R-universe search: spline

wenjie2wang

splines2:Regression Spline Functions and Classes

Constructs basis functions of B-splines, M-splines, I-splines, convex splines (C-splines), periodic splines, natural cubic splines, generalized Bernstein polynomials, their derivatives, and integrals (except C-splines) by closed-form recursive formulas. It also contains a C++ head-only library integrated with Rcpp. See Wang and Yan (2021) <doi:10.6339/21-JDS1020> for details.

Maintained by Wenjie Wang. Last updated 11 days ago.

derivative integral rcpp splines openblas cpp

55.5 match 43 stars 11.46 score 394 scripts 34 dependents

cran

gss:General Smoothing Splines

A comprehensive package for structural multivariate function estimation using smoothing splines.

Maintained by Chong Gu. Last updated 5 months ago.

fortran openblas

50.4 match 3 stars 6.40 score 137 dependents

tidymodels

recipes:Preprocessing and Feature Engineering Steps for Modeling

A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models.

Maintained by Max Kuhn. Last updated 5 days ago.

14.8 match 584 stars 18.71 score 7.2k scripts 380 dependents

gavinsimpson

gratia:Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'

Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.

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

distributional-regression gam gamm generalized-additive-mixed-models generalized-additive-models ggplot2 glm lm mgcv penalized-spline random-effects smoothing splines

19.3 match 216 stars 12.68 score 1.6k scripts 1 dependents

tyee001

VGAM:Vector Generalized Linear and Additive Models

An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes.

Maintained by Thomas Yee. Last updated 1 months ago.

fortran

22.6 match 10 stars 10.67 score 3.6k scripts 169 dependents

cran

mgcv:Mixed GAM Computation Vehicle with Automatic Smoothness Estimation

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

Maintained by Simon Wood. Last updated 1 years ago.

openblas openmp

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

covaruber

sommer:Solving Mixed Model Equations in R

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

Maintained by Giovanny Covarrubias-Pazaran. Last updated 21 days ago.

average-information mixed-models rcpparmadillo openblas cpp openmp

16.8 match 43 stars 12.70 score 300 scripts 9 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.

27.1 match 5 stars 7.17 score 388 scripts 23 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.

15.6 match 3 stars 12.29 score 2.0k scripts 143 dependents

dnychka

fields:Tools for Spatial Data

For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

Maintained by Douglas Nychka. Last updated 9 months ago.

fortran

15.2 match 15 stars 12.60 score 7.7k scripts 295 dependents

jeffreyracine

crs:Categorical Regression Splines

Regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I 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://www.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 2 months ago.

cpp

22.8 match 17 stars 8.30 score 90 scripts 1 dependents

mwheymans

psfmi:Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets

Pooling, backward and forward selection of linear, logistic and Cox regression models in multiply imputed datasets. Backward and forward selection can be done from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and the median p-values method. This is also possible for Mixed models. The models can contain continuous, dichotomous, categorical and restricted cubic spline predictors and interaction terms between all these type of predictors. The stability of the models can be evaluated using (cluster) bootstrapping. The package further contains functions to pool model performance measures as ROC/AUC, Reclassification, R-squared, scaled Brier score, H&L test and calibration plots for logistic regression models. Internal validation can be done across multiply imputed datasets with cross-validation or bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients can be obtained. Backward and forward selection as part of internal validation is possible. A function to externally validate logistic prediction models in multiple imputed datasets is available and a function to compare models. For Cox models a strata variable can be included. Eekhout (2017) <doi:10.1186/s12874-017-0404-7>. Wiel (2009) <doi:10.1093/biostatistics/kxp011>. Marshall (2009) <doi:10.1186/1471-2288-9-57>.

Maintained by Martijn Heymans. Last updated 2 years ago.

cox-regression imputation imputed-datasets logistic multiple-imputation pool predictor regression selection spline spline-predictors

23.6 match 10 stars 7.17 score 70 scripts

goepp

aspline:Spline Regression with Adaptive Knot Selection

Perform one-dimensional spline regression with automatic knot selection. This package uses a penalized approach to select the most relevant knots. B-splines of any degree can be fitted. More details in 'Goepp et al. (2018)', "Spline Regression with Automatic Knot Selection", <arXiv:1808.01770>.

Maintained by Vivien Goepp. Last updated 3 years ago.

adaptive-splines fitting-splines knots regression-spline cpp

36.3 match 6 stars 4.52 score 11 scripts

chjackson

survextrap:Survival Extrapolation with a Flexible Parametric Model and External Data

Survival analysis using a flexible Bayesian model for individual-level right-censored data, optionally combined with aggregate data on counts of survivors in different periods of time. An M-spline is used to describe the hazard function, with a hierarchical prior on the coefficients to control overfitting. Proportional hazards or flexible non-proportional hazards models can be used to relate survival to predictors. Mixture cure models, additive hazards (relative survival) models and waning treatment effects models are also supported. Priors can be customised and calibrated to substantive beliefs. Posterior distributions are estimated using Stan, and outputs are arranged in a tidy format. See See Jackson (2023) <doi:10.48550/arXiv.2306.03957>.

Maintained by Christopher Jackson. Last updated 17 days ago.

cpp

32.3 match 10 stars 5.00 score 11 scripts

jkcshea

ivmte:Instrumental Variables: Extrapolation by Marginal Treatment Effects

The marginal treatment effect was introduced by Heckman and Vytlacil (2005) <doi:10.1111/j.1468-0262.2005.00594.x> to provide a choice-theoretic interpretation to instrumental variables models that maintain the monotonicity condition of Imbens and Angrist (1994) <doi:10.2307/2951620>. This interpretation can be used to extrapolate from the compliers to estimate treatment effects for other subpopulations. This package provides a flexible set of methods for conducting this extrapolation. It allows for parametric or nonparametric sieve estimation, and allows the user to maintain shape restrictions such as monotonicity. The package operates in the general framework developed by Mogstad, Santos and Torgovitsky (2018) <doi:10.3982/ECTA15463>, and accommodates either point identification or partial identification (bounds). In the partially identified case, bounds are computed using either linear programming or quadratically constrained quadratic programming. Support for four solvers is provided. Gurobi and the Gurobi R API can be obtained from <http://www.gurobi.com/index>. CPLEX can be obtained from <https://www.ibm.com/analytics/cplex-optimizer>. CPLEX R APIs 'Rcplex' and 'cplexAPI' are available from CRAN. MOSEK and the MOSEK R API can be obtained from <https://www.mosek.com/>. The lp_solve library is freely available from <http://lpsolve.sourceforge.net/5.5/>, and is included when installing its API 'lpSolveAPI', which is available from CRAN.

Maintained by Joshua Shea. Last updated 7 months ago.

26.8 match 18 stars 5.33 score 30 scripts

pauleilers

JOPS:Practical Smoothing with P-Splines

Functions and data to reproduce all plots in the book "Practical Smoothing. The Joys of P-splines" by Paul H.C. Eilers and Brian D. Marx (2021, ISBN:978-1108482950).

Maintained by Paul Eilers. Last updated 2 years ago.

41.7 match 1 stars 3.43 score 296 scripts 3 dependents

therneau

survival:Survival Analysis

Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.

Maintained by Terry M Therneau. Last updated 3 months ago.

6.9 match 400 stars 20.43 score 29k scripts 3.9k dependents

dewittpe

cpr:Control Polygon Reduction

Implementation of the Control Polygon Reduction and Control Net Reduction methods for finding parsimonious B-spline regression models.

Maintained by Peter DeWitt. Last updated 8 months ago.

openblas cpp

23.6 match 2 stars 5.87 score 62 scripts 1 dependents

fauvernierma

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

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

Maintained by Mathieu Fauvernier. Last updated 3 months ago.

cpp

19.5 match 12 stars 6.82 score 85 scripts 1 dependents

kingaa

pomp:Statistical Inference for Partially Observed Markov Processes

Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.

Maintained by Aaron A. King. Last updated 1 months ago.

abc b-spline differential-equations dynamical-systems iterated-filtering likelihood likelihood-free markov-chain-monte-carlo markov-model mathematical-modelling measurement-error particle-filter sequential-monte-carlo simulation-based-inference sobol-sequence state-space statistical-inference stochastic-processes time-series openblas

9.5 match 115 stars 11.81 score 1.3k scripts 4 dependents

deepayan

lattice:Trellis Graphics for R

A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements. See ?Lattice for an introduction.

Maintained by Deepayan Sarkar. Last updated 11 months ago.

6.3 match 68 stars 17.33 score 27k scripts 13k dependents

kgoldfeld

simstudy:Simulation of Study Data

Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).

Maintained by Keith Goldfeld. Last updated 8 months ago.

data-generation data-simulation simulation statistical-models cpp

9.6 match 82 stars 11.00 score 972 scripts 1 dependents

biometris

LMMsolver:Linear Mixed Model Solver

An efficient and flexible system to solve sparse mixed model equations. Important applications are the use of splines to model spatial or temporal trends as described in Boer (2023). (<doi:10.1177/1471082X231178591>).

Maintained by Bart-Jan van Rossum. Last updated 2 months ago.

mixed-models splines cpp

12.5 match 11 stars 8.14 score 66 scripts 3 dependents

chjackson

flexsurv:Flexible Parametric Survival and Multi-State Models

Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions. Any user-defined parametric distribution can be fitted, given at least an R function defining the probability density or hazard. There are also tools for fitting and predicting from fully parametric multi-state models, based on either cause-specific hazards or mixture models.

Maintained by Christopher Jackson. Last updated 2 months ago.

cpp

7.5 match 57 stars 13.31 score 632 scripts 43 dependents

stan-dev

rstanarm:Bayesian Applied Regression Modeling via Stan

Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

Maintained by Ben Goodrich. Last updated 9 months ago.

bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics multilevel-models rstan rstanarm stan statistical-modeling cpp

6.2 match 393 stars 15.68 score 5.0k scripts 13 dependents

mathscell

bspline:B-Spline Interpolation and Regression

Build and use B-splines for interpolation and regression. In case of regression, equality constraints as well as monotonicity and/or positivity of B-spline weights can be imposed. Moreover, knot positions (not only spline weights) can be part of optimized parameters too. For this end, 'bspline' is able to calculate Jacobian of basis vectors as function of knot positions. User is provided with functions calculating spline values at arbitrary points. These functions can be differentiated and integrated to obtain B-splines calculating derivatives/integrals at any point. B-splines of this package can simultaneously operate on a series of curves sharing the same set of knots. 'bspline' is written with concern about computing performance that's why the basis and Jacobian calculation is implemented in C++. The rest is implemented in R but without notable impact on computing speed.

Maintained by Serguei Sokol. Last updated 27 days ago.

openblas cpp

27.7 match 3 stars 3.48 score 1 scripts

andrisignorell

DescTools:Tools for Descriptive Statistics

A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.

Maintained by Andri Signorell. Last updated 9 days ago.

fortran cpp

5.8 match 87 stars 16.68 score 7.7k scripts 99 dependents

harrelfe

Hmisc:Harrell Miscellaneous

Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.

