Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values and censored values are allowed. Independence is assumed over the single variables.

Download data from the Ada and Archibald MacLeish Field Station in Whately, MA. The Ada and Archibald MacLeish Field Station is a 260-acre patchwork of forest and farmland located in West Whately, MA that provides opportunities for faculty and students to pursue environmental research, outdoor education, and low-impact recreation (see <https://www.smith.edu/discover-smith/smith-action/sustainable-smith/macleish-field-station> for more information). This package contains weather data over several years, and spatial data on various man-made and natural structures.

Programmatic interface to several NASA Earth Observation OPeNDAP servers (Open-source Project for a Network Data Access Protocol) (<https://www.opendap.org/>). Allows for easy downloads of MODIS subsets, as well as other Earth Observation datacubes, in a time-saving and efficient way : by sampling it at the very downloading phase (spatially, temporally and dimensionally).

Administrative boundaries of Spain at several levels (Autonomous Communities and Cities, provinces, municipalities and NUTS'), based on GISCO from Eurostat <https://ec.europa.eu/eurostat/web/gisco> and CartoBase ANE from Instituto Geográfico Nacional <https://www.ign.es/>. Includes tools to download and process static map tiles and a leaflet plugin for Spanish public administration tile providers.

Several multivariate techniques from a biplot perspective. It is the translation (with many improvements) into R of the previous package developed in Matlab'. The package contains some of the main developments of my team during the last 30 years together with some more standard techniques. Package includes: Classical Biplots, HJ-Biplot, Canonical Biplots, MANOVA Biplots, Correspondence Analysis, Canonical Correspondence Analysis, Canonical STATIS-ACT, Logistic Biplots for binary and ordinal data, Multidimensional Unfolding, External Biplots for Principal Coordinates Analysis or Multidimensional Scaling, among many others. References can be found in the help of each procedure.

This package performs the execution of the main procedures of multiple comparisons in the literature, Scott-Knott (1974) <http://www.jstor.org/stable/2529204>, Batista (2016) <http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic representations and export to different extensions of its results. An additional part of the package is the presence of the performance evaluation of the tests (Type I error per experiment and the power). This will assist the user in making the decision for the chosen test.

Plug-in and difference-based long-run covariance matrix estimation for time series regression. Two applications of hypothesis testing are also provided. The first one is for testing for structural stability in coefficient functions. The second one is aimed at detecting long memory in time series regression. Lujia Bai and Weichi Wu (2024)<doi:10.3150/23-BEJ1680> Zhou Zhou and Wei Biao Wu(2010)<doi:10.1111/j.1467-9868.2010.00743.x> Jianqing Fan and Wenyang Zhang<doi:10.1214/aos/1017939139> Lujia Bai and Weichi Wu(2024)<doi:10.1093/biomet/asae013> Dimitris N. Politis, Joseph P. Romano, Michael Wolf(1999)<doi:10.1007/978-1-4612-1554-7> Weichi Wu and Zhou Zhou(2018)<doi:10.1214/17-AOS1582>.

Meta-analysis of generalized additive models and generalized additive mixed models. A typical use case is when data cannot be shared across locations, and an overall meta-analytic fit is sought. metagam provides functionality for removing individual participant data from models computed using the mgcv and gamm4 packages such that the model objects can be shared without exposing individual data. Furthermore, methods for meta-analysing these fits are provided. The implemented methods are described in Sorensen et al. (2020), <doi:10.1016/j.neuroimage.2020.117416>, extending previous works by Schwartz and Zanobetti (2000) and Crippa et al. (2018) <doi:10.6000/1929-6029.2018.07.02.1>.

Share R objects across processes on the same machine via a single copy in POSIX shared memory (Linux, macOS) or a Win32 file mapping (Windows). Every process reads from the same physical pages through the R Alternative Representation ('ALTREP') framework, giving lazy, zero-copy access. Shared objects serialize compactly as their shared memory name rather than their full contents.

This package provides a container for data used by the mapindia package. The data used by mapindia has been extracted into this package so that the file size of the mapindia package can be reduced considerably. The data in this package will be updated when latest data is available.

Implementation of the sampling and aggregation method for the covariate shift maximin effect, which was proposed in <doi:10.48550/arXiv.2011.07568>. It constructs the confidence interval for any linear combination of the high-dimensional maximin effect.

Analise multivariada, tendo funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), escalonamento multidimensional (MDS), analise discriminante linear (LDA) e quadratica (QDA), analise de cluster hierarquico e nao hierarquico, regressao linear simples e multipla, analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos, biplot, scatter plot, projection pursuit (PP), grant tour e outras funcoes uteis para a analise multivariada.

