al. (2022) <doi:10.1007/s11336-022-09883-5>).

Maintained by Felix Zimmer. Last updated 1 years ago.

6.0 match 1 stars 4.30 score 9 scripts

roelandkindt

BiodiversityR:Package for Community Ecology and Suitability Analysis

Graphical User Interface (via the R-Commander) and utility functions (often based on the vegan package) for statistical analysis of biodiversity and ecological communities, including species accumulation curves, diversity indices, Renyi profiles, GLMs for analysis of species abundance and presence-absence, distance matrices, Mantel tests, and cluster, constrained and unconstrained ordination analysis. A book on biodiversity and community ecology analysis is available for free download from the website. In 2012, methods for (ensemble) suitability modelling and mapping were expanded in the package.

Maintained by Roeland Kindt. Last updated 2 months ago.

3.5 match 16 stars 7.42 score 390 scripts 2 dependents

cvxgrp

CVXR:Disciplined Convex Optimization

An object-oriented modeling language for disciplined convex programming (DCP) as described in Fu, Narasimhan, and Boyd (2020, <doi:10.18637/jss.v094.i14>). It allows the user to formulate convex optimization problems in a natural way following mathematical convention and DCP rules. The system analyzes the problem, verifies its convexity, converts it into a canonical form, and hands it off to an appropriate solver to obtain the solution. Interfaces to solvers on CRAN and elsewhere are provided, both commercial and open source.

Maintained by Anqi Fu. Last updated 4 months ago.

cpp

2.0 match 207 stars 12.89 score 768 scripts 51 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.

3.5 match 5 stars 7.17 score 388 scripts 23 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

3.3 match 10 stars 7.75 score 108 scripts

cran

cPCG:Efficient and Customized Preconditioned Conjugate Gradient Method for Solving System of Linear Equations

Solves system of linear equations using (preconditioned) conjugate gradient algorithm, with improved efficiency using Armadillo templated 'C++' linear algebra library, and flexibility for user-specified preconditioning method. Please check <https://github.com/styvon/cPCG> for latest updates.

Maintained by Yongwen Zhuang. Last updated 6 years ago.

openblas cpp

11.2 match 2.28 score 19 scripts

roustant

DiceKriging:Kriging Methods for Computer Experiments

Estimation, validation and prediction of kriging models. Important functions : km, print.km, plot.km, predict.km.

Maintained by Olivier Roustant. Last updated 4 years ago.

3.6 match 4 stars 6.99 score 526 scripts 37 dependents

runehaubo

ordinal:Regression Models for Ordinal Data

Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points/intercepts). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence.

Maintained by Rune Haubo Bojesen Christensen. Last updated 3 months ago.

2.0 match 38 stars 12.41 score 1.3k scripts 178 dependents

ropensci

landscapetools:Landscape Utility Toolbox

Provides utility functions for some of the less-glamorous tasks involved in landscape analysis. It includes functions to coerce raster data to the common tibble format and vice versa, it helps with flexible reclassification tasks of raster data and it provides a function to merge multiple raster. Furthermore, 'landscapetools' helps landscape scientists to visualize their data by providing optional themes and utility functions to plot single landscapes, rasterstacks, -bricks and lists of raster.

Maintained by Marco Sciaini. Last updated 2 years ago.

landscape landscape-ecology raster visualization workflow

3.8 match 46 stars 6.60 score 191 scripts

diegommcc

SpatialDDLS:Deconvolution of Spatial Transcriptomics Data Based on Neural Networks

Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.

Maintained by Diego Mañanes. Last updated 5 months ago.

deconvolution deep-learning neural-network spatial-transcriptomics

4.9 match 5 stars 5.00 score 1 scripts

luislaum

grec:Gradient-Based Recognition of Spatial Patterns in Environmental Data

Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.

Maintained by Wencheng Lau-Medrano. Last updated 1 years ago.

6.9 match 7 stars 3.54 score 6 scripts

marcmenem

ggshadow:Shadow and Glow Geoms for 'ggplot2'

A collection of Geoms for R's 'ggplot2' library. GeomShadowLine, GeomShadowPath, GeomShadowPoint, and GeomShadowStep support drawing a shadow below lines or around points to make busy plots more aesthetically pleasing. GeomGlowLine, GeomGlowPath, GeomGlowPoint, and GeomGlowStep support adding neon glow around lines or points to get a steampunk style.

Maintained by Marc Menem. Last updated 1 years ago.

data-visualisation ggplot2 vizualisation

3.9 match 60 stars 6.27 score 62 scripts

r-forge

mlt:Most Likely Transformations

Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020) <DOI:10.18637/jss.v092.i01>. Shift-scale (Siegfried et al, 2023, <DOI:10.1080/00031305.2023.2203177>) and multivariate (Klein et al, 2022, <DOI:10.1111/sjos.12501>) transformation models are part of this package. A package vignette is available from <DOI:10.32614/CRAN.package.mlt.docreg> and more convenient user interfaces to many models from <DOI:10.32614/CRAN.package.tram>.

Maintained by Torsten Hothorn. Last updated 4 days ago.

3.3 match 7.31 score 41 scripts 10 dependents

rishvish

DImodelsVis:Visualising and Interpreting Statistical Models Fit to Compositional Data

Statistical models fit to compositional data are often difficult to interpret due to the sum to 1 constraint on data variables. 'DImodelsVis' provides novel visualisations tools to aid with the interpretation of models fit to compositional data. All visualisations in the package are created using the 'ggplot2' plotting framework and can be extended like every other 'ggplot' object.