Maintained by Frank E Harrell Jr. Last updated 2 days ago.

fortran

5.3 match 210 stars 17.61 score 17k scripts 750 dependents

hrbrmstr

ggalt:Extra Coordinate Systems, 'Geoms', Statistical Transformations, Scales and Fonts for 'ggplot2'

A compendium of new geometries, coordinate systems, statistical transformations, scales and fonts for 'ggplot2', including splines, 1d and 2d densities, univariate average shifted histograms, a new map coordinate system based on the 'PROJ.4'-library along with geom_cartogram() that mimics the original functionality of geom_map(), formatters for "bytes", a stat_stepribbon() function, increased 'plotly' compatibility and the 'StateFace' open source font 'ProPublica'. Further new functionality includes lollipop charts, dumbbell charts, the ability to encircle points and coordinate-system-based text annotations.

Maintained by Bob Rudis. Last updated 2 years ago.

geom ggplot-extension ggplot2 ggplot2-geom ggplot2-scales

7.5 match 674 stars 12.59 score 2.3k scripts 7 dependents

nicwir

QurvE:Robust and User-Friendly Analysis of Growth and Fluorescence Curves

High-throughput analysis of growth curves and fluorescence data using three methods: linear regression, growth model fitting, and smooth spline fit. Analysis of dose-response relationships via smoothing splines or dose-response models. Complete data analysis workflows can be executed in a single step via user-friendly wrapper functions. The results of these workflows are summarized in detailed reports as well as intuitively navigable 'R' data containers. A 'shiny' application provides access to all features without requiring any programming knowledge. The package is described in further detail in Wirth et al. (2023) <doi:10.1038/s41596-023-00850-7>.

Maintained by Nicolas T. Wirth. Last updated 1 years ago.

15.2 match 25 stars 6.00 score 7 scripts

mcanouil

eggla:Early Growth Genetics Longitudinal Analysis

Tools for longitudinal analysis within the EGG (Early Growth Genetics) Consortium (<http://egg-consortium.org/>).

Maintained by Mickaël Canouil. Last updated 1 months ago.

genetics growth-curves infancy longitudinal-analysis mixed-effects-models spline-regression

21.9 match 3 stars 4.15 score 19 scripts

adwolfer

santaR:Short Asynchronous Time-Series Analysis

A graphical and automated pipeline for the analysis of short time-series in R ('santaR'). This approach is designed to accommodate asynchronous time sampling (i.e. different time points for different individuals), inter-individual variability, noisy measurements and large numbers of variables. Based on a smoothing splines functional model, 'santaR' is able to detect variables highlighting significantly different temporal trajectories between study groups. Designed initially for metabolic phenotyping, 'santaR' is also suited for other Systems Biology disciplines. Command line and graphical analysis (via a 'shiny' application) enable fast and parallel automated analysis and reporting, intuitive visualisation and comprehensive plotting options for non-specialist users.

Maintained by Arnaud Wolfer. Last updated 1 years ago.

pcamethods (>= 1.92.0)

13.8 match 11 stars 6.44 score 63 scripts

janoleko

LaMa:Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.

Maintained by Jan-Ole Koslik. Last updated 1 days ago.

openblas cpp openmp

11.3 match 9 stars 7.84 score 42 scripts

anna-neufeld

splinetree:Longitudinal Regression Trees and Forests

Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu and Lambert (1999) <doi:10.1080/10618600.1999.10474847>. This method allows trees and forests to be built while considering either level and shape or only shape of response trajectories.

Maintained by Anna Neufeld. Last updated 6 years ago.

16.0 match 4 stars 5.24 score 29 scripts

veronica0206

nlpsem:Linear and Nonlinear Longitudinal Process in Structural Equation Modeling Framework

Provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Jin Liu (2023) <arXiv:2302.03237v2>.

Maintained by Jin Liu. Last updated 4 months ago.

12.0 match 145 stars 6.91 score 16 scripts

objornstad

ncf:Spatial Covariance Functions

Spatial (cross-)covariance and related geostatistical tools: the nonparametric (cross-)covariance function , the spline correlogram, the nonparametric phase coherence function, local indicators of spatial association (LISA), (Mantel) correlogram, (Partial) Mantel test.

Maintained by Ottar N. Bjornstad. Last updated 3 years ago.

12.7 match 5 stars 6.44 score 328 scripts 1 dependents

cran

polspline:Polynomial Spline Routines

Routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars, by C. Kooperberg and co-authors.

Maintained by Charles Kooperberg. Last updated 10 months ago.

fortran openblas

14.4 match 5.65 score 131 dependents

sinhrks

ggfortify:Data Visualization Tools for Statistical Analysis Results

Unified plotting tools for statistics commonly used, such as GLM, time series, PCA families, clustering and survival analysis. The package offers a single plotting interface for these analysis results and plots in a unified style using 'ggplot2'.

Maintained by Yuan Tang. Last updated 9 months ago.

5.5 match 529 stars 14.49 score 9.1k scripts 22 dependents

r-forge

cobs:Constrained B-Splines (Sparse Matrix Based)

Qualitatively Constrained (Regression) Smoothing Splines via Linear Programming and Sparse Matrices.

Maintained by Martin Maechler. Last updated 3 months ago.

10.7 match 7.44 score 134 scripts 18 dependents

stefanocoretta

rticulate:Articulatory Data Processing in R

A tool for processing Articulate Assistant Advanced™ (AAA) ultrasound tongue imaging data and Carstens AG500/1 electro-magnetic articulographic data.

Maintained by Stefano Coretta. Last updated 24 days ago.

phonetics software tongue-image ultrasound ultrasound-tongue-imaging

12.6 match 5 stars 5.88 score 17 scripts

emilioluissaenzguillen

GeDS:Geometrically Designed Spline Regression

Spline Regression, Generalized Additive Models, and Component-wise Gradient Boosting, utilizing Geometrically Designed (GeD) Splines. GeDS regression is a non-parametric method inspired by geometric principles, for fitting spline regression models with variable knots in one or two independent variables. It efficiently estimates the number of knots and their positions, as well as the spline order, assuming the response variable follows a distribution from the exponential family. GeDS models integrate the broader category of Generalized (Non-)Linear Models, offering a flexible approach to modeling complex relationships. A description of the method can be found in Kaishev et al. (2016) <doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2023) <doi:10.1016/j.amc.2022.127493>. Further extending its capabilities, GeDS's implementation includes Generalized Additive Models (GAM) and Functional Gradient Boosting (FGB), enabling versatile multivariate predictor modeling, as discussed in the forthcoming work of Dimitrova et al. (2025).

Maintained by Emilio L. Sáenz Guillén. Last updated 5 days ago.

cpp

18.1 match 4.06 score 19 scripts

pecanproject

PEcAn.uncertainty:PEcAn Functions Used for Propagating and Partitioning Uncertainties in Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by David LeBauer. Last updated 2 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

8.2 match 216 stars 8.91 score 15 scripts 5 dependents

stla

qsplines:Quaternions Splines

Provides routines to create some quaternions splines: Barry-Goldman algorithm, De Casteljau algorithm, and Kochanek-Bartels algorithm. The implementations are based on the Python library 'splines'. Quaternions splines allow to construct spherical curves. References: Barry and Goldman <doi:10.1145/54852.378511>, Kochanek and Bartels <doi:10.1145/800031.808575>.

Maintained by Stéphane Laurent. Last updated 2 years ago.

quaternions rcpp splines cpp

23.7 match 2 stars 3.08 score 12 scripts

samhforbes

eyetrackingR:Eye-Tracking Data Analysis

Addresses tasks along the pipeline from raw data to analysis and visualization for eye-tracking data. Offers several popular types of analyses, including linear and growth curve time analyses, onset-contingent reaction time analyses, as well as several non-parametric bootstrapping approaches. For references to the approach see Mirman, Dixon & Magnuson (2008) <doi:10.1016/j.jml.2007.11.006>, and Barr (2008) <doi:10.1016/j.jml.2007.09.002>.

Maintained by Samuel Forbes. Last updated 2 years ago.

9.0 match 22 stars 7.84 score 60 scripts

zheyuanli

gps:General P-Splines

General P-splines are non-uniform B-splines penalized by a general difference penalty, proposed by Li and Cao (2022) <arXiv:2201.06808>. Constructible on arbitrary knots, they extend the standard P-splines of Eilers and Marx (1996) <doi:10.1214/ss/1038425655>. They are also related to the O-splines of O'Sullivan (1986) <doi:10.1214/ss/1177013525> via a sandwich formula that links a general difference penalty to a derivative penalty. The package includes routines for setting up and handling difference and derivative penalties. It also fits P-splines and O-splines to (x, y) data (optionally weighted) for a grid of smoothing parameter values in the automatic search intervals of Li and Cao (2023) <doi:10.1007/s11222-022-10178-z>. It aims to facilitate other packages to implement P-splines or O-splines as a smoothing tool in their model estimation framework.

Maintained by Zheyuan Li. Last updated 2 years ago.

openblas

23.4 match 2 stars 3.00 score

r-forge

zoo:S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations)

An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo's key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics.

Maintained by Achim Zeileis. Last updated 13 days ago.

4.3 match 16.23 score 33k scripts 2.2k dependents

oswaldogressani

blapsr:Bayesian Inference with Laplace Approximations and P-Splines

Laplace approximations and penalized B-splines are combined for fast Bayesian inference in latent Gaussian models. The routines can be used to fit survival models, especially proportional hazards and promotion time cure models (Gressani, O. and Lambert, P. (2018) <doi:10.1016/j.csda.2018.02.007>). The Laplace-P-spline methodology can also be implemented for inference in (generalized) additive models (Gressani, O. and Lambert, P. (2021) <doi:10.1016/j.csda.2020.107088>). See the associated website for more information and examples.

Maintained by Oswaldo Gressani. Last updated 3 years ago.

15.6 match 5 stars 4.40 score 4 scripts

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

6.1 match 28 stars 11.01 score 137 scripts 50 dependents

zarquon42b

Morpho:Calculations and Visualisations Related to Geometric Morphometrics

A toolset for Geometric Morphometrics and mesh processing. This includes (among other stuff) mesh deformations based on reference points, permutation tests, detection of outliers, processing of sliding semi-landmarks and semi-automated surface landmark placement.

Maintained by Stefan Schlager. Last updated 5 months ago.

openblas cpp openmp

6.7 match 51 stars 10.00 score 218 scripts 13 dependents

momx

Momocs:Morphometrics using R

The goal of 'Momocs' is to provide a complete, convenient, reproducible and open-source toolkit for 2D morphometrics. It includes most common 2D morphometrics approaches on outlines, open outlines, configurations of landmarks, traditional morphometrics, and facilities for data preparation, manipulation and visualization with a consistent grammar throughout. It allows reproducible, complex morphometrics analyses and other morphometrics approaches should be easy to plug in, or develop from, on top of this canvas.

Maintained by Vincent Bonhomme. Last updated 1 years ago.

morphometrics

8.9 match 51 stars 7.42 score 346 scripts

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

7.2 match 94 stars 9.16 score 474 scripts

egpivo

SpatPCA:Regularized Principal Component Analysis for Spatial Data

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.

Maintained by Wen-Ting Wang. Last updated 7 months ago.

admm covariance-estimation eigenfunctions lasso matrix-factorization pca rcpparmadillo rcppparallel regularization spatial spatial-data-analysis splines openblas cpp openmp

11.9 match 20 stars 5.53 score 17 scripts

bioc

metagenomeSeq:Statistical analysis for sparse high-throughput sequencing

metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.

