// "Whatever you do, work at it with all your heart, as working for the Lord,
// not for human masters, since you know that you will receive an inheritance
// from the Lord as a reward. It is the Lord Christ you are serving."
// (Col 3:23-24)

use std::{
    fmt::{Debug, Display, Formatter, Result as FmtResult},
    ops::Add,
    process::{Command, Stdio},
    str::FromStr,
};

use approx::assert_relative_eq;
use nalgebra::{DMatrix, Dim, IsContiguous, Matrix, Storage};
use num::{complex::Complex64, Float, ToPrimitive};

#[macro_export]
macro_rules! r_assert_relative_equal {
    ($lhs:expr, $rhs:expr, $max_relative:expr) => {{
        $crate::r::r_assert_relative_equal_($lhs, $rhs, $max_relative)
    }};
    ($lhs:expr, $rhs:expr) => {{
        r_assert_relative_equal!($lhs, $rhs, 1e-3)
    }};
}

pub fn r_assert_relative_equal_(mut lhs: f64, mut rhs: f64, max_relative: f64) {
    if lhs < 1e-30 {
        lhs = 0.0;
    }
    if rhs < 1e-30 {
        rhs = 0.0;
    }
    if lhs.is_finite() && rhs.is_finite() {
        if (lhs - rhs).abs() / lhs > max_relative && (lhs - rhs).abs() / rhs > max_relative {
            panic!(
                "Test Failed: r_assert_relative_equal\n\n\tleft          : {}\n\tright         : {}\n\tdiff          : {:e}\n\tmax_relative  : {:e}\n\n",
                lhs, rhs, (lhs - rhs).abs(), max_relative
            )
        }
    } else if lhs.is_nan() && rhs.is_nan()
        // Sometimes Rust can calculate things that R cannot
        || lhs.is_nan() && !rhs.is_nan()
    {
    } else if (lhs.is_finite() && !rhs.is_finite())
        || (!lhs.is_finite() && rhs.is_finite())
        || (lhs.is_sign_negative() && rhs.is_sign_positive())
        || (lhs.is_sign_positive() && rhs.is_sign_negative())
    {
        panic!(
            "Test Failed: r_assert_relative_equal\n\n\tleft  : {}\n\tright : {}\n\n",
            lhs, rhs
        )
    }
}

#[macro_export]
macro_rules! r_assert_relative_equal_result {
    ($lhs:expr, $rhs:expr, $max_relative:expr) => {{
        $crate::r::r_assert_relative_equal_result_(
            $lhs,
            Ok(num_traits::ToPrimitive::to_f64($rhs).unwrap()),
            $max_relative,
        )
    }};
    ($lhs:expr, $rhs:expr) => {{
        r_assert_relative_equal_result!($lhs, $rhs, 1e-3)
    }};
}

pub fn r_assert_relative_equal_result_(
    lhs: Result<f64, Box<dyn std::error::Error>>,
    rhs: Result<f64, Box<dyn std::error::Error>>,
    max_relative: f64,
) {
    match (lhs, rhs) {
        (Ok(o1), Ok(o2)) => {
            if !(o1.is_nan() && o2.is_nan()) {
                assert_relative_eq!(o1, o2, max_relative = max_relative)
            }
        }
        (Err(e), Ok(o)) => {
            if !o.is_nan() {
                panic!("Test Failed: r_assert_relative_equal_result\n\n\tleft  : Err({})\n\tright : Ok({})\n\n", e, o)
            }
        }
        r => panic!("Unexpected: {:?}", r),
    }
}

#[derive(Copy, Clone, Debug)]
pub enum RTesterError {
    NotFinished,
}

impl Display for RTesterError {
    fn fmt(&self, f: &mut Formatter) -> FmtResult {
        match self {
            RTesterError::NotFinished => write!(f, "R Tester stopped before finishing"),
        }
    }
}

impl std::error::Error for RTesterError {}

#[derive(Clone, Debug)]
pub struct RTester {
    seed: Option<RString>,
    rand_type: Option<RString>,
    libraries: RString,
    script: RString,
    display: RString,
    done: RString,
}