Maintained by Rishabh Vishwakarma. Last updated 6 months ago.

6.5 match 3.70 score 7 scripts

laurabruckman

netSEM:Network Structural Equation Modeling

The network structural equation modeling conducts a network statistical analysis on a data frame of coincident observations of multiple continuous variables [1]. It builds a pathway model by exploring a pool of domain knowledge guided candidate statistical relationships between each of the variable pairs, selecting the 'best fit' on the basis of a specific criteria such as adjusted r-squared value. This material is based upon work supported by the U.S. National Science Foundation Award EEC-2052776 and EEC-2052662 for the MDS-Rely IUCRC Center, under the NSF Solicitation: NSF 20-570 Industry-University Cooperative Research Centers Program [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) <doi:10.1109/ACCESS.2013.2267611>.

Maintained by Laura S. Bruckman. Last updated 2 years ago.

6.5 match 3.72 score 13 scripts

kaz-yos

regmedint:Regression-Based Causal Mediation Analysis with Interaction and Effect Modification Terms

This is an extension of the regression-based causal mediation analysis first proposed by Valeri and VanderWeele (2013) <doi:10.1037/a0031034> and Valeri and VanderWeele (2015) <doi:10.1097/EDE.0000000000000253>). It supports including effect measure modification by covariates(treatment-covariate and mediator-covariate product terms in mediator and outcome regression models) as proposed by Li et al (2023) <doi:10.1097/EDE.0000000000001643>. It also accommodates the original 'SAS' macro and 'PROC CAUSALMED' procedure in 'SAS' when there is no effect measure modification. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model.

Maintained by Yi Li. Last updated 1 years ago.

causal-inference mediation-analysis

3.5 match 29 stars 6.84 score 40 scripts

objornstad

epimdr:Functions and Data for "Epidemics: Models and Data in R"

Functions, data sets and shiny apps for "Epidemics: Models and Data in R" by Ottar N. Bjornstad (ISBN 978-3-319-97487-3) <https://www.springer.com/gp/book/9783319974866>. The package contains functions to study the S(E)IR model, spatial and age-structured SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the coursera Epidemics Massive Online Open Course <https://www.coursera.org/learn/epidemics>.

Maintained by Ottar N. Bjornstad. Last updated 5 years ago.

14.3 match 1.65 score 45 scripts

ericgiunta

Colossus:"Risk Model Regression and Analysis with Complex Non-Linear Models"

Performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards <https://en.wikipedia.org/wiki/Proportional_hazards_model>, Poisson <https://en.wikipedia.org/wiki/Poisson_regression>, and Fine-Gray competing risks <https://www.publichealth.columbia.edu/research/population-health-methods/competing-risk-analysis> regression are supported. This work was sponsored by NASA Grant 80NSSC19M0161 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109.

Maintained by Eric Giunta. Last updated 3 days ago.

cpp openmp

3.3 match 1 stars 7.06 score 36 scripts

bioc

Cardinal:A mass spectrometry imaging toolbox for statistical analysis

Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification.

Maintained by Kylie Ariel Bemis. Last updated 3 months ago.

software infrastructure proteomics lipidomics massspectrometry imagingmassspectrometry immunooncology normalization clustering classification regression

2.3 match 47 stars 10.34 score 200 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

3.6 match 7 stars 6.52 score 12 scripts 10 dependents

jamesramsay5

fda:Functional Data Analysis

These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer and in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. Matlab versions are available by ftp from <https://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>.

Maintained by James Ramsay. Last updated 4 months ago.

1.9 match 3 stars 12.29 score 2.0k scripts 143 dependents

hneth

unikn:Graphical Elements of the University of Konstanz's Corporate Design

Define and use graphical elements of corporate design manuals in R. The 'unikn' package provides color functions (by defining dedicated colors and color palettes, and commands for finding, changing, viewing, and using them) and styled text elements (e.g., for marking, underlining, or plotting colored titles). The pre-defined range of colors and text decoration functions is based on the corporate design of the University of Konstanz <https://www.uni-konstanz.de/>, but can be adapted and extended for other purposes or institutions.

Maintained by Hansjoerg Neth. Last updated 3 months ago.

branding color color-palette colorscheme corporate-design palette text-decoration university-colors visual-identity

2.6 match 39 stars 8.82 score 156 scripts 2 dependents

sciurus365

simlandr:Simulation-Based Landscape Construction for Dynamical Systems

A toolbox for constructing potential landscapes for dynamical systems using Monte Carlo simulation. The method is based on the potential landscape definition by Wang et al. (2008) <doi:10.1073/pnas.0800579105> (also see Zhou & Li, 2016 <doi:10.1063/1.4943096> for further mathematical discussions) and can be used for a large variety of models.

Maintained by Jingmeng Cui. Last updated 1 months ago.

research-tool

3.5 match 6 stars 6.41 score 12 scripts 2 dependents

pletschm

aldvmm:Adjusted Limited Dependent Variable Mixture Models

The goal of the package 'aldvmm' is to fit adjusted limited dependent variable mixture models of health state utilities. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package 'aldvmm' uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) <doi:10.1177/1536867X1501500307> using normal component distributions and a multinomial logit model of probabilities of component membership.