Maintained by Joseph N. Paulson. Last updated 3 months ago.

immunooncology classification clustering geneticvariability differentialexpression microbiome metagenomics normalization visualization multiplecomparison sequencing software

5.4 match 69 stars 12.02 score 494 scripts 7 dependents

bioc

SplineDV:Differential Variability (DV) analysis for single-cell RNA sequencing data. (e.g. Identify Differentially Variable Genes across two experimental conditions)

A spline based scRNA-seq method for identifying differentially variable (DV) genes across two experimental conditions. Spline-DV constructs a 3D spline from 3 key gene statistics: mean expression, coefficient of variance, and dropout rate. This is done for both conditions. The 3D spline provides the “expected” behavior of genes in each condition. The distance of the observed mean, CV and dropout rate of each gene from the expected 3D spline is used to measure variability. As the final step, the spline-DV method compares the variabilities of each condition to identify differentially variable (DV) genes.

Maintained by Shreyan Gupta. Last updated 1 months ago.

software singlecell sequencing differentialexpression rnaseq geneexpression transcriptomics featureextraction

12.7 match 2 stars 5.08 score 3 scripts

mxrodriguezuvigo

SpATS:Spatial Analysis of Field Trials with Splines

Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models.

Maintained by Maria Xose Rodriguez-Alvarez. Last updated 5 months ago.

11.6 match 8 stars 5.54 score 96 scripts 9 dependents

biometris

statgenHTP:High Throughput Phenotyping (HTP) Data Analysis

Phenotypic analysis of data coming from high throughput phenotyping (HTP) platforms, including different types of outlier detection, spatial analysis, and parameter estimation. The package is being developed within the EPPN2020 project (<https://eppn2020.plant-phenotyping.eu/>). Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).

Maintained by Bart-Jan van Rossum. Last updated 3 months ago.

genetics high-troughput-phenotyping

11.2 match 4 stars 5.43 score 17 scripts

aaronolsen

bezier:Toolkit for Bezier Curves and Splines

The bezier package is a toolkit for working with Bezier curves and splines. The package provides functions for point generation, arc length estimation, degree elevation and curve fitting.

Maintained by Aaron Olsen. Last updated 6 years ago.

10.8 match 3 stars 5.54 score 31 scripts 25 dependents

thomasp85

ggforce:Accelerating 'ggplot2'

The aim of 'ggplot2' is to aid in visual data investigations. This focus has led to a lack of facilities for composing specialised plots. 'ggforce' aims to be a collection of mainly new stats and geoms that fills this gap. All additional functionality is aimed to come through the official extension system so using 'ggforce' should be a stable experience.

Maintained by Thomas Lin Pedersen. Last updated 1 years ago.

ggplot-extension ggplot2 visualization cpp

3.6 match 920 stars 15.83 score 9.3k scripts 293 dependents

k3jph

cmna:Computational Methods for Numerical Analysis

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

Maintained by James Howard. Last updated 4 years ago.

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

10.0 match 16 stars 5.65 score 62 scripts 3 dependents

tpetzoldt

growthrates:Estimate Growth Rates from Experimental Data

A collection of methods to determine growth rates from experimental data, in particular from batch experiments and plate reader trials.

Maintained by Thomas Petzoldt. Last updated 1 years ago.

7.4 match 27 stars 7.52 score 102 scripts

vochr

TapeR:Flexible Tree Taper Curves Based on Semiparametric Mixed Models

Implementation of functions for fitting taper curves (a semiparametric linear mixed effects taper model) to diameter measurements along stems. Further functions are provided to estimate the uncertainty around the predicted curves, to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. For cases where tree heights are not measured, methods for estimating additional variance in volume predictions resulting from uncertainties in tree height models (tariffs) are provided. The example data include the taper curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees and a subset of section-wise diameter measurements of these trees. The functions implemented here are detailed in Kublin, E., Breidenbach, J., Kaendler, G. (2013) <doi:10.1007/s10342-013-0715-0>.

Maintained by Christian Vonderach. Last updated 1 years ago.

12.6 match 4.38 score 16 scripts 1 dependents

cran

pspline:Penalized Smoothing Splines

Smoothing splines with penalties on order m derivatives.

Maintained by Brian Ripley. Last updated 3 months ago.

fortran

9.7 match 1 stars 5.69 score 94 dependents

cran

bigsplines:Smoothing Splines for Large Samples

Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.

Maintained by Nathaniel E. Helwig. Last updated 7 years ago.

fortran

25.1 match 2.19 score 26 scripts 2 dependents

gasparrini

dlnm:Distributed Lag Non-Linear Models

Collection of functions for distributed lag linear and non-linear models.

Maintained by Antonio Gasparrini. Last updated 3 years ago.

5.3 match 77 stars 10.30 score 392 scripts 6 dependents

bioc

TPP:Analyze thermal proteome profiling (TPP) experiments

Analyze thermal proteome profiling (TPP) experiments with varying temperatures (TR) or compound concentrations (CCR).

Maintained by Dorothee Childs. Last updated 5 months ago.

immunooncology proteomics massspectrometry

10.8 match 4.98 score 16 scripts

kkholst

mets:Analysis of Multivariate Event Times

Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions.

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

multivariate-time-to-event survival-analysis time-to-event fortran openblas cpp

4.0 match 14 stars 13.47 score 236 scripts 42 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

6.2 match 24 stars 8.55 score 81 scripts

growthcharts

brokenstick:Broken Stick Model for Irregular Longitudinal Data

Data on multiple individuals through time are often sampled at times that differ between persons. Irregular observation times can severely complicate the statistical analysis of the data. The broken stick model approximates each subject’s trajectory by one or more connected line segments. The times at which segments connect (breakpoints) are identical for all subjects and under control of the user. A well-fitting broken stick model effectively transforms individual measurements made at irregular times into regular trajectories with common observation times. Specification of the model requires three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The package contains functions for fitting the broken stick model to data, for predicting curves in new data and for plotting broken stick estimates. The package supports two optimization methods, and includes options to structure the variance-covariance matrix of the random effects. The analyst may use the software to smooth growth curves by a series of connected straight lines, to align irregularly observed curves to a common time grid, to create synthetic curves at a user-specified set of breakpoints, to estimate the time-to-time correlation matrix and to predict future observations. See <doi:10.18637/jss.v106.i07> for additional documentation on background, methodology and applications.

Maintained by Stef van Buuren. Last updated 2 years ago.

b-spline growth-curves linear-mixed-models longitudinal-data

9.9 match 9 stars 5.33 score 12 scripts

rjdverse

rjd3sts:State Space Framework and Structural Time Series with 'JDemetra+ 3.x'

R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It offers access to several functions on state space models and structural time series.

Maintained by Jean Palate. Last updated 8 months ago.

openjdk

7.9 match 2 stars 6.64 score 25 scripts 4 dependents

famuvie

breedR:Statistical Methods for Forest Genetic Resources Analysts

Statistical tools to build predictive models for the breeders community. It aims to assess the genetic value of individuals under a number of situations, including spatial autocorrelation, genetic/environment interaction and competition. It is under active development as part of the Trees4Future project, particularly developed having forest genetic trials in mind. But can be used for animals or other situations as well.

Maintained by Facundo Muñoz. Last updated 8 months ago.

9.5 match 33 stars 5.44 score 24 scripts

rspatial

raster:Geographic Data Analysis and Modeling

Reading, writing, manipulating, analyzing and modeling of spatial data. This package has been superseded by the "terra" package <https://CRAN.R-project.org/package=terra>.

Maintained by Robert J. Hijmans. Last updated 2 months ago.

cpp

3.0 match 164 stars 17.05 score 58k scripts 555 dependents

finleya

MBA:Multilevel B-Spline Approximation

Functions to interpolate irregularly and regularly spaced data using Multilevel B-spline Approximation (MBA). Functions call portions of the SINTEF Multilevel B-spline Library written by Øyvind Hjelle which implements methods developed by Lee, Wolberg and Shin (1997; <doi:10.1109/2945.620490>).

Maintained by Andrew Finley. Last updated 6 months ago.

cpp

7.3 match 4 stars 6.75 score 295 scripts 26 dependents

trevorhastie

mda:Mixture and Flexible Discriminant Analysis

Mixture and flexible discriminant analysis, multivariate adaptive regression splines (MARS), BRUTO, and vector-response smoothing splines. Hastie, Tibshirani and Friedman (2009) "Elements of Statistical Learning (second edition, chap 12)" Springer, New York.

Maintained by Trevor Hastie. Last updated 4 months ago.

fortran

6.4 match 3 stars 7.60 score 428 scripts 17 dependents

mariaguilleng

boostingDEA:A Boosting Approach to Data Envelopment Analysis

Includes functions to estimate production frontiers and make ideal output predictions in the Data Envelopment Analysis (DEA) context using both standard models from DEA and Free Disposal Hull (FDH) and boosting techniques. In particular, EATBoosting (Guillen et al., 2023 <doi:10.1016/j.eswa.2022.119134>) and MARSBoosting. Moreover, the package includes code for estimating several technical efficiency measures using different models such as the input and output-oriented radial measures, the input and output-oriented Russell measures, the Directional Distance Function (DDF), the Weighted Additive Measure (WAM) and the Slacks-Based Measure (SBM).

Maintained by Maria D. Guillen. Last updated 2 years ago.

12.1 match 2 stars 4.00 score 3 scripts

halpo

orthogonalsplinebasis:Orthogonal B-Spline Basis Functions

Represents the basis functions for B-splines in a simple matrix formulation that facilitates, taking integrals, derivatives, and making orthogonal the basis functions.

Maintained by Andrew Redd. Last updated 3 years ago.

14.3 match 1 stars 3.36 score 19 scripts 4 dependents

bioc

NBAMSeq:Negative Binomial Additive Model for RNA-Seq Data

High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation.

Maintained by Xu Ren. Last updated 5 months ago.

rnaseq differentialexpression geneexpression sequencing coverage differential-expression gene-expression generalized-additive-models generalized-linear-models negative-binomial-regression splines

10.0 match 2 stars 4.78 score 2 scripts

obrl-soil

mpspline2:Mass-Preserving Spline Functions for Soil Data

A low-dependency implementation of GSIF::mpspline() <https://r-forge.r-project.org/scm/viewvc.php/pkg/R/mpspline.R?view=markup&revision=240&root=gsif>, which applies a mass-preserving spline to soil attributes. Splining soil data is a safe way to make continuous down-profile estimates of attributes measured over discrete, often discontinuous depth intervals.

Maintained by Lauren OBrien. Last updated 1 years ago.

10.6 match 6 stars 4.50 score 35 scripts

finnishcancerregistry

popEpi:Functions for Epidemiological Analysis using Population Data

Enables computation of epidemiological statistics, including those where counts or mortality rates of the reference population are used. Currently supported: excess hazard models (Dickman, Sloggett, Hills, and Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean survival times, relative/net survival (in particular the Ederer II (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and Esteve (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), and standardized incidence and mortality ratios, all of which can be easily adjusted for by covariates such as age. Fast splitting and aggregation of 'Lexis' objects (from package 'Epi') and other computations achieved using 'data.table'.

Maintained by Joonas Miettinen. Last updated 1 months ago.

adjust-estimates age-adjusting direct-adjusting epidemiology indirect-adjusting survival

5.9 match 8 stars 8.05 score 117 scripts 1 dependents

stephenmilborrow

earth:Multivariate Adaptive Regression Splines

Build regression models using the techniques in Friedman's papers "Fast MARS" and "Multivariate Adaptive Regression Splines" <doi:10.1214/aos/1176347963>. (The term "MARS" is trademarked and thus not used in the name of the package.)