#[derive(Clone, Debug)]
pub struct RString(String);

impl Display for RString {
    fn fmt(&self, f: &mut Formatter) -> FmtResult {
        write!(f, "{}", self.0)
    }
}

impl Add for RString {
    type Output = Self;

    fn add(self, rhs: Self) -> Self::Output {
        Self(self.0 + rhs.0.as_str())
    }
}

impl FromStr for RString {
    type Err = ();

    fn from_str(s: &str) -> Result<Self, Self::Err> {
        Ok(Self(s.to_string()))
    }
}

impl RString {
    pub fn from_string(string: String) -> Self {
        Self(string)
    }

    pub fn empty() -> Self {
        Self(String::new())
    }

    pub fn push(self, string: &str) -> Self {
        Self(self.0 + string)
    }

    pub fn as_f64(&self) -> Result<f64, Box<dyn std::error::Error>> {
        let string = self.0.as_str();

        let string = if string.contains(']') {
            string
                .trim()
                .split("] ")
                .last()
                .unwrap()
                .replace(['<', '>'], "")
        } else {
            string.replace(['<', '>'], "")
        };
        match string.as_str() {
            "Inf" => Ok(f64::infinity()),
            "-Inf" => Ok(f64::NEG_INFINITY),
            "NaN" | "NA" => Ok(f64::nan()),
            _ => Ok(string.parse()?),
        }
    }

    pub fn as_complex64(&self) -> Result<Complex64, Box<dyn std::error::Error>> {
        let string = self.0.as_str();

        let string = if string.contains(']') {
            string
                .trim()
                .split("] ")
                .last()
                .unwrap()
                .replace(['<', '>'], "")
        } else {
            string.replace(['<', '>'], "")
        }
        .replace('i', "");

        let split_index = string
            .rfind('+')
            .filter(|i| *i != 0)
            .unwrap_or(string.rfind('-').filter(|i| *i != 0).unwrap_or(0));
        let split_string = string.split_at(split_index);

        let real = RString::from_str(split_string.0).unwrap().as_f64()?;
        let imaginary = RString::from_str(split_string.1).unwrap().as_f64()?;

        Ok(Complex64::new(real, imaginary))
    }

    pub fn as_complex64_vec(&self) -> Result<Vec<Complex64>, Box<dyn std::error::Error>> {
        let string = self.0.as_str();

        Ok(string
            .split_whitespace()
            .filter(|i| !i.contains('['))
            .filter_map(|s| RString::from_str(s).unwrap().as_complex64().ok())
            .collect())
    }

    pub fn as_f64_vec(&self) -> Result<Vec<f64>, Box<dyn std::error::Error>> {
        let string = self.0.as_str();

        Ok(string
            .split_whitespace()
            .filter_map(|s| RString::from_str(s).unwrap().as_f64().ok())
            .collect())
    }

    pub fn as_f64_matrix(&self) -> Result<DMatrix<f64>, Box<dyn std::error::Error>> {
        let string = self.0.as_str();
        let mut num_cols = 0;
        string.lines().take(1).for_each(|s| {
            num_cols = s.split_ascii_whitespace().count();
        });
        let num_rows = string.lines().skip(1).count();
        let mut ret = DMatrix::from_iterator(
            num_rows,
            num_cols,
            vec![0.0; num_rows * num_cols].into_iter(),
        );
        for (row, line) in string.lines().skip(1).enumerate() {
            for (col, val) in line.split_ascii_whitespace().skip(1).enumerate() {
                if let Some(x) = ret.get_mut((row, col)) {
                    *x = RString::from_str(val.trim()).unwrap().as_f64().unwrap();
                }
            }
        }

