Analysis of Selection Index in Plant Breeding

The aim of most plant breeding programmes is simultaneous improvement of several characters. An objective method involving simultaneous selection for several attributes then becomes necessary. It has been recognised that most rapid improvements in the economic value is expected from selection applied simultaneously to all the characters which determine the economic value of a plant, and appropriate assigned weights to each character according to their economic importance, heritability and correlations between characters. So the selection for economic value is a complex matter. If the component characters are combined together into an index in such a way that when selection is applied to the index, as if index is the character to be improved, most rapid improvement of economic value is expected. Such an index was first proposed by Smith (1937 ) based on the Fisher's (1936 ) "discriminant function" Dabholkar (1999 < https://books.google.co.in/books?id=mlFtumAXQ0oC&lpg=PA4&ots=Xgxp1qLuxS&dq=elements%20of%20biometrical%20genetics&lr&pg=PP1#v=onepage&q&f=false>). In this package selection index is calculated based on the Smith (1937) selection index method.

Duration Curve Hydrological Model Indexes

Compute duration curves of daily flow series, both real and modeled, to be compared through indexes of flow duration curves. The package functions include comparative plots and goodness of fit tests. Flow duration curve indexes are based on: Yilmaz et al., (2008) .

An R Package for Single-Index Quantile Regression

Single-Index Quantile Regression is effective in some scenarios. We provides functions that allow users to fit Single-Index Quantile Regression model. It also provides functions to do prediction, estimate standard errors of the single-index coefficients via bootstrap, and visualize the estimated univariate function. Please see W., Y., Y. (2010) for details.

Divisia Monetary Aggregates Index

Functions to calculate Divisia monetary aggregates index as given in Barnett, W. A. (1980) ().

Preference Selection Index Method (PSIM)

The Preference Selection Index Method was created in (2010) and provides an innovative approach to determining the relative importance of criteria without pairwise comparisons, unlike the Analytic Hierarchy Process. The Preference Selection Index Method uses statistical methods to calculate the criteria weights and reflects their relative importance in the final decision-making process, offering an objective and non-subjective solution. This method is beneficial in multi-criteria decision analysis. The 'PSIM' package provides a practical and accessible tool for implementing the Preference Selection Index Method in R. It calculates the weights of criteria and makes the method available to researchers, analysts, and professionals without the need to develop complex calculations manually. More details about the Preference Selection Index Method can be found in Maniya K. and Bhatt M. G.(2010) .

Robust Effect Size Index (RESI) Estimation

Summarize model output using a robust effect size index. The index is introduced in Vandekar, Tao, & Blume (2020, ). Software paper available at .

Simulating the Development of h-Index Values

H-index and h-alpha are a bibliometric indicators. This package provides functions to simulate how these indicators may develop over time for a given set of researchers and to visualize the simulation data. The implementation is based on the 'STATA' ado h-index and is described in more detail in Bornmann et al. (2019) .

Indexed 'tibble' and Methods for 'dplyr'

Provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure, and support for tibbles and methods for 'dplyr'.

Superior Identification Index and Its Extensions

Calculate superior identification index and its extensions. Measure the performance of journals based on how well they could identify the top papers by any index (e.g. citation indices) according to Huang & Yang. (2022) . These methods could be extended to evaluate other entities such as institutes, countries, etc.

Seed Germination and Seedling Growth Indexes

Functions to calculate seed germination and seedling emergence and growth indexes. The main indexes for germination and seedling emergence, considering the time for seed germinate are: T10, T50 and T90, in Farooq et al. (2005) <10.1111/j.1744-7909.2005.00031.x>; and MGT, in Labouriau (1983). Considering the germination speed are: Germination Speed Index, in Maguire (1962), Mean Germination Rate, in Labouriau (1983); considering the homogeneity of germination are: Coefficient of Variation of the Germination Time, in Carvalho et al. (2005) <10.1590/S0100-84042005000300018>, and Variance of Germination, in Labouriau (1983); Uncertainty, in Labouriau and Valadares (1976) ; and Synchrony, in Primack (1980). The main seedling indexes are Growth, in Sako (2001), Uniformity, in Sako (2001) and Castan et al. (2018) ; and Vigour, in Medeiros and Pereira (2018) .