Maintained by Stephen Milborrow. Last updated 5 months ago.

fortran openblas

5.5 match 5 stars 8.40 score 3.9k scripts 26 dependents

pecanproject

PEcAn.data.atmosphere:PEcAn Functions Used for Managing Climate Driver Data

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The PECAn.data.atmosphere package converts climate driver data into a standard format for models integrated into PEcAn. As a standalone package, it provides an interface to access diverse climate data sets.

Maintained by David LeBauer. Last updated 2 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

3.9 match 216 stars 11.59 score 64 scripts 14 dependents

lubomirantoni

ICSsmoothing:Data Smoothing by Interpolating Cubic Splines

We construct the explicit form of clamped cubic interpolating spline (both uniform - knots are equidistant and non-uniform - knots are arbitrary). Using this form, we propose a linear regression model suitable for real data smoothing.

Maintained by Lubomir Antoni. Last updated 1 years ago.

16.6 match 2.70 score 6 scripts

matthias-da

robCompositions:Compositional Data Analysis

Methods for analysis of compositional data including robust methods (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), count zeros (<doi:10.1177/1471082X14535524>), methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), contingency (<doi:10.1080/03610926.2013.824980>) and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.

Maintained by Matthias Templ. Last updated 26 days ago.

cpp

4.8 match 11 stars 9.19 score 226 scripts 2 dependents

opendendro

dplR:Dendrochronology Program Library in R

Perform tree-ring analyses such as detrending, chronology building, and cross dating. Read and write standard file formats used in dendrochronology.

Maintained by Andy Bunn. Last updated 18 days ago.

fortran

3.7 match 39 stars 11.71 score 546 scripts 26 dependents

maeveupton

reslr:Modelling Relative Sea Level Data

The Bayesian modelling of relative sea-level data using a comprehensive approach that incorporates various statistical models within a unifying framework. Details regarding each statistical models; linear regression (Ashe et al 2019) <doi:10.1016/j.quascirev.2018.10.032>, change point models (Cahill et al 2015) <doi:10.1088/1748-9326/10/8/084002>, integrated Gaussian process models (Cahill et al 2015) <doi:10.1214/15-AOAS824>, temporal splines (Upton et al 2023) <arXiv:2301.09556>, spatio-temporal splines (Upton et al 2023) <arXiv:2301.09556> and generalised additive models (Upton et al 2023) <arXiv:2301.09556>. This package facilitates data loading, model fitting and result summarisation. Notably, it accommodates the inherent measurement errors found in relative sea-level data across multiple dimensions, allowing for their inclusion in the statistical models.

Maintained by Maeve Upton. Last updated 1 years ago.

jags cpp

8.1 match 4 stars 5.23 score 28 scripts

dfrancom

BASS:Bayesian Adaptive Spline Surfaces

Bayesian fitting and sensitivity analysis methods for adaptive spline surfaces described in <doi:10.18637/jss.v094.i08>. Built to handle continuous and categorical inputs as well as functional or scalar output. An extension of the methodology in Denison, Mallick and Smith (1998) <doi:10.1023/A:1008824606259>.

Maintained by Devin Francom. Last updated 2 years ago.

8.8 match 1 stars 4.78 score 173 scripts

winvector

vtreat:A Statistically Sound 'data.frame' Processor/Conditioner

A 'data.frame' processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. 'vtreat' prepares variables so that data has fewer exceptional cases, making it easier to safely use models in production. Common problems 'vtreat' defends against: 'Inf', 'NA', too many categorical levels, rare categorical levels, and new categorical levels (levels seen during application, but not during training). Reference: "'vtreat': a data.frame Processor for Predictive Modeling", Zumel, Mount, 2016, <DOI:10.5281/zenodo.1173313>.

Maintained by John Mount. Last updated 2 months ago.

categorical-variables machine-learning-algorithms nested-models prepare-data

3.8 match 285 stars 11.19 score 328 scripts 1 dependents

fitzlab-al

gdm:Generalized Dissimilarity Modeling

A toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models. Mokany K, Ware C, Woolley SNC, Ferrier S, Fitzpatrick MC (2022) <doi:10.1111/geb.13459> Ferrier S, Manion G, Elith J, Richardson K (2007) <doi:10.1111/j.1472-4642.2007.00341.x>.

Maintained by Matt Fitzpatrick. Last updated 2 months ago.

cpp

5.2 match 35 stars 8.12 score 145 scripts

jeinbeck-code

LPCM:Local Principal Curve Methods

Fitting multivariate data patterns with local principal curves, including tools for data compression (projection) and measuring goodness-of-fit; with some additional functions for mean shift clustering. See Einbeck, Tutz and Evers (2005) <doi:10.1007/s11222-005-4073-8> and Ameijeiras-Alonso and Einbeck (2023) <doi:10.1007/s11634-023-00575-1>.

Maintained by Jochen Einbeck. Last updated 7 months ago.

13.5 match 3.09 score 35 scripts 1 dependents

cran

fBasics:Rmetrics - Markets and Basic Statistics

Provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Even more there are several utility functions for data handling and management.

Maintained by Georgi N. Boshnakov. Last updated 7 months ago.

5.8 match 2 stars 7.11 score 129 dependents

fishfollower

stockassessment:State-Space Assessment Model

Fitting SAM...

Maintained by Anders Nielsen. Last updated 13 days ago.

stockassessment cpp

5.3 match 49 stars 7.76 score 324 scripts 2 dependents

bxc147

Epi:Statistical Analysis in Epidemiology

Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data. In particular representation, manipulation, rate estimation and simulation for multistate data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages. Contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data and some useful functions for tabulation and plotting, as well as a number of epidemiological data sets.

Maintained by Bendix Carstensen. Last updated 2 months ago.

4.2 match 4 stars 9.65 score 708 scripts 11 dependents

pbreheny

grpreg:Regularization Paths for Regression Models with Grouped Covariates

Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge. For more information, see Breheny and Huang (2009) <doi:10.4310/sii.2009.v2.n3.a10>, Huang, Breheny, and Ma (2012) <doi:10.1214/12-sts392>, Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>, and Breheny (2015) <doi:10.1111/biom.12300>, or visit the package homepage <https://pbreheny.github.io/grpreg/>.

Maintained by Patrick Breheny. Last updated 16 days ago.

3.5 match 34 stars 11.38 score 192 scripts 34 dependents

jared-fowler

prettyglm:Pretty Summaries of Generalized Linear Model Coefficients

One of the main advantages of using Generalised Linear Models is their interpretability. The goal of 'prettyglm' is to provide a set of functions which easily create beautiful coefficient summaries which can readily be shared and explained. 'prettyglm' helps users create coefficient summaries which include categorical base levels, variable importance and type III p.values. 'prettyglm' also creates beautiful relativity plots for categorical, continuous and splined coefficients.

Maintained by Jared Fowler. Last updated 1 years ago.

classification classification-model data-science data-visualization glm linear-models regression regression-analysis regression-model regression-models statistical-models

8.3 match 3 stars 4.73 score 36 scripts

gamlss-dev

gamlss:Generalized Additive Models for Location Scale and Shape

Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.

Maintained by Mikis Stasinopoulos. Last updated 4 months ago.

3.5 match 16 stars 11.23 score 2.0k scripts 49 dependents

tidymodels

broom:Convert Statistical Objects into Tidy Tibbles

Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.

Maintained by Simon Couch. Last updated 4 months ago.

modeling tidy-data

1.8 match 1.5k stars 21.56 score 37k scripts 1.4k dependents

cran

interp:Interpolation Methods

Bivariate data interpolation on regular and irregular grids, either linear or using splines are the main part of this package. It is intended to provide FOSS replacement functions for the ACM licensed akima::interp and tripack::tri.mesh functions. Linear interpolation is implemented in interp::interp(..., method="linear"), this corresponds to the call akima::interp(..., linear=TRUE) which is the default setting and covers most of akima::interp use cases in depending packages. A re-implementation of Akimas irregular grid spline interpolation (akima::interp(..., linear=FALSE)) is now also available via interp::interp(..., method="akima"). Estimators for partial derivatives are now also available in interp::locpoly(), these are a prerequisite for the spline interpolation. The basic part is a GPLed triangulation algorithm (sweep hull algorithm by David Sinclair) providing the starting point for the irregular grid interpolator. As side effect this algorithm is also used to provide replacements for almost all functions of the tripack package which also suffers from the same ACM license restrictions. All functions are designed to be backward compatible with their akima / tripack counterparts.

Maintained by Albrecht Gebhardt. Last updated 1 years ago.

fortran cpp

4.2 match 1 stars 9.06 score 228 scripts 286 dependents

jobnmadu

Dyn4cast:Dynamic Modeling and Machine Learning Environment

Estimates, predict and forecast dynamic models as well as Machine Learning metrics which assists in model selection for further analysis. The package also have capabilities to provide tools and metrics that are useful in machine learning and modeling. For example, there is quick summary, percent sign, Mallow's Cp tools and others. The ecosystem of this package is analysis of economic data for national development. The package is so far stable and has high reliability and efficiency as well as time-saving.

Maintained by Job Nmadu. Last updated 6 days ago.

data-science equal-lenght-forecast forecasting knots machine-learning nigeria prediction regression-models spline-models statistics time-series

7.5 match 4 stars 5.06 score 38 scripts

plambertuliege

cubicBsplines:Computation of a Cubic B-Spline Basis and Its Derivatives

Computation of a cubic B-spline basis for arbitrary knots. It also provides the 1st and 2nd derivatives, as well as the integral of the basis elements. It is used by the author to fit penalized B-spline models, see e.g. Jullion, A. and Lambert, P. (2006) <doi:10.1016/j.csda.2006.09.027>, Lambert, P. and Eilers, P.H.C. (2009) <doi:10.1016/j.csda.2008.11.022> and, more recently, Lambert, P. (2021) <doi:10.1016/j.csda.2021.107250>. It is inspired by the algorithm developed by de Boor, C. (1977) <doi:10.1137/0714026>.

Maintained by Philippe Lambert. Last updated 2 years ago.

fortran

10.3 match 1 stars 3.65 score 2 scripts 3 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

5.3 match 145 stars 7.09 score 50 scripts 2 dependents

robjhyndman

forecast:Forecasting Functions for Time Series and Linear Models

Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

Maintained by Rob Hyndman. Last updated 7 months ago.

forecast forecasting openblas cpp

2.0 match 1.1k stars 18.63 score 16k scripts 239 dependents

ropensci

dynamite:Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data

Easy-to-use and efficient interface for Bayesian inference of complex panel (time series) data using dynamic multivariate panel models by Helske and Tikka (2024) <doi:10.1016/j.alcr.2024.100617>. The package supports joint modeling of multiple measurements per individual, time-varying and time-invariant effects, and a wide range of discrete and continuous distributions. Estimation of these dynamic multivariate panel models is carried out via 'Stan'. For an in-depth tutorial of the package, see (Tikka and Helske, 2024) <doi:10.48550/arXiv.2302.01607>.