        Ok(ret)
    }

    pub fn as_bool(&self) -> Result<bool, Box<String>> {
        let str = self.0.as_str();

        if str.contains("TRUE") {
            Ok(true)
        } else if str.contains("FALSE") {
            Ok(false)
        } else {
            Err(Box::new(format!("{str} is not a bool")))
        }
    }

    pub fn as_lm_regression(&self) -> LMRegression {
        let summary = self.0.as_str();

        let mut coefficient_lines = Vec::new();
        let mut r_squared_line = String::new();
        let mut significance_line = String::new();

        for line in summary
            .trim()
            .split('\n')
            .skip_while(|line| !line.contains("Coefficients:"))
            .skip(2)
            .take_while(|line| !line.contains("---") && !line.is_empty())
        {
            coefficient_lines.push(line);
        }

        for line in summary.trim().split('\n') {
            if line.starts_with("Multiple R-squared") {
                r_squared_line = line.to_string();
            }
            if line.starts_with("F-statistic") {
                significance_line = line.to_string();
            }
        }

        let coefs = lm_summary_coefficients(&coefficient_lines);
        let r_squared = r_r_squared(&r_squared_line);
        let significance = r_significance(&significance_line);

        LMRegression {
            coefs: coefs
                .into_iter()
                .map(|(estimate, std_error, t, p)| LMCoefs {
                    estimate,
                    std_error,
                    t,
                    p,
                })
                .collect(),
            r_squared,
            f: significance.0,
            p: significance.1,
        }
    }

    pub fn as_rlm_regression(&self) -> RLMRegression {
        let summary = self.0.as_str();

        let mut coefficient_lines = Vec::new();

        let mut on_coefficients = false;
        for line in summary.trim().split('\n') {
            if line.contains("Coefficients:") {
                on_coefficients = true;
            } else if line.contains("---") || line.is_empty() {
                on_coefficients = false;
            } else if on_coefficients {
                coefficient_lines.push(line);
            }
        }

        let coefs = rlm_summary_coefficients(&coefficient_lines);

        RLMRegression {
            coefs: coefs
                .into_iter()
                .map(|(estimate, std_error, t)| RLMCoefs {
                    estimate,
                    std_error,
                    t,
                })
                .collect(),
        }
    }

    pub fn as_glm_regression(&self) -> GLMRegression {
        let summary = self.0.as_str();

        let mut coefficient_lines = Vec::new();
        let mut null_dev_line = String::new();
        let mut resid_dev_line = String::new();
        let mut aic_line = String::new();

        let mut on_coefficients = false;
        for line in summary.trim().split('\n') {
            if line.contains("Estimate") {
                on_coefficients = true;
            } else if line.is_empty() {
                on_coefficients = false;
            } else if on_coefficients {
                coefficient_lines.push(line);
            }
        }

        for line in summary.trim().split('\n') {
            if line.trim().starts_with("Null deviance") {
                null_dev_line = line.to_string();
            }
            if line.starts_with("Residual deviance") {
                resid_dev_line = line.to_string();
            }
            if line.starts_with("AIC") {
                aic_line = line.to_string();
            }
        }

        let coefs = lm_summary_coefficients(&coefficient_lines);
        let null_dev = r_null_deviance(&null_dev_line);
        let resid_dev = r_residual_deviance(&resid_dev_line);
        let aic = r_aic(&aic_line);

        GLMRegression {
            coefs: coefs
                .into_iter()
                .map(|(estimate, std_error, z, p)| GLMCoefs {
                    estimate,
                    std_error,
                    z,
                    p,
                })
                .collect(),
            null_dev,
            resid_dev,
            aic,
        }
    }

    pub fn as_r_fit(&self) -> RFitRegression {
        let summary = self.0.as_str();

        let mut coefficient_lines = Vec::new();
        let mut r_squared_line = String::new();
        let mut significance_line = String::new();
        let mut wald_line = String::new();

        for line in summary
            .trim()
            .split('\n')
            .skip_while(|line| !line.contains("Coefficients:"))
            .skip(2)
            .take_while(|line| !line.contains("---") && !line.is_empty())
        {
            coefficient_lines.push(line);
        }

        for line in summary.trim().split('\n') {
            if line.starts_with("Multiple R-squared (Robust)") {
                r_squared_line = line.to_string();
            }
            if line.starts_with("Reduction in Dispersion") {
                significance_line = line.to_string();
            }
            if line.starts_with("Overall Wald Test") {
                wald_line = line.to_string();
            }
        }

        let coefs = lm_summary_coefficients(&coefficient_lines);
        let r_squared_robust = r_r_squared(&r_squared_line);
        let red_dispersion = r_red_dispersion(&significance_line);
        let wald_test = r_wald_test(&wald_line);