Maintained by Mark Pletscher. Last updated 1 years ago.

clinical-trials cost-effectiveness eq5d finite-mixture health-economics hta hui limited-dependent-variable mapping mixture-model patient-reported-outcomes quality-of-life utilities

5.1 match 5 stars 4.40 score 2 scripts

bjoelle

FossilSim:Simulation and Plots for Fossil and Taxonomy Data

Simulating and plotting taxonomy and fossil data on phylogenetic trees under mechanistic models of speciation, preservation and sampling.

Maintained by Joelle Barido-Sottani. Last updated 6 months ago.

4.3 match 1 stars 5.24 score 65 scripts 1 dependents

radiant-rstats

radiant.model:Model Menu for Radiant: Business Analytics using R and Shiny

The Radiant Model menu includes interfaces for linear and logistic regression, naive Bayes, neural networks, classification and regression trees, model evaluation, collaborative filtering, decision analysis, and simulation. The application extends the functionality in 'radiant.data'.

Maintained by Vincent Nijs. Last updated 5 months ago.

3.6 match 19 stars 6.18 score 80 scripts 2 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 4 months ago.

cpp

3.3 match 12 stars 6.82 score 85 scripts 1 dependents

e-sensing

sits:Satellite Image Time Series Analysis for Earth Observation Data Cubes

An end-to-end toolkit for land use and land cover classification using big Earth observation data, based on machine learning methods applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>. Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, Brazil Data Cube, Copernicus Data Space Environment (CDSE), Digital Earth Africa, Digital Earth Australia, NASA HLS using the Spatio-temporal Asset Catalog (STAC) protocol (<https://stacspec.org/>) and the 'gdalcubes' R package developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Includes functions for quality assessment of training samples using self-organized maps as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>. Includes methods to reduce training samples imbalance proposed by Chawla et al (2002) <doi:10.1613/jair.953>. Provides machine learning methods including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, and temporal attention encoders by Garnot and Landrieu (2020) <doi:10.48550/arXiv.2007.00586>. Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference as described by Camara et al (2024) <doi:10.3390/rs16234572>, and methods for active learning and uncertainty assessment. Supports region-based time series analysis using package supercells <https://jakubnowosad.com/supercells/>. Enables best practices for estimating area and assessing accuracy of land change as recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.

Maintained by Gilberto Camara. Last updated 1 months ago.

big-earth-data cbers earth-observation eo-datacubes geospatial image-time-series land-cover-classification landsat planetary-computer r-spatial remote-sensing rspatial satellite-image-time-series satellite-imagery sentinel-2 stac-api stac-catalog cpp

2.3 match 494 stars 9.50 score 384 scripts

anthonychristidis

PSGD:Projected Subset Gradient Descent

Functions to generate ensembles of generalized linear models using a greedy projected subset gradient descent algorithm. The sparsity and diversity tuning parameters are selected by cross-validation.

Maintained by Anthony Christidis. Last updated 3 months ago.

openblas cpp openmp

7.3 match 3.00 score 2 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

1.8 match 208 stars 12.08 score 2.5k scripts 29 dependents

stanfill

rotations:Working with Rotation Data

Tools for working with rotational data, including simulation from the most commonly used distributions on SO(3), methods for different Bayes, mean and median type estimators for the central orientation of a sample, confidence/credible regions for the central orientation based on those estimators and a novel visualization technique for rotation data. Most recently, functions to identify potentially discordant (outlying) values have been added. References: Bingham, Melissa A. and Nordman, Dan J. and Vardeman, Steve B. (2009), Bingham, Melissa A and Vardeman, Stephen B and Nordman, Daniel J (2009), Bingham, Melissa A and Nordman, Daniel J and Vardeman, Stephen B (2010), Leon, C.A. and Masse, J.C. and Rivest, L.P. (2006), Hartley, R and Aftab, K and Trumpf, J. (2011), Stanfill, Bryan and Genschel, Ulrike and Hofmann, Heike (2013), Maonton, Jonathan (2004), Mardia, KV and Jupp, PE (2000, ISBN:9780471953333), Rancourt, D. and Rivest, L.P. and Asselin, J. (2000), Chang, Ted and Rivest, Louis-Paul (2001), Fisher, Nicholas I. (1996, ISBN:0521568900).

Maintained by Bryan Stanfill. Last updated 1 years ago.

openblas cpp

4.1 match 5.23 score 57 scripts 1 dependents

ucl

rmcmc:Robust Markov Chain Monte Carlo Methods

Functions for simulating Markov chains using the Barker proposal to compute Markov chain Monte Carlo (MCMC) estimates of expectations with respect to a target distribution on a real-valued vector space. The Barker proposal, described in Livingstone and Zanella (2022) <doi:10.1111/rssb.12482>, is a gradient-based MCMC algorithm inspired by the Barker accept-reject rule. It combines the robustness of simpler MCMC schemes, such as random-walk Metropolis, with the efficiency of gradient-based methods, such as the Metropolis adjusted Langevin algorithm. The key function provided by the package is sample_chain(), which allows sampling a Markov chain with a specified target distribution as its stationary distribution. The chain is sampled by generating proposals and accepting or rejecting them using a Metropolis-Hasting acceptance rule. During an initial warm-up stage, the parameters of the proposal distribution can be adapted, with adapters available to both: tune the scale of the proposals by coercing the average acceptance rate to a target value; tune the shape of the proposals to match covariance estimates under the target distribution. As well as the default Barker proposal, the package also provides implementations of alternative proposal distributions, such as (Gaussian) random walk and Langevin proposals. Optionally, if 'BridgeStan's R interface <https://roualdes.github.io/bridgestan/latest/languages/r.html>, available on GitHub <https://github.com/roualdes/bridgestan>, is installed, then 'BridgeStan' can be used to specify the target distribution to sample from.