Maintained by Santtu Tikka. Last updated 19 days ago.

bayesian-inference panel-data stan statistical-models

4.7 match 29 stars 7.92 score 20 scripts

mmaechler

sfsmisc:Utilities from 'Seminar fuer Statistik' ETH Zurich

Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, some of which were ported from S-plus in the 1990s. For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole().

Maintained by Martin Maechler. Last updated 5 months ago.

3.4 match 11 stars 10.87 score 566 scripts 119 dependents

refunders

refund:Regression with Functional Data

Methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.

Maintained by Julia Wrobel. Last updated 6 months ago.

3.5 match 41 stars 10.25 score 472 scripts 16 dependents

mstrimas

smoothr:Smooth and Tidy Spatial Features

Tools for smoothing and tidying spatial features (i.e. lines and polygons) to make them more aesthetically pleasing. Smooth curves, fill holes, and remove small fragments from lines and polygons.

Maintained by Matthew Strimas-Mackey. Last updated 2 years ago.

gis spatial

3.8 match 100 stars 9.53 score 440 scripts 9 dependents

oucru-modelling

serosv:Model Infectious Disease Parameters from Serosurveys

An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) <doi:10.1007/978-1-4614-4072-7>.

Maintained by Anh Phan Truong Quynh. Last updated 1 months ago.

cpp

5.4 match 6.58 score 24 scripts

robjhyndman

demography:Forecasting Mortality, Fertility, Migration and Population Data

Functions for demographic analysis including lifetable calculations; Lee-Carter modelling; functional data analysis of mortality rates, fertility rates, net migration numbers; and stochastic population forecasting.

Maintained by Rob Hyndman. Last updated 3 months ago.

actuarial demography forecasting

4.3 match 74 stars 8.21 score 241 scripts 6 dependents

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

3.4 match 100 stars 9.94 score 748 scripts 7 dependents

egpivo

SpatMCA:Regularized Spatial Maximum Covariance Analysis

Provide regularized maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D (Wang and Huang, 2018 <doi:10.1002/env.2481>).

Maintained by Wen-Ting Wang. Last updated 7 months ago.

admm cca cross-covariance lasso matrix-factorization rcpparmadillo rcppparallel splines openblas cpp openmp

10.0 match 5 stars 3.40 score 4 scripts

mikeblazanin

gcplyr:Wrangle and Analyze Growth Curve Data

Easy wrangling and model-free analysis of microbial growth curve data, as commonly output by plate readers. Tools for reshaping common plate reader outputs into 'tidy' formats and merging them with design information, making data easy to work with using 'gcplyr' and other packages. Also streamlines common growth curve processing steps, like smoothing and calculating derivatives, and facilitates model-free characterization and analysis of growth data. See methods at <https://mikeblazanin.github.io/gcplyr/>.

Maintained by Mike Blazanin. Last updated 2 months ago.

dplyr ggplot2 tidyverse

4.3 match 30 stars 7.90 score 75 scripts

hugaped

MBNMAdose:Dose-Response MBNMA Models

Fits Bayesian dose-response model-based network meta-analysis (MBNMA) that incorporate multiple doses within an agent by modelling different dose-response functions, as described by Mawdsley et al. (2016) <doi:10.1002/psp4.12091>. By modelling dose-response relationships this can connect networks of evidence that might otherwise be disconnected, and can improve precision on treatment estimates. Several common dose-response functions are provided; others may be added by the user. Various characteristics and assumptions can be flexibly added to the models, such as shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting at the treatment level.

Maintained by Hugo Pedder. Last updated 1 months ago.

jags cpp

5.1 match 10 stars 6.60 score

berrij

profoc:Probabilistic Forecast Combination Using CRPS Learning

Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <doi:10.48550/arXiv.2102.00968> <doi:10.1016/j.jeconom.2021.11.008>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <doi:10.48550/arXiv.1404.1356>. Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization <https://github.com/kthohr/optim>.

Maintained by Jonathan Berrisch. Last updated 6 months ago.

openblas cpp openmp

5.8 match 14 stars 5.74 score 13 scripts

drizopoulos

JMbayes:Joint Modeling of Longitudinal and Time-to-Event Data under a Bayesian Approach

Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.

Maintained by Dimitris Rizopoulos. Last updated 4 years ago.

joint-models longitudinal-responses prediction-model survival-analysis openblas cpp openmp jags

4.6 match 60 stars 6.98 score 80 scripts

briencj

asremlPlus:Augments 'ASReml-R' in Fitting Mixed Models and Packages Generally in Exploring Prediction Differences

Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Procedures are available for choosing models that conform to the hierarchy or marginality principle, for fitting and choosing between two-dimensional spatial models using correlation, natural cubic smoothing spline and P-spline models. A history of the fitting of a sequence of models is kept in a data frame. Also used to compute functions and contrasts of, to investigate differences between and to plot predictions obtained using any model fitting function. The content falls into the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model selection and description functions, (iv) Model diagnostics and simulation functions, (v) Prediction production and presentation functions, (vi) Response transformation functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a computationally efficient algorithm for fitting a wide range of linear mixed models using Residual Maximum Likelihood. It is a commercial package and a license for it can be purchased from 'VSNi' <https://vsni.co.uk/> as 'asreml-R', who will supply a zip file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed for functions that are methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be installed from <http://chris.brien.name/rpackages/>.

Maintained by Chris Brien. Last updated 27 days ago.

asreml mixed-models

3.4 match 19 stars 9.34 score 200 scripts

nicholasjclark

mvgam:Multivariate (Dynamic) Generalized Additive Models

Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.

Maintained by Nicholas J Clark. Last updated 4 hours ago.

bayesian-statistics dynamic-factor-models ecological-modelling forecasting gaussian-process generalised-additive-models generalized-additive-models joint-species-distribution-modelling multilevel-models multivariate-timeseries stan time-series-analysis timeseries vector-autoregression vectorautoregression cpp

3.2 match 139 stars 9.85 score 117 scripts

dmphillippo

multinma:Bayesian Network Meta-Analysis of Individual and Aggregate Data

Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using 'Stan'.

Maintained by David M. Phillippo. Last updated 2 days ago.

statistics cpp

3.5 match 35 stars 9.11 score 163 scripts

hugaped

MBNMAtime:Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models

Fits Bayesian time-course models for model-based network meta-analysis (MBNMA) that allows inclusion of multiple time-points from studies. Repeated measures over time are accounted for within studies by applying different time-course functions, following the method of Pedder et al. (2019) <doi:10.1002/jrsm.1351>. The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple treatment comparisons. Several general time-course functions are provided; others may be added by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting.

Maintained by Hugo Pedder. Last updated 1 months ago.

jags cpp

5.1 match 7 stars 6.10 score

actuaryzhang

cplm:Compound Poisson Linear Models

Likelihood-based and Bayesian methods for various compound Poisson linear models based on Zhang, Yanwei (2013) <doi:10.1007/s11222-012-9343-7>.

Maintained by Yanwei (Wayne) Zhang. Last updated 1 years ago.

openblas

3.6 match 16 stars 8.45 score 75 scripts 10 dependents

cran

joint.Cox:Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis

Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death. Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model. The methods are applicable for meta-analytic data containing individual-patient information from several studies. Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). Methodologies were published in Emura et al. (2017) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>, Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>, Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>. See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>. Survival data from ovarian cancer patients are also available.

Maintained by Takeshi Emura. Last updated 3 years ago.

11.3 match 5 stars 2.71 score 17 scripts 2 dependents

bioc

monocle:Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq

Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well.

Maintained by Cole Trapnell. Last updated 5 months ago.

immunooncology sequencing rnaseq geneexpression differentialexpression infrastructure dataimport datarepresentation visualization clustering multiplecomparison qualitycontrol cpp

3.4 match 8.89 score 1.6k scripts 2 dependents

bioc

Rgraphviz:Provides plotting capabilities for R graph objects

Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package.

Maintained by Kasper Daniel Hansen. Last updated 3 days ago.

graphandnetwork visualization zlib

3.0 match 10.14 score 1.2k scripts 109 dependents

rapporter

pander:An R 'Pandoc' Writer

Contains some functions catching all messages, 'stdout' and other useful information while evaluating R code and other helpers to return user specified text elements (like: header, paragraph, table, image, lists etc.) in 'pandoc' markdown or several type of R objects similarly automatically transformed to markdown format. Also capable of exporting/converting (the resulting) complex 'pandoc' documents to e.g. HTML, 'PDF', 'docx' or 'odt'. This latter reporting feature is supported in brew syntax or with a custom reference class with a smarty caching 'backend'.

Maintained by Gergely Daróczi. Last updated 15 days ago.

literate-programming markdown pandoc pandoc-markdown reproducible-research rmarkdown cpp

1.8 match 297 stars 16.60 score 7.6k scripts 108 dependents

s-baumann

schumaker:Schumaker Shape-Preserving Spline

This is a shape preserving spline <doi:10.1137/0720057> which is guaranteed to be monotonic and concave or convex if the data is monotonic and concave or convex. It does not use any optimisation and is therefore quick and smoothly converges to a fixed point in economic dynamics problems including value function iteration. It also automatically gives the first two derivatives of the spline and options for determining behaviour when evaluated outside the interpolation domain.

Maintained by Stuart Baumann. Last updated 4 years ago.

13.2 match 2.26 score 18 scripts

gmelloni

interactionRCS:Calculate Estimates in Models with Interaction

A tool to calculate and plot estimates from models in which an interaction between the main predictor and a continuous covariate has been specified. Methods used in the package refer to Harrell Jr FE (2015, ISBN:9783319330396); Durrleman S, Simon R. (1989) <doi:10.1002/sim.4780080504>; Greenland S. (1995) <doi:10.1097/00001648-199507000-00005>.

Maintained by Giorgio Melloni. Last updated 7 months ago.

6.4 match 5 stars 4.70 score 5 scripts

edzer

spacetime:Classes and Methods for Spatio-Temporal Data

Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal selection and subsetting, as well as for spatial/temporal/spatio-temporal matching or aggregation, retrieving coordinates, print, summary, etc.

Maintained by Edzer Pebesma. Last updated 2 months ago.

2.3 match 74 stars 13.23 score 628 scripts 69 dependents

julia-wrobel

registr:Curve Registration for Exponential Family Functional Data

A method for performing joint registration and functional principal component analysis for curves (functional data) that are generated from exponential family distributions. This mainly implements the algorithms described in 'Wrobel et al. (2019)' <doi:10.1111/biom.12963> and further adapts them to potentially incomplete curves where (some) curves are not observed from the beginning and/or until the end of the common domain. Curve registration can be used to better understand patterns in functional data by separating curves into phase and amplitude variability. This software handles both binary and continuous functional data, and is especially applicable in accelerometry and wearable technology.

Maintained by Julia Wrobel. Last updated 3 years ago.

openblas cpp

4.7 match 16 stars 6.27 score 29 scripts

tidymodels

parsnip:A Common API to Modeling and Analysis Functions

A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', 'H2O', etc).

Maintained by Max Kuhn. Last updated 4 days ago.

1.8 match 612 stars 16.37 score 3.4k scripts 69 dependents

cran

assist:A Suite of R Functions Implementing Spline Smoothing Techniques

Fit various smoothing spline models. Includes an ssr() function for smoothing spline regression, an nnr() function for nonparametric nonlinear regression, an snr() function for semiparametric nonlinear regression, an slm() function for semiparametric linear mixed-effects models, and an snm() function for semiparametric nonlinear mixed-effects models. See Wang (2011) <doi:10.1201/b10954> for an overview.