        RFitRegression {
            coefs: coefs
                .into_iter()
                .map(|(estimate, std_error, t, p)| LMCoefs {
                    estimate,
                    std_error,
                    t,
                    p,
                })
                .collect(),
            r_squared_robust,
            red_disp_test: red_dispersion.0,
            disp_p: red_dispersion.1,
            wald_test: wald_test.0,
            wald_p: wald_test.1,
        }
    }

    pub fn as_one_way_r_fit(&self) -> Vec<OneWayRFit> {
        let summary = self.0.as_str();

        let mut ret = Vec::new();
        for line in summary
            .trim()
            .split('\n')
            .skip_while(|line| !line.contains("Estimate"))
            .skip(1)
        {
            let split = line.split_whitespace().collect::<Vec<_>>();
            ret.push(OneWayRFit {
                i: split[1].to_string(),
                j: split[2].to_string(),
                estimate: RString::from_str(split[3]).unwrap().as_f64().unwrap(),
                std_err: RString::from_str(split[4]).unwrap().as_f64().unwrap(),
                confidence_interval: (
                    RString::from_str(split[5]).unwrap().as_f64().unwrap(),
                    RString::from_str(split[6]).unwrap().as_f64().unwrap(),
                ),
            });
        }

        ret
    }

    pub fn as_raov(&self) -> Vec<RAOV> {
        let summary = self.0.as_str();

        let mut ret = Vec::new();
        for line in summary
            .trim()
            .split('\n')
            .skip_while(|line| !line.contains("Mean RD"))
            .skip(1)
        {
            let mut line = line.to_string();
            for i in 1..=20 {
                line = line.replace(&format!("x[, {i}]"), &format!("x[,{i}]"));
            }
            let split = line.split_whitespace().collect::<Vec<_>>();
            ret.push(RAOV {
                name: split[0].to_string(),
                df: RString::from_str(split[1]).unwrap().as_f64().unwrap(),
                rd: RString::from_str(split[2]).unwrap().as_f64().unwrap(),
                mean_rd: RString::from_str(split[3]).unwrap().as_f64().unwrap(),
                f: RString::from_str(split[4]).unwrap().as_f64().unwrap(),
                p: RString::from_str(split[5]).unwrap().as_f64().unwrap(),
            });
        }

        ret
    }

    pub fn from_f64_slice(v: &[f64]) -> Self {
        let length = v.len();

        let mut ret = "c(".to_string();

        for (i, v_i) in v.iter().take(length).enumerate() {
            ret += &v_i.to_string();
            if i < length - 1 {
                ret += ", "
            }
        }

        ret += ")";
        Self(ret)
    }

    pub fn from_complex64_slice(v: &[Complex64]) -> Self {
        let length = v.len();

        let mut ret = "c(".to_string();

        for (i, v_i) in v.iter().take(length).enumerate() {
            ret += &v_i.re.to_string();
            ret += "+";
            ret += &v_i.re.to_string();
            ret += "i";

            if i < length - 1 {
                ret += ", "
            }
        }

        ret += ")";
        Self(ret)
    }

    pub fn from_f64_matrix<R, C, S>(m: &Matrix<f64, R, C, S>) -> Self
    where
        R: Dim,
        C: Dim,
        S: Storage<f64, R, C> + IsContiguous,
    {
        let (row_size, col_size) = m.shape();
        let slice_string = RString::from_f64_slice(m.as_slice());
        let mut ret = "matrix(".to_string();
        ret += &slice_string.to_string();
        ret += &format!(", nrow={}, ncol={})", row_size, col_size);
        Self(ret)
    }

    pub fn from_bool(b: bool) -> Self {
        Self(match b {
            true => "TRUE".to_string(),
            false => "FALSE".to_string(),
        })
    }

    pub fn from_f64<F: ToPrimitive + std::fmt::Debug>(num: F) -> Self {
        let num = num.to_f64().unwrap_or(f64::nan());
        Self(if num.is_infinite() && num.is_sign_positive() {
            "Inf".to_string()
        } else if num.is_infinite() && num.is_sign_negative() {
            "-Inf".to_string()
        } else if num.is_nan() {
            "NaN".to_string()
        } else {
            format!("{}", num)
        })
    }