Maintained by Matthew M. Graham. Last updated 14 days ago.

approximate-inference mcmc

3.7 match 5 stars 5.85 score 8 scripts

wraff

wrMisc:Analyze Experimental High-Throughput (Omics) Data

The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples. Many times large experimental datasets need some additional filtering, adequate functions are provided. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too. Batch reading (or writing) of sets of files and combining data to arrays is supported, too.

Maintained by Wolfgang Raffelsberger. Last updated 7 months ago.

4.8 match 4.44 score 33 scripts 4 dependents

bioxgeo

geodiv:Methods for Calculating Gradient Surface Metrics

Methods for calculating gradient surface metrics for continuous analysis of landscape features.

Maintained by Annie C. Smith. Last updated 1 years ago.

cpp

3.6 match 11 stars 5.88 score 23 scripts 1 dependents

gforge

Gmisc:Descriptive Statistics, Transition Plots, and More

Tools for making the descriptive "Table 1" used in medical articles, a transition plot for showing changes between categories (also known as a Sankey diagram), flow charts by extending the grid package, a method for variable selection based on the SVD, Bézier lines with arrows complementing the ones in the 'grid' package, and more.

Maintained by Max Gordon. Last updated 2 years ago.

cpp

2.0 match 50 stars 10.40 score 233 scripts 2 dependents

ericgilleland

ismev:An Introduction to Statistical Modeling of Extreme Values

Functions to support the computations carried out in `An Introduction to Statistical Modeling of Extreme Values' by Stuart Coles. The functions may be divided into the following groups; maxima/minima, order statistics, peaks over thresholds and point processes.

Maintained by Eric Gilleland. Last updated 7 years ago.

4.0 match 1 stars 5.19 score 326 scripts 13 dependents

mclements

rstpm2:Smooth Survival Models, Including Generalized Survival Models

R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects <doi:10.1002/sim.7451>, and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.

Maintained by Mark Clements. Last updated 5 months ago.

fortran openblas cpp

1.9 match 28 stars 11.01 score 137 scripts 50 dependents

atarkhan

bigSurvSGD:Big Survival Analysis Using Stochastic Gradient Descent

Fits Cox Model via stochastic gradient descent (SGD). This implementation avoids computational instability of the standard Cox Model when dealing large datasets. Furthermore, it scales up with large datasets that do not fit the memory. It also handles large sparse datasets using Proximal stochastic gradient descent algorithm.

Maintained by Aliasghar Tarkhan. Last updated 5 years ago.

cpp

7.1 match 7 stars 2.85 score 1 scripts

robinhankin

hyper2:The Hyperdirichlet Distribution, Mark 2

A suite of routines for the hyperdirichlet distribution and reified Bradley-Terry; supersedes the 'hyperdirichlet' package; uses 'disordR' discipline <doi:10.48550/ARXIV.2210.03856>. To cite in publications please use Hankin 2017 <doi:10.32614/rj-2017-061>, and for Generalized Plackett-Luce likelihoods use Hankin 2024 <doi:10.18637/jss.v109.i08>.

Maintained by Robin K. S. Hankin. Last updated 4 days ago.

cpp

3.3 match 5 stars 6.01 score 38 scripts 1 dependents

uupharmacometrics

xpose:Diagnostics for Pharmacometric Models

Diagnostics for non-linear mixed-effects (population) models from 'NONMEM' <https://www.iconplc.com/solutions/technologies/nonmem/>. 'xpose' facilitates data import, creation of numerical run summary and provide 'ggplot2'-based graphics for data exploration and model diagnostics.

Maintained by Benjamin Guiastrennec. Last updated 2 months ago.

diagnostics ggplot2 nonmem pharmacometrics xpose

1.8 match 62 stars 11.02 score 183 scripts 6 dependents

mlverse

luz:Higher Level 'API' for 'torch'

A high level interface for 'torch' providing utilities to reduce the the amount of code needed for common tasks, abstract away torch details and make the same code work on both the 'CPU' and 'GPU'. It's flexible enough to support expressing a large range of models. It's heavily inspired by 'fastai' by Howard et al. (2020) <arXiv:2002.04688>, 'Keras' by Chollet et al. (2015) and 'PyTorch Lightning' by Falcon et al. (2019) <doi:10.5281/zenodo.3828935>.

Maintained by Daniel Falbel. Last updated 6 months ago.

2.0 match 89 stars 9.86 score 318 scripts 4 dependents

crj32

MLeval:Machine Learning Model Evaluation

Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. 'MLeval' accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. 'MLeval' produces a range of evaluation metrics with confidence intervals.

Maintained by Christopher R John. Last updated 5 years ago.

3.4 match 6 stars 5.71 score 144 scripts

statdivlab

corncob:Count Regression for Correlated Observations with the Beta-Binomial

Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.