Maintained by Yuedong Wang. Last updated 2 years ago.

fortran openblas

29.2 match 1.00 score

briencj

dae:Functions Useful in the Design and ANOVA of Experiments

The content falls into the following groupings: (i) Data, (ii) Factor manipulation functions, (iii) Design functions, (iv) ANOVA functions, (v) Matrix functions, (vi) Projector and canonical efficiency functions, and (vii) Miscellaneous functions. There is a vignette describing how to use the design functions for randomizing and assessing designs available as a vignette called 'DesignNotes'. The ANOVA functions facilitate the extraction of information when the 'Error' function has been used in the call to 'aov'. The package 'dae' can also be installed from <http://chris.brien.name/rpackages/>.

Maintained by Chris Brien. Last updated 3 months ago.

3.3 match 1 stars 8.62 score 356 scripts 7 dependents

agalecki

lmeSplines:Add Smoothing Spline Modelling Capability to `nlme`

Adds smoothing spline modelling capability to nlme. Fits smoothing spline terms in Gaussian linear and nonlinear mixed-effects models.

Maintained by Andrzej Galecki. Last updated 3 years ago.

7.5 match 3.78 score 9 scripts 4 dependents

japal

zCompositions:Treatment of Zeros, Left-Censored and Missing Values in Compositional Data Sets

Principled methods for the imputation of zeros, left-censored and missing data in compositional data sets (Palarea-Albaladejo and Martin-Fernandez (2015) <doi:10.1016/j.chemolab.2015.02.019>).

Maintained by Javier Palarea-Albaladejo. Last updated 9 months ago.

censored-data compositional-data imputation-methods missing-data nondetection

3.4 match 7 stars 8.40 score 370 scripts 11 dependents

splinesmoothing

mgss:A Matrix-Free Multigrid Preconditioner for Spline Smoothing

Data smoothing with penalized splines is a popular method and is well established for one- or two-dimensional covariates. The extension to multiple covariates is straightforward but suffers from exponentially increasing memory requirements and computational complexity. This toolbox provides a matrix-free implementation of a conjugate gradient (CG) method for the regularized least squares problem resulting from tensor product B-spline smoothing with multivariate and scattered data. It further provides matrix-free preconditioned versions of the CG-algorithm where the user can choose between a simpler diagonal preconditioner and an advanced geometric multigrid preconditioner. The main advantage is that all algorithms are performed matrix-free and therefore require only a small amount of memory. For further detail see Siebenborn & Wagner (2021).

Maintained by Martin Siebenborn. Last updated 4 years ago.

cpp

10.4 match 2.70 score 3 scripts

cran

intccr:Semiparametric Competing Risks Regression under Interval Censoring

Semiparametric regression models on the cumulative incidence function for interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) /doi{10.1002/sim.7350} and the models with missing event types as described in Park, Bakoyannis, Zhang, & Yiannoutsos (2021) \doi{10.1093/biostatistics/kxaa052}. The proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models.

Maintained by Jun Park. Last updated 3 years ago.

10.8 match 1 stars 2.57 score 37 scripts

marcvidalbadia

pfica:Independent Components Analysis Techniques for Functional Data

This package includes a set of tools to perform smoothed (and non-smoothed) principal/independent components analysis of functional data. Various functional pre-whitening approaches are implemented as discussed in Vidal and Aguilera (2022) “Novel whitening approaches in functional settings", <doi:10.1002/sta4.516>. Further whitening representations of functional data can be derived in terms of a few principal components, providing a powerful avenue to explore hidden structures in low dimensional settings: see Vidal, Rosso and Aguilera (2021) “Bi-smoothed functional independent component analysis for EEG artifact removal”, <doi:10.3390/math9111243>.

Maintained by Marc Vidal. Last updated 2 years ago.

b-splines fobi ica kurtosis penalization

9.3 match 2 stars 3.00 score 3 scripts

unina-sfere

adass:Adaptive Smoothing Spline (AdaSS) Estimator for the Function-on-Function Linear Regression

Implements the adaptive smoothing spline estimator for the function-on-function linear regression model described in Centofanti et al. (2023) <doi:10.1007/s00180-022-01223-6>.

Maintained by Fabio Centofanti. Last updated 8 months ago.

8.3 match 2 stars 3.30 score 20 scripts

ren1227

splineCox:A Two-Stage Estimation Approach to Cox Regression Using M-Spline Function

Implements a two-stage estimation approach for Cox regression using five-parameter M-spline functions to model the baseline hazard. It allows for flexible hazard shapes and model selection based on log-likelihood criteria.

Maintained by Ren Teranishi. Last updated 3 months ago.

6.6 match 4.18 score

cran

circularEV:Extreme Value Analysis for Circular Data

General functions for performing extreme value analysis on a circular domain as part of the statistical methodology in the paper by Konzen, E., Neves, C., and Jonathan, P. (2021). Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics, 32(4), e2667.

Maintained by Evandro Konzen. Last updated 3 years ago.

11.8 match 2.30 score 8 scripts

oswaldogressani

EpiLPS:A Fast and Flexible Bayesian Tool for Estimating Epidemiological Parameters

Estimation of epidemiological parameters with Laplacian-P-splines following the methodology of Gressani et al. (2022) <doi:10.1371/journal.pcbi.1010618>.

Maintained by Oswaldo Gressani. Last updated 5 months ago.

openblas cpp openmp

5.8 match 19 stars 4.69 score 17 scripts

projectmosaic

mosaic:Project MOSAIC Statistics and Mathematics Teaching Utilities

Data sets and utilities from Project MOSAIC (<http://www.mosaic-web.org>) used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all.

Maintained by Randall Pruim. Last updated 1 years ago.

2.0 match 93 stars 13.32 score 7.2k scripts 7 dependents

bioc

splineTimeR:Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction

This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks.

Maintained by Herbert Braselmann. Last updated 5 months ago.

geneexpression differentialexpression timecourse regression genesetenrichment networkenrichment networkinference graphandnetwork

6.5 match 4.01 score 17 scripts

briencj

growthPheno:Functional Analysis of Phenotypic Growth Data to Smooth and Extract Traits

Assists in the plotting and functional smoothing of traits measured over time and the extraction of features from these traits, implementing the SET (Smoothing and Extraction of Traits) method described in Brien et al. (2020) Plant Methods, 16. Smoothing of growth trends for individual plants using natural cubic smoothing splines or P-splines is available for removing transient effects and segmented smoothing is available to deal with discontinuities in growth trends. There are graphical tools for assessing the adequacy of trait smoothing, both when using this and other packages, such as those that fit nonlinear growth models. A range of per-unit (plant, pot, plot) growth traits or features can be extracted from the data, including single time points, interval growth rates and other growth statistics, such as maximum growth or days to maximum growth. The package also has tools adapted to inputting data from high-throughput phenotyping facilities, such from a Lemna-Tec Scananalyzer 3D (see <https://www.youtube.com/watch?v=MRAF_mAEa7E/> for more information). The package 'growthPheno' can also be installed from <http://chris.brien.name/rpackages/>.

Maintained by Chris Brien. Last updated 2 months ago.

4.0 match 6 stars 6.53 score 42 scripts

samhforbes

PupillometryR:A Unified Pipeline for Pupillometry Data

Provides a unified pipeline to clean, prepare, plot, and run basic analyses on pupillometry experiments.

Maintained by Samuel Forbes. Last updated 1 years ago.

3.4 match 44 stars 7.58 score 288 scripts 1 dependents

plambertuliege

ordgam:Additive Model for Ordinal Data using Laplace P-Splines

Additive proportional odds model for ordinal data using Laplace P-splines. The combination of Laplace approximations and P-splines enable fast and flexible inference in a Bayesian framework. Specific approximations are proposed to account for the asymmetry in the marginal posterior distributions of non-penalized parameters. For more details, see Lambert and Gressani (2023) <doi:10.1177/1471082X231181173> ; Preprint: <arXiv:2210.01668>).

Maintained by Philippe Lambert. Last updated 2 years ago.

8.5 match 3.02 score 21 scripts

bioc

gcrma:Background Adjustment Using Sequence Information

Background adjustment using sequence information

Maintained by Z. Wu. Last updated 5 months ago.

microarray onechannel preprocessing

3.5 match 7.28 score 164 scripts 11 dependents

cran

zetadiv:Functions to Compute Compositional Turnover Using Zeta Diversity

Functions to compute compositional turnover using zeta-diversity, the number of species shared by multiple assemblages. The package includes functions to compute zeta-diversity for a specific number of assemblages and to compute zeta-diversity for a range of numbers of assemblages. It also includes functions to explain how zeta-diversity varies with distance and with differences in environmental variables between assemblages, using generalised linear models, linear models with negative constraints, generalised additive models,shape constrained additive models, and I-splines.

Maintained by Guillaume Latombe. Last updated 3 years ago.

8.8 match 3 stars 2.89 score 64 scripts

plambertuliege

DALSM:Nonparametric Double Additive Location-Scale Model (DALSM)

Fit of a double additive location-scale model with a nonparametric error distribution from possibly right- or interval-censored data. The additive terms in the location and dispersion submodels, as well as the unknown error distribution in the location-scale model, are estimated using Laplace P-splines. For more details, see Lambert (2021) <doi:10.1016/j.csda.2021.107250>.

Maintained by Philippe Lambert. Last updated 8 days ago.

7.3 match 3.45 score 19 scripts

bioc

aroma.light:Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types

Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes.

Maintained by Henrik Bengtsson. Last updated 5 months ago.

infrastructure microarray onechannel twochannel multichannel visualization preprocessing bioconductor

3.9 match 1 stars 6.43 score 26 scripts 20 dependents

cran

pbs:Periodic B Splines

Periodic B Splines Basis

Maintained by swang1. Last updated 12 years ago.

7.0 match 3.57 score 23 dependents

boennecd

VAJointSurv:Variational Approximation for Joint Survival and Marker Models

Estimates joint marker (longitudinal) and survival (time-to-event) outcomes using variational approximations. The package supports multivariate markers allowing for correlated error terms and multiple types of survival outcomes which may be left-truncated, right-censored, and recurrent. Time-varying fixed and random covariate effects are supported along with non-proportional hazards.

Maintained by Benjamin Christoffersen. Last updated 2 months ago.

openblas cpp openmp

4.8 match 5 stars 5.20 score 21 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.

2.0 match 29 stars 12.34 score 6.6k scripts 931 dependents

florianhartig

DHARMa:Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models

The 'DHARMa' package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Currently supported are linear and generalized linear (mixed) models from 'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB', 'GLMMadaptive', and 'spaMM'; phylogenetic linear models from 'phylolm' (classes 'phylolm' and 'phyloglm'); generalized additive models ('gam' from 'mgcv'); 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial, phylogenetic and temporal autocorrelation.

Maintained by Florian Hartig. Last updated 12 days ago.

glmm regression regression-diagnostics residual

1.7 match 226 stars 14.74 score 2.8k scripts 10 dependents

bayesiandemography

bage:Bayesian Estimation and Forecasting of Age-Specific Rates

Fast Bayesian estimation and forecasting of age-specific rates, probabilities, and means, based on 'Template Model Builder'.