    pub fn from_library_name(lib_name: &str) -> Self {
        Self(String::from("library(\"") + lib_name + "\")")
    }
}

pub fn print_mat<R, C, S>(x: &Matrix<f64, R, C, S>)
where
    R: Dim,
    C: Dim,
    S: Storage<f64, R, C>,
{
    print!("     ");
    for i in 0..x.ncols() {
        print!("{:4} ", i);
    }
    println!();
    for i in 0..x.nrows() {
        print!("{:4} ", i);
        for j in 0..x.ncols() {
            print!("{:4} ", x[(i, j)]);
        }
        println!();
    }
}

const RSCRIPT: Option<&str> = option_env!("Rscript");

impl Default for RTester {
    fn default() -> Self {
        Self {
            seed: None,
            rand_type: None,
            libraries: RString::empty(),
            script: RString::empty(),
            display: RString::empty(),
            done: RString::from_str("R_TESTER_DONE").unwrap(),
        }
    }
}

impl RTester {
    pub fn new() -> Self {
        Self::default()
    }

    pub fn add_library(mut self, library: &RString) -> Self {
        self.libraries.0 += library.0.as_str();
        self.libraries.0 += ";";
        self
    }

    pub fn set_seed(mut self, seed: u16) -> Self {
        self.seed = Some(RString::from_f64(seed));
        self
    }

    pub fn set_rand_type(mut self, rand_type: &str) -> Self {
        self.rand_type = Some(RString::from_str(rand_type).unwrap());
        self
    }

    pub fn set_script(mut self, script: &RString) -> Self {
        self.script = script.clone();
        self
    }

    pub fn set_display(mut self, display: &RString) -> Self {
        self.display = display.clone();
        self
    }

    pub fn run(&self) -> Result<RString, Box<dyn std::error::Error>> {
        let seed_piece = match (self.seed.clone(), self.rand_type.clone()) {
            (Some(seed), Some(rand_type)) => {
                RString::from_string(format!("set.seed({}, '{}');", seed, rand_type))
            }
            (None, Some(rand_type)) => {
                RString::from_string(format!("set.seed(NULL, '{}');", rand_type))
            }
            (Some(seed), None) => {
                RString::from_string(format!("set.seed({}, 'Marsaglia-Multicarry');", seed))
            }
            (None, None) => RString::from_str("").unwrap(),
        };

        let script = format!(
            "options(warn=-1, show.error.messages=FALSE, digits=14, width=1e4, scipen=1);{}{}{}print({}, digits=14, dig.tst=14);cat('{}');",
            self.libraries.0, seed_piece.0, self.script.0, self.display.0, self.done.0,
        );
        let mut ret_string = String::from_utf8(
            Command::new(
                RSCRIPT
                    .ok_or("")
                    .expect("Environment variable `Rscript` not set"),
            )
            .args(["-e", &script])
            .stdout(Stdio::piped())
            .spawn()?
            .wait_with_output()?
            .stdout,
        )?;

        let done_message = (0..self.done.0.len())
            .filter_map(|_| ret_string.pop())
            .collect::<String>()
            .chars()
            .rev()
            .collect::<String>();
        if done_message == self.done.0 {
            Ok(RString(ret_string))
        } else {
            let error_script = format!(
                "options(digits=14, width=1e4);{}{}{}print({});cat('{}');",
                self.libraries.0, seed_piece.0, self.script.0, self.display.0, self.done.0,
            );
            let ret = Command::new(
                RSCRIPT
                    .ok_or("")
                    .expect("Environment variable `Rscript` not set"),
            )
            .args(["-e", &error_script])
            .stdout(Stdio::piped())
            .stderr(Stdio::piped())
            .spawn()?
            .wait_with_output()?;
            let output = String::from_utf8(ret.stdout)?;
            let error = String::from_utf8(ret.stderr)?;
            println!("Input:\n{}", script);
            println!("Output:\n{}", output);
            println!("Error:\n{}", error);
            Err(Box::new(RTesterError::NotFinished))
        }
    }
}