Maintained by Amy D Willis. Last updated 6 months ago.

2.0 match 105 stars 9.64 score 248 scripts 1 dependents

trinker

qdap:Bridging the Gap Between Qualitative Data and Quantitative Analysis

Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R packages that undertake higher level analysis and visualization of text. This affords the user a more efficient and targeted analysis. 'qdap' is designed for transcript analysis, however, many functions are applicable to other areas of Text Mining/ Natural Language Processing.

Maintained by Tyler Rinker. Last updated 4 years ago.

qdap quantitative-discourse-analysis text-analysis text-mining text-plotting openjdk

2.0 match 176 stars 9.61 score 1.3k scripts 3 dependents

vthorrf

optimg:General-purpose Gradient-based Optimization

This package is a general purpose tool for helping users to implement gradient descent methods for function optimization. Currently, the Steepest 2-Groups Gradient Descent and the Adaptive Moment Estimation (Adam) are the methods implemented. Other methods will be implemented in the future.

Maintained by Vithor Rosa Franco. Last updated 2 years ago.

5.6 match 1 stars 3.41 score 17 scripts 1 dependents

bioc

CMA:Synthesis of microarray-based classification

This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment.

Maintained by Roman Hornung. Last updated 5 months ago.

classification decisiontree

3.8 match 5.09 score 61 scripts

marcinkosinski

coxphSGD:Stochastic Gradient Descent log-Likelihood Estimation in Cox Proportional Hazards Model

Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.

Maintained by Marcin Kosinski. Last updated 8 years ago.

4.9 match 7 stars 3.85 score 4 scripts

grunwaldlab

poppr:Genetic Analysis of Populations with Mixed Reproduction

Population genetic analyses for hierarchical analysis of partially clonal populations built upon the architecture of the 'adegenet' package. Originally described in Kamvar, Tabima, and Grünwald (2014) <doi:10.7717/peerj.281> with version 2.0 described in Kamvar, Brooks, and Grünwald (2015) <doi:10.3389/fgene.2015.00208>.

Maintained by Zhian N. Kamvar. Last updated 10 months ago.

clonality genetic-analysis genetic-distances minimum-spanning-networks multilocus-genotypes multilocus-lineages population-genetics populations openmp

1.8 match 69 stars 10.84 score 672 scripts

ktabelow

dti:Analysis of Diffusion Weighted Imaging (DWI) Data

Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging modality, that measures diffusion of water in tissues like the human brain. The package contains R-functions to process diffusion-weighted data. The functionality includes diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models, several methods for structural adaptive smoothing including POAS and msPOAS, and a streamline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D.

Maintained by Karsten Tabelow. Last updated 6 months ago.

fortran openblas cpp

8.6 match 2.20 score 16 scripts

davezes

mactivate:Multiplicative Activation

Provides methods and classes for adding m-activation ("multiplicative activation") layers to MLR or multivariate logistic regression models. M-activation layers created in this library detect and add input interaction (polynomial) effects into a predictive model. M-activation can detect high-order interactions -- a traditionally non-trivial challenge. Details concerning application, methodology, and relevant survey literature can be found in this library's vignette, "About."

Maintained by Dave Zes. Last updated 4 years ago.

7.0 match 2.68 score 12 scripts

tnagler

VineCopula:Statistical Inference of Vine Copulas

Provides tools for the statistical analysis of regular vine copula models, see Aas et al. (2009) <doi:10.1016/j.insmatheco.2007.02.001> and Dissman et al. (2013) <doi:10.1016/j.csda.2012.08.010>. The package includes tools for parameter estimation, model selection, simulation, goodness-of-fit tests, and visualization. Tools for estimation, selection and exploratory data analysis of bivariate copula models are also provided.

Maintained by Thomas Nagler. Last updated 24 days ago.

copula estimation statistics vine

1.7 match 91 stars 10.99 score 362 scripts 23 dependents

jillbo1000

EZtune:Tunes AdaBoost, Elastic Net, Support Vector Machines, and Gradient Boosting Machines

Contains two functions that are intended to make tuning supervised learning methods easy. The eztune function uses a genetic algorithm or Hooke-Jeeves optimizer to find the best set of tuning parameters. The user can choose the optimizer, the learning method, and if optimization will be based on accuracy obtained through validation error, cross validation, or resubstitution. The function eztune_cv will compute a cross validated error rate. The purpose of eztune_cv is to provide a cross validated accuracy or MSE when resubstitution or validation data are used for optimization because error measures from both approaches can be misleading.

Maintained by Jill Lundell. Last updated 3 years ago.

3.9 match 4.76 score 38 scripts 1 dependents

forestry-labs

Rforestry:Random Forests, Linear Trees, and Gradient Boosting for Inference and Interpretability

Provides fast implementations of Honest Random Forests, Gradient Boosting, and Linear Random Forests, with an emphasis on inference and interpretability. Additionally contains methods for variable importance, out-of-bag prediction, regression monotonicity, and several methods for missing data imputation.