Maintained by John Bryant. Last updated 2 months ago.

cpp

3.3 match 3 stars 7.30 score 39 scripts

liamrevell

phytools:Phylogenetic Tools for Comparative Biology (and Other Things)

A wide range of methods for phylogenetic analysis - concentrated in phylogenetic comparative biology, but also including numerous techniques for visualizing, analyzing, manipulating, reading or writing, and even inferring phylogenetic trees. Included among the functions in phylogenetic comparative biology are various for ancestral state reconstruction, model-fitting, and simulation of phylogenies and trait data. A broad range of plotting methods for phylogenies and comparative data include (but are not restricted to) methods for mapping trait evolution on trees, for projecting trees into phenotype space or a onto a geographic map, and for visualizing correlated speciation between trees. Lastly, numerous functions are designed for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data. For instance, there are functions for computing consensus phylogenies from a set, for simulating phylogenetic trees and data under a range of models, for randomly or non-randomly attaching species or clades to a tree, as well as for a wide range of other manipulations and analyses that phylogenetic biologists might find useful in their research.

Maintained by Liam J. Revell. Last updated 27 days ago.

1.8 match 218 stars 13.85 score 4.8k scripts 76 dependents

cran

glober:Estimating Functions with Multivariate B-Splines

Generalized LassO applied to knot selection in multivariate B-splinE Regression (GLOBER) implements a novel approach for estimating functions in a multivariate nonparametric regression model based on an adaptive knot selection for B-splines using the Generalized Lasso. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2023), <arXiv:2306.00686>.

Maintained by Mary E. Savino. Last updated 2 years ago.

12.0 match 2.00 score 3 scripts

christophergandrud

simPH:Simulate and Plot Estimates from Cox Proportional Hazards Models

Simulates and plots quantities of interest (relative hazards, first differences, and hazard ratios) for linear coefficients, multiplicative interactions, polynomials, penalised splines, and non-proportional hazards, as well as stratified survival curves from Cox Proportional Hazard models. It also simulates and plots marginal effects for multiplicative interactions. Methods described in Gandrud (2015) <doi:10.18637/jss.v065.i03>.

Maintained by Christopher Gandrud. Last updated 3 years ago.

3.8 match 14 stars 6.23 score 48 scripts

bioc

moanin:An R Package for Time Course RNASeq Data Analysis

Simple and efficient workflow for time-course gene expression data, built on publictly available open-source projects hosted on CRAN and bioconductor. moanin provides helper functions for all the steps required for analysing time-course data using functional data analysis: (1) functional modeling of the timecourse data; (2) differential expression analysis; (3) clustering; (4) downstream analysis.

Maintained by Nelle Varoquaux. Last updated 5 months ago.

timecourse geneexpression rnaseq microarray differentialexpression clustering

5.8 match 4.15 score 14 scripts

jihx1015

MECfda:Scalar-on-Function Regression with Measurement Error Correction

Solve scalar-on-function linear models, including generalized linear mixed effect model and quantile linear regression model, and bias correction estimation methods due to measurement error. Details about the measurement error bias correction methods, see Luan et al. (2023) <doi:10.48550/arXiv.2305.12624>, Tekwe et al. (2022) <doi:10.1093/biostatistics/kxac017>, Zhang et al. (2023) <doi:10.5705/ss.202021.0246>, Tekwe et al. (2019) <doi:10.1002/sim.8179>.

Maintained by Heyang Ji. Last updated 9 days ago.

10.4 match 1 stars 2.30 score 1 scripts

bioc

limma:Linear Models for Microarray and Omics Data

Data analysis, linear models and differential expression for omics data.

Maintained by Gordon Smyth. Last updated 5 days ago.

exonarray geneexpression transcription alternativesplicing differentialexpression differentialsplicing genesetenrichment dataimport bayesian clustering regression timecourse microarray micrornaarray mrnamicroarray onechannel proprietaryplatforms twochannel sequencing rnaseq batcheffect multiplecomparison normalization preprocessing qualitycontrol biomedicalinformatics cellbiology cheminformatics epigenetics functionalgenomics genetics immunooncology metabolomics proteomics systemsbiology transcriptomics

1.7 match 13.81 score 16k scripts 585 dependents

bioc

lumi:BeadArray Specific Methods for Illumina Methylation and Expression Microarrays

The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays.

Maintained by Lei Huang. Last updated 5 months ago.

microarray onechannel preprocessing dnamethylation qualitycontrol twochannel

3.8 match 6.27 score 294 scripts 5 dependents

rkoenker

quantreg:Quantile Regression

Estimation and inference methods for models for conditional quantile functions: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also now included. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press, <doi:10.1017/CBO9780511754098> and Koenker, R. et al. (2017) Handbook of Quantile Regression, CRC Press, <doi:10.1201/9781315120256>.

Maintained by Roger Koenker. Last updated 6 days ago.

fortran openblas

1.7 match 18 stars 13.93 score 2.6k scripts 1.5k dependents

covaruber

lme4breeding:Relationship-Based Mixed-Effects Models

Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the 'lme4' machinery. The core computational algorithms are implemented using the 'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'.

Maintained by Giovanny Covarrubias-Pazaran. Last updated 21 days ago.

4.5 match 6 stars 5.23 score 7 scripts

bryanhanson

HiveR:2D and 3D Hive Plots for R

Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned. The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/). Keywords: networks, food webs, linnet, systems biology, bioinformatics.

Maintained by Bryan A. Hanson. Last updated 8 months ago.

3.4 match 72 stars 6.76 score 53 scripts 2 dependents

bioc

edgeR:Empirical Analysis of Digital Gene Expression Data in R

Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.

Maintained by Yunshun Chen. Last updated 5 days ago.

alternativesplicing batcheffect bayesian biomedicalinformatics cellbiology chipseq clustering coverage differentialexpression differentialmethylation differentialsplicing dnamethylation epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways proteomics qualitycontrol regression rnaseq sage sequencing singlecell systemsbiology timecourse transcription transcriptomics openblas

1.7 match 13.40 score 17k scripts 255 dependents

bstewart

stm:Estimation of the Structural Topic Model

The Structural Topic Model (STM) allows researchers to estimate topic models with document-level covariates. The package also includes tools for model selection, visualization, and estimation of topic-covariate regressions. Methods developed in Roberts et. al. (2014) <doi:10.1111/ajps.12103> and Roberts et. al. (2016) <doi:10.1080/01621459.2016.1141684>. Vignette is Roberts et. al. (2019) <doi:10.18637/jss.v091.i02>.

Maintained by Brandon Stewart. Last updated 1 years ago.

openblas cpp

1.8 match 404 stars 12.63 score 1.6k scripts 6 dependents

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 4 years ago.

7.5 match 2 stars 3.00 score 10 scripts

mcthedwards

bsplinePsd:Bayesian Nonparametric Spectral Density Estimation Using B-Spline Priors

Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series. The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented in Edwards, M.C., Meyer, R. and Christensen, N., Statistics and Computing (2018). <doi.org/10.1007/s11222-017-9796-9>.

Maintained by Matthew C. Edwards. Last updated 6 years ago.

cpp

8.3 match 1 stars 2.70 score 3 scripts

lassehjort

cuRe:Parametric Cure Model Estimation

Contains functions for estimating generalized parametric mixture and non-mixture cure models, loss of lifetime, mean residual lifetime, and crude event probabilities.

Maintained by Lasse Hjort Jakobsen. Last updated 2 years ago.

5.8 match 9 stars 3.90 score 22 scripts

krisrs1128

multimedia:Multimodal Mediation Analysis

Multimodal mediation analysis is an emerging problem in microbiome data analysis. Multimedia make advanced mediation analysis techniques easy to use, ensuring that all statistical components are transparent and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis. More details are available in Jiang et al. (2024) "multimedia: Multimodal Mediation Analysis of Microbiome Data" <doi:10.1101/2024.03.27.587024>.

Maintained by Kris Sankaran. Last updated 30 days ago.

coverage microbiome regression sequencing software statisticalmethod structuralequationmodels causal-inference data-integration mediation-analysis

4.0 match 1 stars 5.56 score 13 scripts

idslme

IDSL.IPA:Intrinsic Peak Analysis (IPA) for HRMS Data

A multi-layered untargeted pipeline for high-throughput LC/HRMS data processing to extract signals of organic small molecules. The package performs ion pairing, peak detection, peak table alignment, retention time correction, aligned peak table gap filling, peak annotation and visualization of extracted ion chromatograms (EICs) and total ion chromatograms (TICs). The 'IDSL.IPA' package was introduced in <doi:10.1021/acs.jproteome.2c00120> .

Maintained by Dinesh Barupal. Last updated 2 years ago.

exposome feature-detection lipidomics mass-spectrometry metabolomics peak-detection peak-picking small-molecule untargeted-metabolomics

4.5 match 13 stars 4.89 score 1 scripts 4 dependents

pavlakrotka

NCC:Simulation and Analysis of Platform Trials with Non-Concurrent Controls

Design and analysis of flexible platform trials with non-concurrent controls. Functions for data generation, analysis, visualization and running simulation studies are provided. The implemented analysis methods are described in: Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>, Saville et al. (2022) <doi:10.1177/17407745221112013> and Schmidli et al. (2014) <doi:10.1111/biom.12242>.

Maintained by Pavla Krotka. Last updated 6 days ago.

clinical-trials platform-trials simulation statistical-inference jags cpp

3.3 match 5 stars 6.64 score 29 scripts

projectmosaic

ggformula:Formula Interface to the Grammar of Graphics

Provides a formula interface to 'ggplot2' graphics.

Maintained by Randall Pruim. Last updated 12 months ago.

1.9 match 38 stars 11.60 score 1.7k scripts 25 dependents

bioc

goseq:Gene Ontology analyser for RNA-seq and other length biased data

Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data.

Maintained by Federico Marini. Last updated 5 months ago.

immunooncology sequencing go geneexpression transcription rnaseq differentialexpression annotation genesetenrichment kegg pathways software

2.3 match 1 stars 9.67 score 636 scripts 9 dependents

pierre-andre

ibr:Iterative Bias Reduction

Multivariate smoothing using iterative bias reduction with kernel, thin plate splines, Duchon splines or low rank splines.

Maintained by "Pierre-Andre Cornillon". Last updated 2 years ago.

openblas

16.9 match 1.28 score 19 scripts

mxrodriguezuvigo

SOP:Generalised Additive P-Spline Regression Models Estimation

Generalised additive P-spline regression models estimation using the separation of overlapping precision matrices (SOP) method. Estimation is based on the equivalence between P-splines and linear mixed models, and variance/smoothing parameters are estimated based on restricted maximum likelihood (REML). The package enables users to estimate P-spline models with overlapping penalties. Based on the work described in Rodriguez-Alvarez et al. (2015) <doi:10.1007/s11222-014-9464-2>; Rodriguez-Alvarez et al. (2019) <doi:10.1007/s11222-018-9818-2>, and Eilers and Marx (1996) <doi:10.1214/ss/1038425655>.

Maintained by Maria Xose Rodriguez-Alvarez. Last updated 1 years ago.

7.1 match 2.98 score 32 scripts 1 dependents

freezenik

bamlss:Bayesian Additive Models for Location, Scale, and Shape (and Beyond)

Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2021) <doi:10.18637/jss.v100.i04>.