fn lm_summary_coefficients(lines: &[&str]) -> Vec<(f64, f64, f64, f64)> {
    lines
        .iter()
        .take_while(|s| !s.trim().is_empty())
        .filter_map(|line| {
            let nums = line
                .split_whitespace()
                .filter_map(|s| RString::from_str(s).unwrap().as_f64().ok())
                .collect::<Vec<_>>();
            if nums.len() == 4 {
                Some((nums[0], nums[1], nums[2], nums[3]))
            } else {
                None
            }
        })
        .collect()
}

fn rlm_summary_coefficients(lines: &[&str]) -> Vec<(f64, f64, f64)> {
    lines
        .iter()
        .skip(1)
        .filter(|l| l.split_ascii_whitespace().count() == 4)
        .map(|line| {
            let nums = line
                .split_whitespace()
                .filter_map(|s| RString::from_str(s).unwrap().as_f64().ok())
                .collect::<Vec<_>>();
            (nums[0], nums[1], nums[2])
        })
        .collect()
}

fn r_r_squared(line: &str) -> f64 {
    RString::from_str(line.split_whitespace().last().unwrap_or("0.0"))
        .unwrap()
        .as_f64()
        .expect("R^2 error")
}

fn r_significance(line: &str) -> (f64, f64) {
    let split_line = line.split_whitespace().collect::<Vec<_>>();
    (
        RString::from_str(split_line[1])
            .unwrap()
            .as_f64()
            .expect("F-statistic error"),
        RString::from_str(split_line.last().unwrap())
            .unwrap()
            .as_f64()
            .expect("P-value error"),
    )
}

fn r_null_deviance(line: &str) -> f64 {
    let split_line = line.split_whitespace().collect::<Vec<_>>();
    RString::from_str(split_line[2])
        .unwrap()
        .as_f64()
        .expect("Null deviance error")
}

fn r_residual_deviance(line: &str) -> f64 {
    let split_line = line.split_whitespace().collect::<Vec<_>>();
    RString::from_str(split_line[2])
        .unwrap()
        .as_f64()
        .expect("Residual deviance error")
}

fn r_aic(line: &str) -> f64 {
    let split_line = line.split_whitespace().collect::<Vec<_>>();
    RString::from_str(split_line[1])
        .unwrap()
        .as_f64()
        .expect("AIC deviance error")
}

fn r_red_dispersion(line: &str) -> (f64, f64) {
    let split_line = line.split_whitespace().collect::<Vec<_>>();
    (
        RString::from_str(split_line.get(4).unwrap_or(&"0.0"))
            .unwrap()
            .as_f64()
            .expect("Reduction in Dispersion Test"),
        RString::from_str(split_line.last().unwrap_or(&"0.0"))
            .unwrap()
            .as_f64()
            .expect("P Value"),
    )
}

fn r_wald_test(line: &str) -> (f64, f64) {
    let split_line = line.split_whitespace().collect::<Vec<_>>();
    (
        RString::from_str(split_line.get(3).unwrap_or(&"0.0"))
            .unwrap()
            .as_f64()
            .expect("Overall Wald Test"),
        RString::from_str(split_line.last().unwrap_or(&"0.0"))
            .unwrap()
            .as_f64()
            .expect("P Value"),
    )
}

#[derive(Copy, Clone, Debug, Default)]
pub struct RGenerator {
    seed: u16,
}

impl RGenerator {
    pub fn new() -> Self {
        Self::default()
    }

    pub fn set_seed(self, seed: u16) -> Self {
        Self { seed, ..self }
    }

    pub fn generate_uniform<T1: ToPrimitive + Debug, T2: ToPrimitive + Debug>(
        &self,
        len: T1,
        (lower_bound, upper_bound): (T2, T2),
    ) -> Vec<f64> {
        RTester::new()
            .set_seed(self.seed)
            .set_script(&RString::from_string(format!(
                "ret = runif({}, {}, {});",
                RString::from_f64(len),
                RString::from_f64(lower_bound),
                RString::from_f64(upper_bound),
            )))
            .set_display(&RString::from_str("ret").unwrap())
            .run()
            .expect("R running error")
            .as_f64_vec()
            .expect("R running error")
    }