Maintained by Theo Saarinen. Last updated 5 days ago.

openblas cpp

3.3 match 5.57 score 82 scripts 1 dependents

hckiang

ttcg:Three-Term Conjugate Gradient for Unconstrained Optimization

Some accelerated three-term conjugate gradient algorithms implemented purely in R with the same user interface as optim(). The search directions and acceleration scheme are described in Andrei, N. (2013) <doi:10.1016/j.amc.2012.11.097>, Andrei, N. (2013) <doi:10.1016/j.cam.2012.10.002>, and Andrei, N (2015) <doi:10.1007/s11075-014-9845-9>. Line search is done by a hybrid algorithm incorporating the ideas in Oliveia and Takahashi (2020) <doi:10.1145/3423597> and More and Thuente (1994) <doi:10.1145/192115.192132>.

Maintained by Hao Chi Kiang. Last updated 1 years ago.

6.9 match 2.70 score 1 scripts

devillemereuil

Reacnorm:Perform a Partition of Variance of Reaction Norms

Partitions the phenotypic variance of a plastic trait, studied through its reaction norm. The variance partition distinguishes between the variance arising from the average shape of the reaction norms (V_Plas) and the (additive) genetic variance . The latter is itself separated into an environment-blind component (V_G/V_A) and the component arising from plasticity (V_GxE/V_AxE). The package also provides a way to further partition V_Plas into aspects (slope/curvature) of the shape of the average reaction norm (pi-decomposition) and partition V_Add (gamma-decomposition) and V_AxE (iota-decomposition) into the impact of genetic variation in the reaction norm parameters. Reference: de Villemereuil & Chevin (2025) <doi:10.32942/X2NC8B>.

Maintained by Pierre de Villemereuil. Last updated 18 days ago.

3.4 match 4 stars 5.34 score

cran

gfboost:Gradient-Free Gradient Boosting

Implementation of routines of the author's PhD thesis on gradient-free Gradient Boosting (Werner, Tino (2020) "Gradient-Free Gradient Boosting", URL '<https://oops.uni-oldenburg.de/id/eprint/4290>').

Maintained by Tino Werner. Last updated 3 years ago.

9.0 match 2.00 score

lbbe-software

DRomics:Dose Response for Omics

Several functions are provided for dose-response (or concentration-response) characterization from omics data. 'DRomics' is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations) rather than a great number of replicates (no need of replicates). 'DRomics' provides functions 1) to check, normalize and or transform data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item, 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data in log2, RNAseq data in raw counts, or already pretreated continuous omics data (such as metabolomic data) in log scale. In order to link responses across biological levels based on a common method, 'DRomics' also handles apical data as long as they are continuous and follow a normal distribution for each dose or concentration, with a common standard error. For further details see Delignette-Muller et al (2023) <DOI:10.24072/pcjournal.325> and Larras et al (2018) <DOI:10.1021/acs.est.8b04752>.

Maintained by Aurelie Siberchicot. Last updated 13 days ago.

3.0 match 4 stars 5.96 score 7 scripts

kezrael

optedr:Calculating Optimal and D-Augmented Designs

Calculates D-, Ds-, A-, I- and L-optimal designs for non-linear models, via an implementation of the cocktail algorithm (Yu, 2011, <doi:10.1007/s11222-010-9183-2>). Compares designs via their efficiency, and augments any design with a controlled efficiency. An efficient rounding function has been provided to transform approximate designs to exact designs.

Maintained by Carlos de la Calle-Arroyo. Last updated 1 months ago.

4.1 match 5 stars 4.29 score 13 scripts

r-econometrics

lfe:Linear Group Fixed Effects

Transforms away factors with many levels prior to doing an OLS. Useful for estimating linear models with multiple group fixed effects, and for estimating linear models which uses factors with many levels as pure control variables. See Gaure (2013) <doi:10.1016/j.csda.2013.03.024> Includes support for instrumental variables, conditional F statistics for weak instruments, robust and multi-way clustered standard errors, as well as limited mobility bias correction (Gaure 2014 <doi:10.1002/sta4.68>). Since version 3.0, it provides dedicated functions to estimate Poisson models.

Maintained by Mauricio Vargas Sepulveda. Last updated 1 years ago.

openblas

1.7 match 10.30 score 1.8k scripts 5 dependents

obreschkow

cooltools:Practical Tools for Scientific Computations and Visualizations

Collection of routines for efficient scientific computations in physics and astrophysics. These routines include utility functions, numerical computation tools, as well as visualisation tools. They can be used, for example, for generating random numbers from spherical and custom distributions, information and entropy analysis, special Fourier transforms, two-point correlation estimation (e.g. as in Landy & Szalay (1993) <doi:10.1086/172900>), binning & gridding of point sets, 2D interpolation, Monte Carlo integration, vector arithmetic and coordinate transformations. Also included is a non-exhaustive list of important constants and cosmological conversion functions. The graphics routines can be used to produce and export publication-ready scientific plots and movies, e.g. as used in Obreschkow et al. (2020, MNRAS Vol 493, Issue 3, Pages 4551–4569). These routines include special color scales, projection functions, and bitmap handling routines.

Maintained by Danail Obreschkow. Last updated 3 months ago.

cpp

5.6 match 1 stars 3.17 score 21 scripts

dusty-turner

ggvfields:Vector Field Visualizations with 'ggplot2'

A 'ggplot2' extension for visualizing vector fields in two-dimensional space. Provides flexible tools for creating vector and stream field layers, visualizing gradients and potential fields, and smoothing vector and scalar data to estimate underlying patterns.

Maintained by Dusty Turner. Last updated 3 days ago.