Maintained by Nikolaus Umlauf. Last updated 5 months ago.

openblas

3.7 match 1 stars 5.76 score 239 scripts 5 dependents

squidlobster

castor:Efficient Phylogenetics on Large Trees

Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.

Maintained by Stilianos Louca. Last updated 4 months ago.

cpp

3.7 match 2 stars 5.75 score 450 scripts 9 dependents

ggobi

tourr:Tour Methods for Multivariate Data Visualisation

Implements geodesic interpolation and basis generation functions that allow you to create new tour methods from R.

Maintained by Dianne Cook. Last updated 16 days ago.

1.9 match 65 stars 11.17 score 426 scripts 9 dependents

jfrench

hero:Spatio-Temporal (Hero) Sandwich Smoother

An implementation of the sandwich smoother proposed in Fast Bivariate Penalized Splines by Xiao et al. (2012) <doi:10.1111/rssb.12007>. A hero is a specific type of sandwich. Dictionary.com (2018) <https://www.dictionary.com> describes a hero as: a large sandwich, usually consisting of a small loaf of bread or long roll cut in half lengthwise and containing a variety of ingredients, as meat, cheese, lettuce, and tomatoes. Also implements the spatio-temporal sandwich smoother of French and Kokoszka (2021) <doi:10.1016/j.spasta.2020.100413>.

Maintained by Joshua French. Last updated 2 years ago.

12.3 match 1.70 score 6 scripts

fate-ewi

bayesdfa:Bayesian Dynamic Factor Analysis (DFA) with 'Stan'

Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic factor analysis is a dimension reduction tool for multivariate time series. 'bayesdfa' extends conventional dynamic factor models in several ways. First, extreme events may be estimated in the latent trend by modeling process error with a student-t distribution. Second, alternative constraints (including proportions are allowed). Third, the estimated dynamic factors can be analyzed with hidden Markov models to evaluate support for latent regimes.

Maintained by Eric J. Ward. Last updated 5 months ago.

cpp

2.5 match 28 stars 8.28 score 101 scripts

cli9

face:Fast Covariance Estimation for Sparse Functional Data

We implement the Fast Covariance Estimation for Sparse Functional Data paper published in Statistics and Computing <doi: 10.1007/s11222-017-9744-8>.

Maintained by Cai Li. Last updated 3 years ago.

5.4 match 3.77 score 109 scripts 3 dependents

thl2019

qfa:Quantile-Frequency Analysis (QFA) of Time Series

Quantile-frequency analysis (QFA) of time series based on trigonometric quantile regression. Spline quantile regression (SQR) for regression coefficient estimation. References: [1] Li, T.-H. (2012) "Quantile periodograms", Journal of the American Statistical Association, 107, 765–776, <doi:10.1080/01621459.2012.682815>. [2] Li, T.-H. (2014) Time Series with Mixed Spectra, CRC Press, <doi:10.1201/b15154> [3] Li, T.-H. (2022) "Quantile Fourier transform, quantile series, and nonparametric estimation of quantile spectra", <doi:10.48550/arXiv.2211.05844>. [4] Li, T.-H. (2024) "Quantile crossing spectrum and spline autoregression estimation," <doi:10.48550/arXiv.2412.02513>. [5] Li, T.-H. (2024) "Spline autoregression method for estimation of quantile spectrum", <doi:10.48550/arXiv.2412.17163>.

Maintained by Ta-Hsin Li. Last updated 2 months ago.

fortran openblas

14.9 match 1.34 score 11 scripts

schaubert

catdata:Categorical Data

This R-package contains examples from the book "Regression for Categorical Data", Tutz 2012, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.

Maintained by Gunther Schauberger. Last updated 1 years ago.

3.0 match 6.61 score 158 scripts 2 dependents

iandryden

shapes:Statistical Shape Analysis

Routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons.

Maintained by Ian Dryden. Last updated 4 months ago.

2.3 match 7 stars 8.50 score 225 scripts 24 dependents

cran

multisensi:Multivariate Sensitivity Analysis

Functions to perform sensitivity analysis on a model with multivariate output.

Maintained by Hervé Monod. Last updated 7 years ago.

9.8 match 1 stars 2.00 score

cran

evmix:Extreme Value Mixture Modelling, Threshold Estimation and Boundary Corrected Kernel Density Estimation

The usual distribution functions, maximum likelihood inference and model diagnostics for univariate stationary extreme value mixture models are provided. Kernel density estimation including various boundary corrected kernel density estimation methods and a wide choice of kernels, with cross-validation likelihood based bandwidth estimator. Reasonable consistency with the base functions in the 'evd' package is provided, so that users can safely interchange most code.

Maintained by Carl Scarrott. Last updated 6 years ago.

6.8 match 2 stars 2.85 score 7 dependents

stla

interpolators:Some Interpolation Methods

Some interpolation methods taken from 'Boost': barycentric rational interpolation, modified Akima interpolation, PCHIP (piecewise cubic Hermite interpolating polynomial) interpolation, and Catmull-Rom splines.

Maintained by Stéphane Laurent. Last updated 1 years ago.

boost catmull-rom-spline interpolation rcpp cpp

7.2 match 2.70 score 9 scripts

macroeconomicdata

dateutils:Date Utils

Utilities for mixed frequency data. In particular, use to aggregate and normalize tabular mixed frequency data, index dates to end of period, and seasonally adjust tabular data.

Maintained by Seth Leonard. Last updated 3 years ago.

data-processing econometrics time-series openblas cpp

3.8 match 3 stars 5.17 score 49 scripts

grantmcdermott

tinyplot:Lightweight Extension of the Base R Graphics System

Lightweight extension of the base R graphics system, with support for automatic legends, facets, themes, and various other enhancements.

Maintained by Grant McDermott. Last updated 4 days ago.

2.0 match 310 stars 9.63 score 51 scripts 3 dependents

alexpkeil1

qgcomp:Quantile G-Computation

G-computation for a set of time-fixed exposures with quantile-based basis functions, possibly under linearity and homogeneity assumptions. This approach estimates a regression line corresponding to the expected change in the outcome (on the link basis) given a simultaneous increase in the quantile-based category for all exposures. Works with continuous, binary, and right-censored time-to-event outcomes. Reference: Alexander P. Keil, Jessie P. Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and Alexandra J. White (2019) A quantile-based g-computation approach to addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.

Maintained by Alexander Keil. Last updated 4 days ago.

exposure exposure-mixture exposure-mixtures quantile-gcomputation survival

2.2 match 37 stars 8.73 score 70 scripts 2 dependents

go-ski

clustra:Clustering Longitudinal Trajectories

Clusters longitudinal trajectories over time (can be unequally spaced, unequal length time series and/or partially overlapping series) on a common time axis. Performs k-means clustering on a single continuous variable measured over time, where each mean is defined by a thin plate spline fit to all points in a cluster. Distance is MSE across trajectory points to cluster spline. Provides graphs of derived cluster splines, silhouette plots, and Adjusted Rand Index evaluations of the number of clusters. Scales well to large data with multicore parallelism available to speed computation.

Maintained by George Ostrouchov. Last updated 3 months ago.

4.3 match 4.48 score 6 scripts

merck

psm3mkv:Evaluate Partitioned Survival and State Transition Models

Fits and evaluates three-state partitioned survival analyses (PartSAs) and Markov models (clock forward or clock reset) to progression and overall survival data typically collected in oncology clinical trials. These model structures are typically considered in cost-effectiveness modeling in advanced/metastatic cancer indications. Muston (2024). "Informing structural assumptions for three state oncology cost-effectiveness models through model efficiency and fit". Applied Health Economics and Health Policy.

Maintained by Dominic Muston. Last updated 9 months ago.

3.0 match 10 stars 6.43 score 1 scripts

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 8 days ago.

hades openjdk

1.8 match 190 stars 10.85 score 297 scripts

statist7

sitar:Super Imposition by Translation and Rotation Growth Curve Analysis

Functions for fitting and plotting SITAR (Super Imposition by Translation And Rotation) growth curve models. SITAR is a shape-invariant model with a regression B-spline mean curve and subject-specific random effects on both the measurement and age scales. The model was first described by Lindstrom (1995) <doi:10.1002/sim.4780141807> and developed as the SITAR method by Cole et al (2010) <doi:10.1093/ije/dyq115>.

Maintained by Tim Cole. Last updated 2 months ago.

2.2 match 13 stars 8.69 score 58 scripts 3 dependents

sciviews

pastecs:Package for Analysis of Space-Time Ecological Series

Regularisation, decomposition and analysis of space-time series. The pastecs R package is a PNEC-Art4 and IFREMER (Benoit Beliaeff <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 functionalities to R.

Maintained by Philippe Grosjean. Last updated 1 years ago.

ecology time-series

1.8 match 4 stars 10.34 score 2.1k scripts 13 dependents

r-forge

SpatialExtremes:Modelling Spatial Extremes

Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) <doi:10.1214/11-STS376>, Padoan et al. (2010) <doi:10.1198/jasa.2009.tm08577>, Dombry et al. (2013) <doi:10.1093/biomet/ass067>.

Maintained by Mathieu Ribatet. Last updated 11 months ago.

openblas

3.5 match 5.36 score 189 scripts 2 dependents

lbelzile

mev:Modelling of Extreme Values

Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.

Maintained by Leo Belzile. Last updated 5 months ago.

extreme-value-statistics likelihood-functions max-stable simulation threshold-selection openblas cpp openmp

2.3 match 13 stars 8.23 score 94 scripts 4 dependents

hoksanyip

SVMMaj:Implementation of the SVM-Maj Algorithm

Implements the SVM-Maj algorithm to train data with support vector machine <doi:10.1007/s11634-008-0020-9>. This algorithm uses two efficient updates, one for linear kernel and one for the nonlinear kernel.

Maintained by Hoksan Yip. Last updated 4 months ago.

5.5 match 1 stars 3.36 score 23 scripts

ecor

RMAWGEN:Multi-Site Auto-Regressive Weather GENerator

S3 and S4 functions are implemented for spatial multi-site stochastic generation of daily time series of temperature and precipitation. These tools make use of Vector AutoRegressive models (VARs). The weather generator model is then saved as an object and is calibrated by daily instrumental "Gaussianized" time series through the 'vars' package tools. Once obtained this model, it can it can be used for weather generations and be adapted to work with several climatic monthly time series.

Maintained by Emanuele Cordano. Last updated 26 days ago.

3.3 match 3 stars 5.62 score 115 scripts 4 dependents

bioc

TurboNorm:A fast scatterplot smoother suitable for microarray normalization

A fast scatterplot smoother based on B-splines with second-order difference penalty. Functions for microarray normalization of single-colour data i.e. Affymetrix/Illumina and two-colour data supplied as marray MarrayRaw-objects or limma RGList-objects are available.

Maintained by Maarten van Iterson. Last updated 5 months ago.

microarray onechannel twochannel preprocessing dnamethylation cpgisland methylationarray normalization

5.5 match 3.30 score 1 scripts

cran

npreg:Nonparametric Regression via Smoothing Splines

Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors (including random intercepts), interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for diagnostics, inference, and prediction.

Maintained by Nathaniel E. Helwig. Last updated 12 months ago.

18.1 match 1.00 score

functionaldata

fdapace:Functional Data Analysis and Empirical Dynamics

A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

Maintained by Yidong Zhou. Last updated 9 months ago.

cpp

1.6 match 31 stars 11.46 score 474 scripts 25 dependents