    pub fn generate_uniform_matrix<
        T1: ToPrimitive + Debug,
        T2: ToPrimitive + Debug,
        T3: ToPrimitive + Debug + Clone,
    >(
        &self,
        num_rows: T1,
        num_columns: T2,
        (lower_bound, upper_bound): (T3, T3),
    ) -> DMatrix<f64> {
        let mut x = RTester::new()
            .set_seed(self.seed)
            .set_display(&RString::from_string(format!(
                "matrix(runif({0}*{1}, {2}, {3}), nrow={0}, ncol={1})",
                RString::from_f64(num_rows),
                RString::from_f64(num_columns),
                RString::from_f64(lower_bound.clone()),
                RString::from_f64(upper_bound.clone()),
            )))
            .run()
            .expect("R error")
            .as_f64_matrix()
            .expect("R array error");
        x.iter_mut().for_each(|x_i| {
            if x_i.is_nan() {
                *x_i = lower_bound.to_f64().unwrap()
                    + ((upper_bound.clone().to_f64().unwrap()
                        - lower_bound.clone().to_f64().unwrap())
                        / 2.0)
            }
        });
        x
    }

    pub fn generate_errors<
        T1: ToPrimitive + Debug,
        T2: ToPrimitive + Debug + Clone,
        T3: ToPrimitive + Debug,
    >(
        &self,
        len: T1,
        (lower_bound, upper_bound): (T2, T2),
        fuzz_amount: T3,
    ) -> Vec<f64> {
        let mut errors = RTester::new()
            .set_seed(self.seed)
            .set_display(&RString::from_string(format!(
                "rnorm({0}, {1}, {2}) + runif({0}, -{3}, {3})",
                RString::from_f64(len),
                RString::from_f64(lower_bound.clone()),
                RString::from_f64(upper_bound.clone()),
                RString::from_f64(fuzz_amount)
            )))
            .run()
            .expect("R error")
            .as_f64_vec()
            .expect("R array error");
        errors.iter_mut().for_each(|e| {
            if e.is_nan() {
                *e = lower_bound.clone().to_f64().unwrap()
                    + ((upper_bound.to_f64().unwrap() - lower_bound.clone().to_f64().unwrap())
                        / 2.0)
            }
        });
        errors
    }
}

#[derive(Clone, Debug)]
pub struct LMCoefs {
    pub estimate: f64,
    pub std_error: f64,
    pub t: f64,
    pub p: f64,
}

#[derive(Clone, Debug)]
pub struct RLMCoefs {
    pub estimate: f64,
    pub std_error: f64,
    pub t: f64,
}

#[derive(Clone, Debug)]
pub struct GLMCoefs {
    pub estimate: f64,
    pub std_error: f64,
    pub z: f64,
    pub p: f64,
}

#[derive(Clone, Debug)]
pub struct LMRegression {
    pub coefs: Vec<LMCoefs>,
    pub r_squared: f64,
    pub f: f64,
    pub p: f64,
}

#[derive(Clone, Debug)]
pub struct RLMRegression {
    pub coefs: Vec<RLMCoefs>,
}

#[derive(Clone, Debug)]
pub struct GLMRegression {
    pub coefs: Vec<GLMCoefs>,
    pub null_dev: f64,
    pub resid_dev: f64,
    pub aic: f64,
}

#[derive(Clone, Debug)]
pub struct RFitRegression {
    pub coefs: Vec<LMCoefs>,
    pub r_squared_robust: f64,
    pub red_disp_test: f64,
    pub disp_p: f64,
    pub wald_test: f64,
    pub wald_p: f64,
}

#[derive(Clone, Debug)]
pub struct OneWayRFit {
    pub i: String,
    pub j: String,
    pub estimate: f64,
    pub std_err: f64,
    pub confidence_interval: (f64, f64),
}

#[derive(Clone, Debug)]
pub struct RAOV {
    pub name: String,
    pub df: f64,
    pub rd: f64,
    pub mean_rd: f64,
    pub f: f64,
    pub p: f64,
}