4.1 match 4.23 score

cdmuir

photosynthesis:Tools for Plant Ecophysiology & Modeling

Contains modeling and analytical tools for plant ecophysiology. MODELING: Simulate C3 photosynthesis using the Farquhar, von Caemmerer, Berry (1980) <doi:10.1007/BF00386231> model as described in Buckley and Diaz-Espejo (2015) <doi:10.1111/pce.12459>. It uses units to ensure that parameters are properly specified and transformed before calculations. Temperature response functions get automatically "baked" into all parameters based on leaf temperature following Bernacchi et al. (2002) <doi:10.1104/pp.008250>. The package includes boundary layer, cuticular, stomatal, and mesophyll conductances to CO2, which each can vary on the upper and lower portions of the leaf. Use straightforward functions to simulate photosynthesis over environmental gradients such as Photosynthetic Photon Flux Density (PPFD) and leaf temperature, or over trait gradients such as CO2 conductance or photochemistry. ANALYTICAL TOOLS: Fit ACi (Farquhar et al. (1980) <doi:10.1007/BF00386231>) and AQ curves (Marshall & Biscoe (1980) <doi:10.1093/jxb/31.1.29>), temperature responses (Heskel et al. (2016) <doi:10.1073/pnas.1520282113>; Kruse et al. (2008) <doi:10.1111/j.1365-3040.2008.01809.x>, Medlyn et al. (2002) <doi:10.1046/j.1365-3040.2002.00891.x>, Hobbs et al. (2013) <doi:10.1021/cb4005029>), respiration in the light (Kok (1956) <doi:10.1016/0006-3002(56)90003-8>, Walker & Ort (2015) <doi:10.1111/pce.12562>, Yin et al. (2009) <doi:10.1111/j.1365-3040.2009.01934.x>, Yin et al. (2011) <doi:10.1093/jxb/err038>), mesophyll conductance (Harley et al. (1992) <doi:10.1104/pp.98.4.1429>), pressure-volume curves (Koide et al. (2000) <doi:10.1007/978-94-009-2221-1_9>, Sack et al. (2003) <doi:10.1046/j.0016-8025.2003.01058.x>, Tyree et al. (1972) <doi:10.1093/jxb/23.1.267>), hydraulic vulnerability curves (Ogle et al. (2009) <doi:10.1111/j.1469-8137.2008.02760.x>, Pammenter et al. (1998) <doi:10.1093/treephys/18.8-9.589>), and tools for running sensitivity analyses particularly for variables with uncertainty (e.g. g_mc(), gamma_star(), R_d()).

Maintained by Chris Muir. Last updated 9 days ago.

2.3 match 37 stars 7.72 score 64 scripts

davidcsterratt

retistruct:Retinal Reconstruction Program

Reconstructs retinae by morphing a flat surface with cuts (a dissected flat-mount retina) onto a curvilinear surface (the standard retinal shape). It can estimate the position of a point on the intact adult retina to within 8 degrees of arc (3.6% of nasotemporal axis). The coordinates in reconstructed retinae can be transformed to visuotopic coordinates. For more details see Sterratt, D. C., Lyngholm, D., Willshaw, D. J. and Thompson, I. D. (2013) <doi:10.1371/journal.pcbi.1002921>.

Maintained by David C. Sterratt. Last updated 9 days ago.

3.8 match 8 stars 4.60 score

andrewraim

fntl:Numerical Tools for 'Rcpp' and Lambda Functions

Provides a 'C++' API for routinely used numerical tools such as integration, root-finding, and optimization, where function arguments are given as lambdas. This facilitates 'Rcpp' programming, enabling the development of 'R'-like code in 'C++' where functions can be defined on the fly and use variables in the surrounding environment.

Maintained by Andrew M. Raim. Last updated 4 months ago.

cpp

3.8 match 4.60 score 10 scripts

cran

incidental:Implements Empirical Bayes Incidence Curves

Make empirical Bayes incidence curves from reported case data using a specified delay distribution.

Maintained by Lauren Hannah. Last updated 5 years ago.

5.6 match 2 stars 3.00 score 10 scripts

trevorld

gridpattern:'grid' Pattern Grobs

Provides 'grid' grobs that fill in a user-defined area with various patterns. Includes enhanced versions of the geometric and image-based patterns originally contained in the 'ggpattern' package as well as original 'pch', 'polygon_tiling', 'regular_polygon', 'rose', 'text', 'wave', and 'weave' patterns plus support for custom user-defined patterns.

Maintained by Trevor L. Davis. Last updated 1 months ago.

2.0 match 33 stars 8.42 score 4 scripts 4 dependents

animint

animint2:Animated Interactive Grammar of Graphics

Functions are provided for defining animated, interactive data visualizations in R code, and rendering on a web page. The 2018 Journal of Computational and Graphical Statistics paper, <doi:10.1080/10618600.2018.1513367> describes the concepts implemented.

Maintained by Toby Hocking. Last updated 28 days ago.

1.9 match 64 stars 8.87 score 173 scripts

cran

catalytic:Tools for Applying Catalytic Priors in Statistical Modeling

To improve estimation accuracy and stability in statistical modeling, catalytic prior distributions are employed, integrating observed data with synthetic data generated from a simpler model's predictive distribution. This approach enhances model robustness, stability, and flexibility in complex data scenarios. The catalytic prior distributions are introduced by 'Huang et al.' (2020, <doi:10.1073/pnas.1920913117>), Li and Huang (2023, <doi:10.48550/arXiv.2312.01411>).

