This package provides a fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix.

This package provides a lavaan'-like syntax for OpenMx models. The syntax supports definition variables, bounds, and parameter transformations. This allows for latent growth curve models with person-specific measurement occasions, moderated nonlinear factor analysis and much more.

Focus-glue-context (FGC) fisheye transformations to two-dimensional coordinates and spatial vector geometries. Implements a smooth radial distortion that enlarges a focal region, transitions through a glue ring, and preserves outside context. Methods build on generalized fisheye views and focus+context mapping. For more details see Furnas (1986) <doi:10.1145/22339.22342>, Furnas (2006) <doi:10.1145/1124772.1124921> and Yamamoto et al. (2009) <doi:10.1145/1653771.1653788>.

Implementation of marginalized models for zero-inflated count data. This package provides a tool to implement an estimation algorithm for the marginalized count models, which directly makes inference on the effect of each covariate on the marginal mean of the outcome. The method involves the marginalized zero-inflated Poisson model described in Long et al. (2014) <doi:10.1002/sim.6293>.

Read, inspect and process corpus files for quantitative corpus linguistics. Obtain concordances via regular expressions, tokenize texts, and compute frequencies and association measures. Useful for collocation analysis, keywords analysis and variationist studies (comparison of linguistic variants and of linguistic varieties).

Sentiment analysis is a popular technique in text mining that attempts to determine the emotional state of some text. We provide a new implementation of a common method for computing sentiment, whereby words are scored as positive or negative according to a dictionary lookup. Then the sum of those scores is returned for the document. We use the Hu and Liu sentiment dictionary ('Hu and Liu', 2004) <doi:10.1145/1014052.1014073> for determining sentiment. The scoring function is vectorized by document, and scores for multiple documents are computed in parallel via OpenMP'.

The MCC-F1 analysis is a method to evaluate the performance of binary classifications. The MCC-F1 curve is more reliable than the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced ground truth. The MCC-F1 analysis also provides the MCC-F1 metric that integrates classifier performance over varying thresholds, and the best threshold of binary classification.

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

This package provides functions to compute and plot multivariate (partial) Mantel correlograms.

Allows users to simulate matrix population models with particular characteristics based on aspects of life history such as mortality trajectories and fertility trajectories. Also allows the exploration of sampling error due to small sample size.

In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. This technique can be employed to any half replicate of two level factorial run order where the number of factors are greater than two. For method details see, Bhowmik, A., Varghese, E., Jaggi, S. and Varghese, C. (2017) <doi:10.1080/03610926.2016.1152490>. This package generates all possible minimally changed two-level half-fractional factorial designs for different experimental setups along with various statistical criteria to measure the performance of these designs through a user-friendly interface. It consist of the function minimal.2halfFFD() which launches the application interface.

Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) <doi:10.1177/0962280206074463>. Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann <arXiv:1105.0828> to impute missing values in datasets. They have the added benefits of returning out of bag error and variable importance estimates, as well as being simple to run in parallel.

This package provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter & Gatsonis (2001) can be fitted. A new approach based to diagnostic meta-analysis of Holling et al. (2012) is also available. Standard methods like summary, plot and so on are provided.

Application of a test to rule out that trends detected in hydrological time series are explained exclusively by the randomness of the climate. Based on: Ricchetti, (2018) <https://repositorio.uchile.cl/handle/2250/168487>.

This package provides a bundle of functions for modifying MAESTRA/MAESPA input files,reading output files, and visualizing the stand in 3D. Handy for running sensitivity analyses, scenario analyses, etc.

This package provides functions to collapse a tidy data frame into matrices in a data frame and expand a data frame of matrices into a tidy data frame.

Bindings for hierarchical regression models for use with the parsnip package. Models include longitudinal generalized linear models (Liang and Zeger, 1986) <doi:10.1093/biomet/73.1.13>, and mixed-effect models (Pinheiro and Bates) <doi:10.1007/978-1-4419-0318-1_1>.

Use the open source MDB Tools utilities <https://github.com/mdbtools/mdbtools/>. Primarily used for converting proprietary Microsoft Access files to simple text files and then reading those as data frames.

Calculate predicted levels and marginal effects, using the delta method to calculate standard errors. This is an R-based version of the margins command from Stata.

Mass-balance-adjusted Regression algorithm for streamflow reconstruction at sub-annual resolution (e.g., seasonal or monthly). The algorithm implements a penalty term to minimize the differences between the total sub-annual flows and the annual flow. The method is described in Nguyen et al (2020) <DOI:10.1002/essoar.10504791.1>.

Modern model-based geostatistics for point-referenced data. This package provides a simple interface to run spatial machine learning models and geostatistical models that estimate a continuous (raster) surface from point-referenced outcomes and, optionally, a set of raster covariates. The package also includes functions to summarize raster outcomes by (polygon) region while preserving uncertainty.

Software to aid in modeling and analyzing mass-spectrometry-based proteome melting data. Quantitative data is imported and normalized and thermal behavior is modeled at the protein level. Methods exist for normalization, modeling, visualization, and export of results. For a general introduction to MS-based thermal profiling, see Savitski et al. (2014) <doi:10.1126/science.1255784>.

Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates models from discrete sequences. Supports model selection via information criteria and simulation of new sequences from an estimated model. See Bühlmann, P. and Wyner, A. J. (1999) <doi:10.1214/aos/1018031204> for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) <doi:10.1111/jtsa.12615> for VLMC with covariates.

This package provides a comprehensive collection of linkage methods for agglomerative hierarchical clustering on a matrix of proximity data (distances or similarities), returning a multifurcated dendrogram or multidendrogram. Multidendrograms can group more than two clusters when ties in proximity data occur, and therefore they do not depend on the order of the input data. Descriptive measures to analyze the resulting dendrogram are additionally provided. <doi:10.18637/jss.v114.i02>.