Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see <doi:10.1145/342009.335388>.

This package provides the necessary sample size for a longitudinal study with binary outcome in order to attain a pre-specified power while strictly maintaining the Type I error rate. Kapur K, Bhaumik R, Tang XC, Hur K, Reda DJ, Bhaumik D (2014) <doi:10.1002/sim.6203>.

Discovery of spatial patterns with Hidden Markov Random Field. This package is designed for spatial transcriptomic data and single molecule fluorescent in situ hybridization (FISH) data such as sequential fluorescence in situ hybridization (seqFISH) and multiplexed error-robust fluorescence in situ hybridization (MERFISH). The methods implemented in this package are described in Zhu et al. (2018) <doi:10.1038/nbt.4260>.

Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses.

Processes data from Molecular Dynamics simulations using Self Organising Maps. Features include the ability to read different input formats. Trajectories can be analysed to identify groups of important frames. Output visualisation can be generated for maps and pathways. Methodological details can be found in Motta S et al (2022) <doi:10.1021/acs.jctc.1c01163>. I/O functions for xtc format files were implemented using the xdrfile library available under open source license. The relevant information can be found in inst/COPYRIGHT.

Landsat satellites collect important data about global forest conditions. Documentation about Landsat's role in forest disturbance estimation is available at the site <https://landsat.gsfc.nasa.gov/>. By constrained quadratic B-splines, this package delivers an optimal shape-restricted trajectory to a time series of Landsat imagery for the purpose of modeling annual forest disturbance dynamics to behave in an ecologically sensible manner assuming one of seven possible "shapes", namely, flat, decreasing, one-jump (decreasing, jump up, decreasing), inverted vee (increasing then decreasing), vee (decreasing then increasing), linear increasing, and double-jump (decreasing, jump up, decreasing, jump up, decreasing). The main routine selects the best shape according to the minimum Bayes information criterion (BIC) or the cone information criterion (CIC), which is defined as the log of the estimated predictive squared error. The package also provides parameters summarizing the temporal pattern including year(s) of inflection, magnitude of change, pre- and post-inflection rates of growth or recovery. In addition, it contains routines for converting a flat map of disturbance agents to time-series disturbance maps and a graphical routine displaying the fitted trajectory of Landsat imagery.

This package contains functions that help to determine event boundaries in event segmentation experiments by bootstrapping a critical segmentation magnitude under the null hypothesis that all key presses were randomly distributed across the experiment. Segmentation magnitude is defined as the sum of Gaussians centered at the times of the segmentation key presses performed by the participants. Within a participant, the maximum of the overlaid Gaussians is used to prevent an excessive influence of a single participant on the overall outcome (e.g. if a participant is pressing the key multiple times in succession). Further functions are included, such as plotting the results.

Data sets used by Krause et al. (2022) <doi:10.1101/2022.04.11.487885>. It comprises phenotypic records obtained from the USDA Northern Region Uniform Soybean Tests from 1989 to 2019 for maturity groups II and III. In addition, soil and weather variables are provided for the 591 observed environments (combination of locations and years).

Computes clustering by fitting Gaussian mixture models (GMM) via stochastic approximation following the methods of Nguyen and Jones (2018) <doi:10.1201/9780429446177>. It also provides some test data generation and plotting functionality to assist with this process.

Improves the interpretation of the Standardized Precipitation Index under changing climate conditions. The package uses the nonstationary approach proposed in Blain et al. (2022) <doi:10.1002/joc.7550> to detect trends in rainfall quantities and to quantify the effect of such trends on the probability of a drought event occurring.

The sufficient forecasting (SF) method is implemented by this package for a single time series forecasting using many predictors and a possibly nonlinear forecasting function. Assuming that the predictors are driven by some latent factors, the SF first conducts factor analysis and then performs sufficient dimension reduction on the estimated factors to derive predictive indices for forecasting. The package implements several dimension reduction approaches, including principal components (PC), sliced inverse regression (SIR), and directional regression (DR). Methods for dimension reduction are as described in: Fan, J., Xue, L. and Yao, J. (2017) <doi:10.1016/j.jeconom.2017.08.009>, Luo, W., Xue, L., Yao, J. and Yu, X. (2022) <doi:10.1093/biomet/asab037> and Yu, X., Yao, J. and Xue, L. (2022) <doi:10.1080/07350015.2020.1813589>.

Used to construct the URLs and parameters of Socrata Open Data API <https://dev.socrata.com> calls, using the API's SoQL parameter format. Has method-chained and sensical syntax. Plays well with pipes.

This package provides a formula sub is a subformula of formula if all the terms on the right hand side of sub are terms of formula and their left hand sides are identical. This package aids in the creation of subformulas.

Calculates constant structure parameters of endocrine homeostatic systems from equilibrium hormone concentrations. Methods and equations have been described in Dietrich et al. (2012) <doi:10.1155/2012/351864> and Dietrich et al. (2016) <doi:10.3389/fendo.2016.00057>.

Interactive shiny application for working with Structural Equation Modelling technique. Runtime examples are provided in the package function as well as at <https://kartikeyab.shinyapps.io/semwebappk/> .

To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects (the default specification assumes no-time-effect) under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) <doi:10.1093/biostatistics/kxy031>, Li et al. (2018) <doi:10.1111/biom.12918>. Supplementary documents can be found at: <https://ysph.yale.edu/cmips/research/software/study-design-power-calculation/swdpwr/>. The Shiny app for swdpwr can be accessed at: <https://jiachenchen322.shinyapps.io/swdpwr_shinyapp/>. The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively.

Consolidated data simulation, sample size calculation and analysis functions for several snSMART (small sample sequential, multiple assignment, randomized trial) designs under one library. See Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732 <doi:10.1002/sim.7900>.

Browser notifications in Shiny apps, using toastr': <https://github.com/CodeSeven/toastr#readme>.

Access functionality of the heatmaply package through Shiny UI'.

It provides miscellaneous sequence analysis functions for describing episodes in individual sequences, measuring association between domains in multidimensional sequence analysis (see Piccarreta (2017) <doi:10.1177/0049124115591013>), heat maps of sequence data, Globally Interdependent Multidimensional Sequence Analysis (see Robette et al (2015) <doi:10.1177/0081175015570976>), smoothing sequences for index plots (see Piccarreta (2012) <doi:10.1177/0049124112452394>), coding sequences for Qualitative Harmonic Analysis (see Deville (1982)), measuring stress from multidimensional scaling factors (see Piccarreta and Lior (2010) <doi:10.1111/j.1467-985X.2009.00606.x>), symmetrical (or canonical) Partial Least Squares (see Bry (1996)).

This package contains methods to generate and evaluate semi-artificial data sets. Based on a given data set different methods learn data properties using machine learning algorithms and generate new data with the same properties. The package currently includes the following data generators: i) a RBF network based generator using rbfDDA() from package RSNNS', ii) a Random Forest based generator for both classification and regression problems iii) a density forest based generator for unsupervised data Data evaluation support tools include: a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM c) evaluation based on classification performance with various learning models, e.g., random forests.

Empirical likelihood methods for asymptotically efficient estimation of models based on conditional or unconditional moment restrictions; see Kitamura, Tripathi & Ahn (2004) <doi:10.1111/j.1468-0262.2004.00550.x> and Owen (2013) <doi:10.1002/cjs.11183>. Kernel-based non-parametric methods for density/regression estimation and numerical routines for empirical likelihood maximisation are implemented in Rcpp for speed.

Modifies the progress() function from httr package to let it send output to progressBar() function from shinyWidgets package. It is just a tweak at the original functions from httr package to make it smooth for shiny developers.

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).