There are three different modules: (1) model fitting and selection using a set of the most commonly used equations describing developmental responses to temperature helped by already existing R packages ('rTPC') and nonlinear regression model functions from nls.multstart (Padfield et al. 2021, <doi:10.1111/2041-210X.13585>), with visualization of model predictions to guide ecological criteria for model selection; (2) calculation of suitability thermal limits, which consist on a temperature interval delimiting the optimal performance zone or suitability; and (3) climatic data extraction and visualization inspired on previous research (Taylor et al. 2019, <doi:10.1111/1365-2664.13455>), with either exportable rasters, static map images or html, interactive maps.

Fit Maximum Entropy Optimality Theory models to data sets, generate the predictions made by such models for novel data, and compare the fit of different models using a variety of metrics. The package is described in Mayer, C., Tan, A., Zuraw, K. (in press) <https://sites.socsci.uci.edu/~cjmayer/papers/cmayer_et_al_maxent_ot_accepted.pdf>.

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

Assessment of inconsistency in meta-analysis by calculating the Decision Inconsistency index (DI) and the Across-Studies Inconsistency (ASI) index. These indices quantify inconsistency taking into account outcome-level decision thresholds.

This package provides tools for privacy-preserving meta-analysis of function-valued quantities across heterogeneous studies. Implements the MetaHunt pipeline, including the denoised functional Successive Projection Algorithm (d-fSPA) for basis hunting, constrained weight estimation, Dirichlet regression of weights on study-level covariates, target prediction, and split/cross conformal prediction intervals. Operates on aggregate-level function evaluations, so individual-level data from source studies are not required. Methodology described in Shi, Imai, and Zhang (2026) <doi:10.48550/arXiv.2604.23847>.

This package implements Mander & Thompson's (2010) <doi:10.1016/j.cct.2010.07.008> methods for two-stage designs optimal under the alternative hypothesis for phase II [cancer] trials. Also provides an implementation of Simon's (1989) <doi:10.1016/0197-2456(89)90015-9> original methodology and allows exploration of the operating characteristics of sub-optimal designs.

An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups. Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary. Several methods were implemented to reduce the metabolomics matrix such as the principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9> , the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, the elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>. Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.

Modified functions of the package pcalg and some additional functions to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based causal discovery in incomplete and multiply imputed datasets. Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) <doi:10.1111/rssa.12565>; Andrews RM, Bang CW, Didelez V, Witte J, Foraita R (2021) <doi:10.1093/ije/dyae113>; Witte J, Foraita R, Didelez V (2022) <doi:10.1002/sim.9535>.

This package provides a new method to implement clustering from multiple modality data of certain samples, the function M2SMjF() jointly factorizes multiple similarity matrices into a shared sub-matrix and several modality private sub-matrices, which is further used for clustering. Along with this method, we also provide function to calculate the similarity matrix and function to evaluate the best cluster number from the original data.

This package implements the method to analyse weighted mobility networks or distribution networks as outlined in: Block, P., Stadtfeld, C., & Robins, G. (2022) <doi:10.1016/j.socnet.2021.08.003>. The purpose of the model is to analyse the structure of mobility, incorporating exogenous predictors pertaining to individuals and locations known from classical mobility analyses, as well as modelling emergent mobility patterns akin to structural patterns known from the statistical analysis of social networks.

Implement meta-analyses for simultaneously estimating individual means with shrinkage, isotonic regression and pretests. Include our original implementation of the isotonic regression via the pool-adjacent-violators algorithm (PAVA) algorithm. For the pretest estimator, the confidence interval for individual means are provided. Methodologies were published in Taketomi et al. (2021) <doi:10.3390/axioms10040267>, Taketomi et al. (2022) <doi:10.3390/a15010026>, Taketomi et al. (2023-) (under review).

Fast implementations of mathematical operations and performance metrics for multi-objective optimization, including filtering and ranking of dominated vectors according to Pareto optimality, hypervolume metric, C.M. Fonseca, L. Paquete, M. López-Ibáñez (2006) <doi:10.1109/CEC.2006.1688440>, epsilon indicator, inverted generational distance, computation of the empirical attainment function, V.G. da Fonseca, C.M. Fonseca, A.O. Hall (2001) <doi:10.1007/3-540-44719-9_15>, and Vorob'ev threshold, expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) <doi:10.1016/j.ejor.2014.07.032>, among others.