Maintained by Dongming Huang. Last updated 3 months ago.

5.2 match 3.18 score

n-kall

priorsense:Prior Diagnostics and Sensitivity Analysis

Provides functions for prior and likelihood sensitivity analysis in Bayesian models. Currently it implements methods to determine the sensitivity of the posterior to power-scaling perturbations of the prior and likelihood.

Maintained by Noa Kallioinen. Last updated 13 days ago.

bayes bayesian bayesian-data-analysis bayesian-methods prior-distribution sensitivity-analysis stan

2.0 match 59 stars 8.27 score 70 scripts

jdwor

vesselr:Gradient and Vesselness Tools for Arrays and NIfTI Images

Simple functions for calculating the image gradient, image hessian, volume ratio filter, and Frangi vesselness filter of 3-dimensional volumes.

Maintained by Jordan D. Dworkin. Last updated 8 years ago.

5.4 match 2 stars 3.00 score 4 scripts

mattcefalu

twangRDC:Gradient Boosting for Linkage Failure in FSRDCs

Provides functions for gradient boosted weighting to correct linkage failures or generate comparison groups.

Maintained by Matthew Cefalu. Last updated 4 years ago.

8.1 match 2.00 score 2 scripts

ezetoum

HBV.IANIGLA:Modular Hydrological Model

The HBV hydrological model (Bergström, S. and Lindström, G., (2015) <doi:10.1002/hyp.10510>) has been split in modules to allow the user to build his/her own model. This version was developed by the author in IANIGLA-CONICET (Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales - Consejo Nacional de Investigaciones Cientificas y Tecnicas) for hydroclimatic studies in the Andes. HBV.IANIGLA incorporates routines for clean and debris covered glacier melt simulations.

Maintained by Ezequiel Toum. Last updated 2 years ago.

cpp

3.5 match 4.56 score 12 scripts

cran

GPArotateDF:Derivative Free Gradient Projection Factor Rotation

Derivative Free Gradient Projection Algorithms for Factor Rotation. For more details see ?GPArotateDF. Theory for these functions can be found in the following publications: Jennrich (2004) <doi:10.1007/BF02295647>. Bernaards and Jennrich (2005) <doi:10.1177/0013164404272507>.

Maintained by Coen Bernaards. Last updated 1 years ago.

8.1 match 2.00 score 4 scripts

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.

5.9 match 2.72 score 52 scripts

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.

2.3 match 7.02 score 94 scripts 5 dependents

r-forge

systemfit:Estimating Systems of Simultaneous Equations

Econometric estimation of simultaneous systems of linear and nonlinear equations using Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted Two-Stage Least Squares (W2SLS), and Three-Stage Least Squares (3SLS) as suggested, e.g., by Zellner (1962) <doi:10.2307/2281644>, Zellner and Theil (1962) <doi:10.2307/1911287>, and Schmidt (1990) <doi:10.1016/0304-4076(90)90127-F>.

Maintained by Arne Henningsen. Last updated 2 years ago.

1.8 match 8.81 score 484 scripts 20 dependents

jenniniku

gllvm:Generalized Linear Latent Variable Models

Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).

Maintained by Jenni Niku. Last updated 14 hours ago.

cpp openmp

1.5 match 52 stars 10.53 score 176 scripts 1 dependents

cran

propagate:Propagation of Uncertainty

Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation.

Maintained by Andrej-Nikolai Spiess. Last updated 7 years ago.

cpp

3.3 match 2 stars 4.82 score 183 scripts 3 dependents

dkaschek

dMod:Dynamic Modeling and Parameter Estimation in ODE Models

The framework provides functions to generate ODEs of reaction networks, parameter transformations, observation functions, residual functions, etc. The framework follows the paradigm that derivative information should be used for optimization whenever possible. Therefore, all major functions produce and can handle expressions for symbolic derivatives.

Maintained by Daniel Kaschek. Last updated 10 days ago.

1.9 match 20 stars 8.35 score 251 scripts

nashjc

optextras:Tools to Support Optimization Possibly with Bounds and Masks

Tools to assist in safely applying user generated objective and derivative function to optimization programs. These are primarily function minimization methods with at most bounds and masks on the parameters. Provides a way to check the basic computation of objective functions that the user provides, along with proposed gradient and Hessian functions, as well as to wrap such functions to avoid failures when inadmissible parameters are provided. Check bounds and masks. Check scaling or optimality conditions. Perform an axial search to seek lower points on the objective function surface. Includes forward, central and backward gradient approximation codes.

Maintained by John C Nash. Last updated 5 years ago.

13.0 match 1.20 score 16 scripts

sandrinepavoine

adiv:Analysis of Diversity

Functions, data sets and examples for the calculation of various indices of biodiversity including species, functional and phylogenetic diversity. Part of the indices are expressed in terms of equivalent numbers of species. The package also provides ways to partition biodiversity across spatial or temporal scales (alpha, beta, gamma diversities). In addition to the quantification of biodiversity, ordination approaches are available which rely on diversity indices and allow the detailed identification of species, functional or phylogenetic differences between communities.

Maintained by Sandrine Pavoine. Last updated 1 years ago.

6.9 match 1 stars 2.28 score 63 scripts